Paper Digest: AAAI 2020 Highlights
The AAAI Conference on Artificial Intelligence (AAAI) is one of the top artificial intelligence conferences in the world. In 2020, it is to be held in New York. There were 7,700 paper submissions, of which arou nd 1,600 were accepted.
To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights / summaries to quickly get the main idea of each paper.
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Paper Digest Team
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TABLE 1: AAAI 2020 Papers
| Title | Authors | Highlight | |
|---|---|---|---|
| 1 | MultiSumm: Towards a Unified Model for Multi-Lingual Abstractive Summarization | Yue Cao, Xiaojun Wan, Jinge Yao, Dian Yu | In this paper, we present MultiSumm, a novel multi-lingual model for abstractive summarization. As an additional contribution, we construct the first summarization dataset for Bosnian and Croatian, containing 177,406 and 204,748 samples, respectively. |
| 2 | Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation | Chong Chen, Min Zhang, Yongfeng Zhang, Weizhi Ma, Yiqun Liu, Shaoping Ma | In this work, we propose a novel non-sampling transfer learning solution, named Efficient Heterogeneous Collaborative Filtering (EHCF) for Top-N recommendation. |
| 3 | Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach | Lei Chen, Le Wu, Richang Hong, Kun Zhang, Meng Wang | In this paper, we revisit GCN based CF models from two aspects. |
| 4 | Question-Driven Purchasing Propensity Analysis for Recommendation | Long Chen, Ziyu Guan, Qibin Xu, Qiong Zhang, Huan Sun, Guangyue Lu, Deng Cai | To address this recommendation problem, we propose a novel Question-Driven Attentive Neural Network (QDANN) to assess the instant demands of questioners and the eligibility of products based on user generated reviews, and do recommendation accordingly. |
| 5 | Gradient Method for Continuous Influence Maximization with Budget-Saving Considerations | Wei Chen, Weizhong Zhang, Haoyu Zhao | In this paper, we extend CIM to consider budget saving, that is, each strategy mix x has a cost c(x) where c is a convex cost function, and we want to maximize the balanced sum g(x) + λ(k − c(x)) where λ is a balance parameter, subject to the constraint of c(x) ≤ k. |
| 6 | Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search | Xinyan Dai, Xiao Yan, Kelvin K. W. Ng, Jiu Liu, James Cheng | In this paper, we present a new angle to analyze the quantization error, which decomposes the quantization error into norm error and direction error. |
| 7 | Modeling Fluency and Faithfulness for Diverse Neural Machine Translation | Yang Feng, Wanying Xie, Shuhao Gu, Chenze Shao, Wen Zhang, Zhengxin Yang, Dong Yu | To address the problem of teacher forcing, we propose a method to introduce an evaluation module to guide the distribution of the prediction. |
| 8 | Leveraging Title-Abstract Attentive Semantics for Paper Recommendation | Guibing Guo, Bowei Chen, Xiaoyan Zhang, Zhirong Liu, Zhenhua Dong, Xiuqiang He | In this paper, we regard the abstract as a sequence of sentences, and propose a two-level attentive neural network to capture: (1) the ability of each word within a sentence to reflect if it is semantically close to the words within the title. |
| 9 | Preserving Ordinal Consensus: Towards Feature Selection for Unlabeled Data | Jun Guo, Heng Chang, Wenwu Zhu | This paper proposes an unsupervised triplet-induced graph to explore a new type of potential structure at feature level, and incorporates it into simultaneous feature selection and clustering. |
| 10 | An Attentional Recurrent Neural Network for Personalized Next Location Recommendation | Qing Guo, Zhu Sun, Jie Zhang, Yin-Leng Theng | Most existing studies on next location recommendation propose to model the sequential regularity of check-in sequences, but suffer from the severe data sparsity issue where most locations have fewer than five following locations. |
| 11 | Re-Attention for Visual Question Answering | Wenya Guo, Ying Zhang, Xiaoping Wu, Jufeng Yang, Xiangrui Cai, Xiaojie Yuan | In this paper, to utilize the information in answer, we propose a re-attention framework for the VQA task. |
| 12 | Semi-Supervised Multi-Modal Learning with Balanced Spectral Decomposition | Peng Hu, Hongyuan Zhu, Xi Peng, Jie Lin | In this paper, we propose a Semi-supervised Multimodal Learning Network method (SMLN) which correlates different modalities by capturing the intrinsic structure and discriminative correlation of the multimedia data. |
| 13 | MuMod: A Micro-Unit Connection Approach for Hybrid-Order Community Detection | Ling Huang, Hong-Yang Chao, Quangqiang Xie | To this end, this paper defines a new problem of community detection, namely hybrid-order community detection, which aims to discover communities by simultaneously leveraging the lower-order connectivity pattern and the higherorder connectivity pattern. |
| 14 | Cross-Lingual Pre-Training Based Transfer for Zero-Shot Neural Machine Translation | Baijun Ji, Zhirui Zhang, Xiangyu Duan, Min Zhang, Boxing Chen, Weihua Luo | To this end, we introduce one monolingual pre-training method and two bilingual pre-training methods to obtain a universal encoder for different languages. |
| 15 | Functionality Discovery and Prediction of Physical Objects | Lei Ji, Botian Shi, Xianglin Guo, Xilin Chen | In this paper, we (1) mine object-functionality knowledge through pattern-based and model-based methods from text, (2) introduce a novel task on physical object-functionality prediction, which consumes an image and an action query to predict whether the object in the image can perform the action, and (3) propose a method to leverage the mined functionality knowledge for the new task. |
| 16 | True Nonlinear Dynamics from Incomplete Networks | Chunheng Jiang, Jianxi Gao, Malik Magdon-Ismail | Incomplete networks are the norm in practice, and we offer new ways to think about nonlinear dynamics when only sparse information is available. |
| 17 | Understanding and Improving Proximity Graph Based Maximum Inner Product Search | Jie Liu, Xiao Yan, Xinyan Dai, Zhirong Li, James Cheng, Ming-Chang Yang | In this paper, we show that there is a strong norm bias in the MIPS problem, which means that the large norm items are very likely to become the result of MIPS. |
| 18 | Type-Aware Anchor Link Prediction across Heterogeneous Networks Based on Graph Attention Network | Xiaoxue Li, Yanmin Shang, Yanan Cao, Yangxi Li, Jianlong Tan, Yanbing Liu | To address the challenge, we propose a novel type-aware anchor link prediction across heterogeneous networks (TALP), which models the effect of type information and fusion information on user nodes alignment from local and global perspective simultaneously. |
| 19 | Deep Match to Rank Model for Personalized Click-Through Rate Prediction | Zequn Lyu, Yu Dong, Chengfu Huo, Weijun Ren | Motivated by this, we propose a novel model named Deep Match to Rank (DMR) which combines the thought of collaborative filtering in matching methods for the ranking task in CTR prediction. |
| 20 | Modality to Modality Translation: An Adversarial Representation Learning and Graph Fusion Network for Multimodal Fusion | Sijie Mai, Haifeng Hu, Songlong Xing | In this paper, we propose a novel adversarial encoder-decoder-classifier framework to learn a modality-invariant embedding space. |
| 21 | A Variational Point Process Model for Social Event Sequences | Zhen Pan, Zhenya Huang, Defu Lian, Enhong Chen | To solve the above challenges, in this paper, we present a novel probabilistic generative model for event sequences. |
| 22 | Incremental Fairness in Two-Sided Market Platforms: On Smoothly Updating Recommendations | Gourab K. Patro, Abhijnan Chakraborty, Niloy Ganguly, Krishna Gummadi | In this work, we focus on the fairness issues arising out of such frequent updates, and argue for incremental updates of the platform algorithms so that the producers have enough time to adjust (both logistically and mentally) to the change. |
| 23 | Towards Comprehensive Recommender Systems: Time-Aware Unified Recommendations Based on Listwise Ranking of Implicit Cross-Network Data | Dilruk Perera, Roger Zimmermann | Therefore, we propose a novel deep learning based unified cross-network solution to mitigate cold-start and data sparsity issues and provide timely recommendations for new and existing users. |
| 24 | Minimizing the Bag-of-Ngrams Difference for Non-Autoregressive Neural Machine Translation | Chenze Shao, Jinchao Zhang, Yang Feng, Fandong Meng, Jie Zhou | In this paper, we propose to train NAT to minimize the Bag-of-Ngrams (BoN) difference between the model output and the reference sentence. |
| 25 | PEIA: Personality and Emotion Integrated Attentive Model for Music Recommendation on Social Media Platforms | Tiancheng Shen, Jia Jia, Yan Li, Yihui Ma, Yaohua Bu, Hanjie Wang, Bo Chen, Tat-Seng Chua, Wendy Hall | In this paper, aiming at music recommendation on social media platforms, we propose a Personality and Emotion Integrated Attentive model (PEIA), which fully utilizes social media data to comprehensively model users’ long-term taste (personality) and short-term preference (emotion). |
| 26 | Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation | Ke Sun, Tieyun Qian, Tong Chen, Yile Liang, Quoc Viet Hung Nguyen, Hongzhi Yin | To address the above limitations, we propose a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation. |
| 27 | Knowledge Graph Alignment Network with Gated Multi-Hop Neighborhood Aggregation | Zequn Sun, Chengming Wang, Wei Hu, Muhao Chen, Jian Dai, Wei Zhang, Yuzhong Qu | To tackle this problem, we propose a new KG alignment network, namely AliNet, aiming at mitigating the non-isomorphism of neighborhood structures in an end-to-end manner. |
| 28 | Learning with Unsure Responses | Kunihiro Takeoka, Yuyang Dong, Masafumi Oyamada | In this paper, we make the unsure responses contribute to training classifiers. |
| 29 | Author Name Disambiguation on Heterogeneous Information Network with Adversarial Representation Learning | Haiwen Wang, Ruijie Wan, Chuan Wen, Shuhao Li, Yuting Jia, Weinan Zhang, Xinbing Wang | We propose a novel generative adversarial framework to grow the two categories of models together: (i) the discriminative module distinguishes whether two papers are from the same author, and (ii) the generative module selects possibly homogeneous papers directly from the heterogeneous information network, which eliminates the complicated feature engineering. |
| 30 | Social Influence Does Matter: User Action Prediction for In-Feed Advertising | Hongyang Wang, Qingfei Meng, Ju Fan, Yuchen Li, Laizhong Cui, Xiaoman Zhao, Chong Peng, Gong Chen, Xiaoyong Du | This paper introduces an end-to-end approach that leverages social influence for action prediction, and focuses on addressing the high sparsity challenge for in-feed ads. |
| 31 | Mining Unfollow Behavior in Large-Scale Online Social Networks via Spatial-Temporal Interaction | Haozhe Wu, Zhiyuan Hu, Jia Jia, Yaohua Bu, Xiangnan He, Tat-Seng Chua | Researches on social network evolution mainly focus on the follow behavior, while the unfollow behavior has largely been ignored. To address these issues, we first construct a large-scale real-world Weibo1 dataset, which records detailed post content and relationship dynamics of 1.8 million Chinese users. |
| 32 | Who Likes What? — SplitLBI in Exploring Preferential Diversity of Ratings | Qianqian Xu, Jiechao Xiong, Zhiyong Yang, Xiaochun Cao, Qingming Huang, Yuan Yao | In this paper, we propose a multi-level model which learns both the common preference or utility function over the population based on features of alternatives to-be-compared, and preferential diversity functions conditioning on user categories. |
| 33 | Multi-Feature Discrete Collaborative Filtering for Fast Cold-Start Recommendation | Yang Xu, Lei Zhu, Zhiyong Cheng, Jingjing Li, Jiande Sun | In this paper, we propose a fast cold-start recommendation method, called Multi-Feature Discrete Collaborative Filtering (MFDCF), to solve these problems. |
| 34 | Cross-Modal Attention Network for Temporal Inconsistent Audio-Visual Event Localization | Hanyu Xuan, Zhenyu Zhang, Shuo Chen, Jian Yang, Yan Yan | Inspired by human system which puts different focuses at specific locations, time segments and media while performing multi-modality perception, we provide an attention-based method to simulate such process. |
| 35 | Learning to Match on Graph for Fashion Compatibility Modeling | Xun Yang, Xiaoyu Du, Meng Wang | This paper presents a graph-based fashion matching framework named Deep Relational Embedding Propagation (DREP), aiming to inject the extra-connectivities between items into the pairwise compatibility modeling. |
| 36 | D2D-LSTM: LSTM-Based Path Prediction of Content Diffusion Tree in Device-to-Device Social Networks | Heng Zhang, Xiaofei Wang, Jiawen Chen, Chenyang Wang, Jianxin Li | In this article, we propose D2D-LSTM based on Long Short-Term Memory (LSTM), which aims to predict complete content propagation paths in D2D social network. |
| 37 | An End-to-End Visual-Audio Attention Network for Emotion Recognition in User-Generated Videos | Sicheng Zhao, Yunsheng Ma, Yang Gu, Jufeng Yang, Tengfei Xing, Pengfei Xu, Runbo Hu, Hua Chai, Kurt Keutzer | In this paper, we propose to recognize video emotions in an end-to-end manner based on convolutional neural networks (CNNs). |
| 38 | Multi-Channel Reverse Dictionary Model | Lei Zheng, Fanchao Qi, Zhiyuan Liu, Yasheng Wang, Qun Liu, Maosong Sun | Inspired by the description-to-word inference process of humans, we propose the multi-channel reverse dictionary model, which can mitigate the two problems simultaneously. |
| 39 | Table2Analysis: Modeling and Recommendation of Common Analysis Patterns for Multi-Dimensional Data | Mengyu Zhou, Wang Tao, Ji Pengxin, Han Shi, Zhang Dongmei | In this paper, we propose Table2Analysis to learn commonly conducted analysis patterns from large amount of (table, analysis) pairs, and recommend analyses for any given table even not seen before. |
| 40 | A Recurrent Model for Collective Entity Linking with Adaptive Features | Xiaoling Zhou, Yukai Miao, Wei Wang, Jianbin Qin | In this paper, we revisit traditional machine learning techniques and propose a light-weight, tuneable and time-efficient method without using deep learning or deep learning generated features. |
| 41 | FairyTED: A Fair Rating Predictor for TED Talk Data | Rupam Acharyya, Shouman Das, Ankani Chattoraj, Md. Iftekhar Tanveer | Utilizing the theories of Causal Models, Counterfactual Fairness and state-of-the-art neural language models, we propose a mathematical framework for fair prediction of the public speaking quality. |
| 42 | Crisis-DIAS: Towards Multimodal Damage Analysis – Deployment, Challenges and Assessment | Mansi Agarwal, Maitree Leekha, Ramit Sawhney, Rajiv Ratn Shah | In this work, we present Crisis-DIAS, a multi-modal sequential damage identification, and severity detection system. |
| 43 | Unsupervised Detection of Sub-Events in Large Scale Disasters | Chidubem Arachie, Manas Gaur, Sam Anzaroot, William Groves, Ke Zhang, Alejandro Jaimes | In this paper we address the problem of automatically identifying important sub-events (within a large-scale emergency “event”, such as a hurricane). |
| 44 | Spatio-Temporal Attention-Based Neural Network for Credit Card Fraud Detection | Dawei Cheng, Sheng Xiang, Chencheng Shang, Yiyi Zhang, Fangzhou Yang, Liqing Zhang | Therefore, in this work, we propose a spatial-temporal attention-based neural network (STAN) for fraud detection. |
| 45 | Tracking Disaster Footprints with Social Streaming Data | Lu Cheng, Jundong Li, K. Selcuk Candan, Huan Liu | The problem is important as it presents a promising new way to efficiently search for accurate information during emergency response. |
| 46 | Detecting and Tracking Communal Bird Roosts in Weather Radar Data | Zezhou Cheng, Saadia Gabriel, Pankaj Bhambhani, Daniel Sheldon, Subhransu Maji, Andrew Laughlin, David Winkler | This paper describes a machine learning system to detect and track roost signatures in weather radar data. |
| 47 | Hindi-English Hate Speech Detection: Author Profiling, Debiasing, and Practical Perspectives | Shivang Chopra, Ramit Sawhney, Puneet Mathur, Rajiv Ratn Shah | In an attempt to bridge this gap, we introduce a three-tier pipeline that employs profanity modeling, deep graph embeddings, and author profiling to retrieve instances of hate speech in Hindi-English code-switched language (Hinglish) on social media platforms like Twitter. |
| 48 | Inferring Nighttime Satellite Imagery from Human Mobility | Brian Dickinson, Gourab Ghoshal, Xerxes Dotiwalla, Adam Sadilek, Henry Kautz | In this study we demonstrate that it is possible to accelerate this process by inferring artificial nighttime satellite imagery from human mobility data, while maintaining a strong differential privacy guarantee. |
| 49 | A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning | Kevin Fauvel, Daniel Balouek-Thomert, Diego Melgar, Pedro Silva, Anthony Simonet, Gabriel Antoniu, Alexandru Costan, Véronique Masson, Manish Parashar, Ivan Rodero, Alexandre Termier | In this paper, we introduce the Distributed Multi-Sensor Earthquake Early Warning (DMSEEW) system, a novel machine learning-based approach that combines data from both types of sensors (GPS stations and seismometers) to detect medium and large earthquakes. |
| 50 | Faking Fairness via Stealthily Biased Sampling | Kazuto Fukuchi, Satoshi Hara, Takanori Maehara | In this study, we answer this question negatively. To assess the (un)detectability of the fraud, we explicitly construct an algorithm, the stealthily biased sampling, that can deliberately construct an evil benchmark dataset via subsampling. |
| 51 | Discriminating Cognitive Disequilibrium and Flow in Problem Solving: A Semi-Supervised Approach Using Involuntary Dynamic Behavioral Signals | Mononito Goswami, Lujie Chen, Artur Dubrawski | In this paper, we explore a semi-supervised learning framework that can learn low-dimensional representations of involuntary dynamic behavioral signals (mainly gross-body movements) from a modest number of short time series segments. |
| 52 | Lightweight and Robust Representation of Economic Scales from Satellite Imagery | Sungwon Han, Donghyun Ahn, Hyunji Cha, Jeasurk Yang, Sungwon Park, Meeyoung Cha | We present READ, a new approach for obtaining essential spatial representation for any given district from high-resolution satellite imagery based on deep neural networks. |
| 53 | The Unreasonable Effectiveness of Inverse Reinforcement Learning in Advancing Cancer Research | John Kalantari, Heidi Nelson, Nicholas Chia | Here, we introduce a Bayesian nonparametric IRL model (PUR-IRL) where the number of reward functions is a priori unbounded in order to account for uncertainty in cancer data, i.e., the existence of latent trajectories and non-uniform sampling. |
| 54 | Linguistic Fingerprints of Internet Censorship: The Case of Sina Weibo | Kei Yin Ng, Anna Feldman, Jing Peng | We build a classifier that significantly outperforms non-expert humans in predicting whether a blogpost will be censored. |
| 55 | Voice for the Voiceless: Active Sampling to Detect Comments Supporting the Rohingyas | Shriphani Palakodety, Ashiqur R. KhudaBukhsh, Jaime G. Carbonell, Shriphani Palakodety, Ashiqur R. KhudaBukhsh, Jaime G. Carbonell | In this work, we construct a substantial corpus of YouTube video comments (263,482 comments from 113,250 users in 5,153 relevant videos) with an aim to analyze the possible role of AI in helping a marginalized community. |
| 56 | Guided Weak Supervision for Action Recognition with Scarce Data to Assess Skills of Children with Autism | Prashant Pandey, Prathosh AP, Manu Kohli, Josh Pritchard | In this paper, we propose to automate the response measurement through video recording of the scene following the use of Deep Neural models for human action recognition from videos. |
| 57 | The Stanford Acuity Test: A Precise Vision Test Using Bayesian Techniques and a Discovery in Human Visual Response | Chris Piech, Ali Malik, Laura M. Scott, Robert T. Chang, Charles Lin | In this paper we make two core contributions. |
| 58 | Automatically Neutralizing Subjective Bias in Text | Reid Pryzant, Richard Diehl Martinez, Nathan Dass, Sadao Kurohashi, Dan Jurafsky, Diyi Yang | To address this issue, we introduce a novel testbed for natural language generation: automatically bringing inappropriately subjective text into a neutral point of view (“neutralizing” biased text). |
| 59 | Capturing the Style of Fake News | Piotr Przybyla | In this study we aim to explore automatic methods that can detect online documents of low credibility, especially fake news, based on the style they are written in. |
| 60 | On Identifying Hashtags in Disaster Twitter Data | Jishnu Ray Chowdhury, Cornelia Caragea, Doina Caragea | The best performing model achieves an F1-score as high as $92.22%$. To facilitate progress on automatic identification (or extraction) of disaster hashtags for Twitter data, we construct a unique dataset of disaster-related tweets annotated with hashtags useful for filtering actionable information. |
| 61 | Neural Approximate Dynamic Programming for On-Demand Ride-Pooling | Sanket Shah, Meghna Lowalekar, Pradeep Varakantham | Therefore, our key technical contribution is in providing a general ADP method that can learn from the ILP based assignment found in ride-pooling. |
| 62 | Weak Supervision for Fake News Detection via Reinforcement Learning | Yaqing Wang, Weifeng Yang, Fenglong Ma, Jin Xu, Bin Zhong, Qiang Deng, Jing Gao | In order to tackle this challenge, we propose a reinforced weakly-supervised fake news detection framework, i.e., WeFEND, which can leverage users’ reports as weak supervision to enlarge the amount of training data for fake news detection. |
| 63 | Protecting Geolocation Privacy of Photo Collections | Jinghan Yang, Ayan Chakrabarti, Yevgeniy Vorobeychik | We consider the specific issue of location privacy as potentially revealed by posting photo collections, which facilitate accurate geolocation with the help of deep learning methods even in the absence of geotags. |
| 64 | Weakly-Supervised Fine-Grained Event Recognition on Social Media Texts for Disaster Management | Wenlin Yao, Cheng Zhang, Shiva Saravanan, Ruihong Huang, Ali Mostafavi | To meet these time-critical needs, we present a weakly supervised approach for rapidly building high-quality classifiers that label each individual Twitter message with fine-grained event categories. |
| 65 | Interactive Learning with Proactive Cognition Enhancement for Crowd Workers | Jing Zhang, Huihui Wang, Shunmei Meng, Victor S. Sheng | To help workers improve their reliability while performing tasks, this paper proposes a novel Interactive Learning framework with Proactive Cognitive Enhancement (ILPCE) for crowd workers. |
| 66 | Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks | Tian Bian, Xi Xiao, Tingyang Xu, Peilin Zhao, Wenbing Huang, Yu Rong, Junzhou Huang | In this paper, we propose a novel bi-directional graph model, named Bi-Directional Graph Convolutional Networks (Bi-GCN), to explore both characteristics by operating on both top-down and bottom-up propagation of rumors. |
| 67 | Doctor2Vec: Dynamic Doctor Representation Learning for Clinical Trial Recruitment | Siddharth Biswal, Cao Xiao, Lucas M. Glass, Elizabeth Milkovits, Jimeng Sun | In this work, we study the problem on clinical trial recruitment, which is about identifying the right doctors to help conduct the trials based on the trial description and patient EHR data of those doctors. |
| 68 | TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources | Sahan Bulathwela, Maria Perez-Ortiz, Emine Yilmaz, John Shawe-Taylor | This work presents the foundations towards building a dynamic, scalable and transparent recommendation system for education, modelling learner’s knowledge from implicit data in the form of engagement with open educational resources. |
| 69 | Real-Time Route Search by Locations | Lisi Chen, Shuo Shang, Tao Guo | We define and study a novel Continuous Route-Search-by-Location (C-RSL) problem to enable real-time route search by locations for a large number of users over route data streams. |
| 70 | Pay Your Trip for Traffic Congestion: Dynamic Pricing in Traffic-Aware Road Networks | Lisi Chen, Shuo Shang, Bin Yao, Jing Li | We propose and investigate a novel Dynamic Pricing Strategy (DPS) to price travelers’ trips in intelligent transportation platforms (e.g., DiDi, Lyft, Uber). |
| 71 | Adaptive Greedy versus Non-Adaptive Greedy for Influence Maximization | Wei Chen, Binghui Peng, Grant Schoenebeck, Biaoshuai Tao | Motivated by the extreme success of greedy-based algorithms/heuristics for influence maximization, we propose the concept of greedy adaptivity gap, which compares the performance of the adaptive greedy algorithm to its non-adaptive counterpart. |
| 72 | DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature | Chaoran Cheng, Fei Tan, Zhi Wei | In this work, we present the first successful end-to-end deep learning approach to bridge the gap between generic NER algorithms and low-resource applications through genomic variants recognition. |
| 73 | Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer | Edward Choi, Zhen Xu, Yujia Li, Michael Dusenberry, Gerardo Flores, Emily Xue, Andrew Dai | In this paper, we study the possibility of jointly learning the hidden structure of EHR while performing supervised prediction tasks on EHR data. |
| 74 | CONAN: Complementary Pattern Augmentation for Rare Disease Detection | Limeng Cui, Siddharth Biswal, Lucas M. Glass, Greg Lever, Jimeng Sun, Cao Xiao | In this paper, we propose a Complementary pattern Augmentation (CONAN) framework for rare disease detection. |
| 75 | Differentially Private and Fair Classification via Calibrated Functional Mechanism | Jiahao Ding, Xinyue Zhang, Xiaohuan Li, Junyi Wang, Rong Yu, Miao Pan | In this paper, we focus on the design of classification model with fairness and differential privacy guarantees by jointly combining functional mechanism and decision boundary fairness. |
| 76 | Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods | Ferdinando Fioretto, Terrence W.K. Mak, Pascal Van Hentenryck | To address these challenges, this paper presents a deep learning approach to the OPF. |
| 77 | CORE: Automatic Molecule Optimization Using Copy & Refine Strategy | Tianfan Fu, Cao Xiao, Jimeng Sun | To address this challenge, we propose a new generating strategy called “Copy&Refine” (CORE), where at each step the generator first decides whether to copy an existing substructure from input X or to generate a new substructure, then the most promising substructure will be added to the new molecule. |
| 78 | GAN-Based Unpaired Chinese Character Image Translation via Skeleton Transformation and Stroke Rendering | Yiming Gao, Jiangqin Wu | Therefore, in this work, we propose a three-stage Generative Adversarial Network (GAN) architecture for multi-chirography image translation, which is divided into skeleton extraction, skeleton transformation and stroke rendering with unpaired training data. |
| 79 | Predictive Student Modeling in Educational Games with Multi-Task Learning | Michael Geden, Andrew Emerson, Jonathan Rowe, Roger Azevedo, James Lester | We demonstrate the effectiveness of this approach by utilizing student data from a series of laboratory-based and classroom-based studies conducted with a game-based learning environment for microbiology education, Crystal Island. |
| 80 | Enhancing Personalized Trip Recommendation with Attractive Routes | Jiqing Gu, Chao Song, Wenjun Jiang, Xiaomin Wang, Ming Liu | In this paper, we study the attractive routes to improve personalized trip recommendation. |
| 81 | Graduate Employment Prediction with Bias | Teng Guo, Feng Xia, Shihao Zhen, Xiaomei Bai, Dongyu Zhang, Zitao Liu, Jiliang Tang | In this paper, we develop a framework, i.e., MAYA (Multi-mAjor emploYment stAtus) to predict students’ employment status while considering biases. |
| 82 | Multi-Scale Anomaly Detection on Attributed Networks | Leonardo Gutiérrez-Gómez, Alexandre Bovet, Jean-Charles Delvenne | Here, we propose a principled way to uncover outlier nodes simultaneously with the context with respect to which they are anomalous, at all relevant scales of the network. |
| 83 | Accurate Structured-Text Spotting for Arithmetical Exercise Correction | Yiqing Hu, Yan Zheng, Hao Liu, Dequang Jiang, Yinsong Liu, Bo Ren | To tackle these problems, we introduce integrated detection, recognition and evaluation branches by leveraging AE’s intrinsic features, namely 1) boundary indistinctive, 2) locally relevant patterns and 3) globally irrelevant symbols. |
| 84 | Pairwise Learning with Differential Privacy Guarantees | Mengdi Huai, Di Wang, Chenglin Miao, Jinhui Xu, Aidong Zhang | To address this challenging issue, in this paper, we propose several differentially private pairwise learning algorithms for both online and offline settings. |
| 85 | CASTER: Predicting Drug Interactions with Chemical Substructure Representation | Kexin Huang, Cao Xiao, Trong Hoang, Lucas Glass, Jimeng Sun | In this work, we develop a ChemicAl SubstrucTurE Representation (CASTER) framework that predicts DDIs given chemical structures of drugs. |
| 86 | RL-Duet: Online Music Accompaniment Generation Using Deep Reinforcement Learning | Nan Jiang, Sheng Jin, Zhiyao Duan, Changshui Zhang | This paper presents a deep reinforcement learning algorithm for online accompaniment generation, with potential for real-time interactive human-machine duet improvisation. |
| 87 | A Graph Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction | Ziqi Ke, Haris Vikalo | In this paper, we present a learning framework based on a graph auto-encoder designed to exploit structural properties of sequencing data. |
| 88 | Generating Realistic Stock Market Order Streams | Junyi Li, Xintong Wang, Yaoyang Lin, Arunesh Sinha, Michael Wellman | We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks (GANs). |
| 89 | SynSig2Vec: Learning Representations from Synthetic Dynamic Signatures for Real-World Verification | Songxuan Lai, Lianwen Jin, Luojun Lin, Yecheng Zhu, Huiyun Mao | To tackle this issue, this paper proposes to learn dynamic signature representations through ranking synthesized signatures. |
| 90 | DeepAlerts: Deep Learning Based Multi-Horizon Alerts for Clinical Deterioration on Oncology Hospital Wards | Dingwen Li, Patrick G. Lyons, Chenyang Lu, Marin Kollef | In this paper, we investigate approaches to properly train deep multi-task models for predicting clinical deterioration events via generating multi-horizon alerts for hospitalized patients outside the ICU, with particular application to oncology patients. |
| 91 | Region Focus Network for Joint Optic Disc and Cup Segmentation | Ge Li, Changsheng Li, Chan Zeng, Peng Gao, Guotong Xie | To remedy this issue, we propose a Region Focus Network (RF-Net) that innovatively integrates detection and multi-class segmentation into a unified architecture for end-to-end joint optic disc and cup segmentation with global optimization. |
| 92 | Pose-Assisted Multi-Camera Collaboration for Active Object Tracking | Jing Li, Jing Xu, Fangwei Zhong, Xiangyu Kong, Yu Qiao, Yizhou Wang | In this paper, we extend the single-camera AOT to a multi-camera setting, where cameras tracking a target in a collaborative fashion. |
| 93 | Robust Low-Rank Discovery of Data-Driven Partial Differential Equations | Jun Li, Gan Sun, Guoshuai Zhao, Li-wei H. Lehman | We propose a low-rank sequential (grouped) threshold ridge regression algorithm to solve the minimization problem. |
| 94 | Towards Cross-Modality Medical Image Segmentation with Online Mutual Knowledge Distillation | Kang Li, Lequan Yu, Shujun Wang, Pheng-Ann Heng | Considering multi-modality data with the same anatomic structures are widely available in clinic routine, in this paper, we aim to exploit the prior knowledge (e.g., shape priors) learned from one modality (aka., assistant modality) to improve the segmentation performance on another modality (aka., target modality) to make up annotation scarcity. |
| 95 | Privacy-Preserving Gradient Boosting Decision Trees | Qinbin Li, Zhaomin Wu, Zeyi Wen, Bingsheng He | In this paper, we study how to improve model accuracy of GBDT while preserving the strong guarantee of differential privacy. |
| 96 | MRI Reconstruction with Interpretable Pixel-Wise Operations Using Reinforcement Learning | Wentian Li, Xidong Feng, Haotian An, Xiang Yao Ng, Yu-Jin Zhang | In this work, we propose a deep reinforcement learning based method to reconstruct the corrupted images with meaningful pixel-wise operations (e.g. edge enhancing filters), so that the reconstruction process is transparent to users. |
| 97 | PSENet: Psoriasis Severity Evaluation Network | Yi Li, Zhe Wu, Shuang Zhao, Xian Wu, Yehong Kuang, Yangtian Yan, Shen Ge, Kai Wang, Wei Fan, Xiang Chen, Yong Wang | To overcome these drawbacks, we propose PSENet which applies deep neural networks to estimate Psoriasis severity based on skin lesion images. |
| 98 | Learning Geo-Contextual Embeddings for Commuting Flow Prediction | Zhicheng Liu, Fabio Miranda, Weiting Xiong, Junyan Yang, Qiao Wang, Claudio Silva | To address these issues, we propose Geo-contextual Multitask Embedding Learner (GMEL), a model that captures the spatial correlations from geographic contextual information for commuting flow prediction. |
| 99 | Learning Multi-Modal Biomarker Representations via Globally Aligned Longitudinal Enrichments | Lyujian Lu, Saad Elbeleidy, Lauren Zoe Baker, Hua Wang | To cope with these challenges, in this paper we propose a longitudinal multi-modal method to learn enriched genotypic and phenotypic biomarker representations in the format of fixed-length vectors that can simultaneously capture the baseline neuroimaging measurements of the entire dataset and progressive variations of the varied counts of follow-up measurements over time of every participant from different biomarker sources. |
| 100 | AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration | Liantao Ma, Junyi Gao, Yasha Wang, Chaohe Zhang, Jiangtao Wang, Wenjie Ruan, Wen Tang, Xin Gao, Xinyu Ma | In this work, we develop a general health status representation learning model, named AdaCare. |
| 101 | ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context | Liantao Ma, Chaohe Zhang, Yasha Wang, Wenjie Ruan, Jiangtao Wang, Wen Tang, Xinyu Ma, Xin Gao, Junyi Gao | In this paper, we propose ConCare to handle the irregular EMR data and extract feature interrelationship to perform individualized healthcare prediction. |
| 102 | Bursting the Filter Bubble: Fairness-Aware Network Link Prediction | Farzan Masrour, Tyler Wilson, Heng Yan, Pang-Ning Tan, Abdol Esfahanian | In this study, we examine the filter bubble problem from the perspective of algorithm fairness and introduce a dyadic-level fairness criterion based on network modularity measure. |
| 103 | Gait Recognition for Co-Existing Multiple People Using Millimeter Wave Sensing | Zhen Meng, Song Fu, Jie Yan, Hongyuan Liang, Anfu Zhou, Shilin Zhu, Huadong Ma, Jianhua Liu, Ning Yang | In this paper, we build a first-of-its-kind mmWave gait data set, in which we collect gait of 95 volunteers ‘seen’ from two mmWave radars in two different scenarios, which together lasts about 30 hours. |
| 104 | Generalizable Resource Allocation in Stream Processing via Deep Reinforcement Learning | Xiang Ni, Jing Li, Mo Yu, Wang Zhou, Kun-Lung Wu | In this paper, we present a graph-aware encoder-decoder framework to learn a generalizable resource allocation strategy that can properly distribute computation tasks of stream processing graphs unobserved from training data. |
| 105 | ActiveThief: Model Extraction Using Active Learning and Unannotated Public Data | Soham Pal, Yash Gupta, Aditya Shukla, Aditya Kanade, Shirish Shevade, Vinod Ganapathy | We demonstrate that (1) it is possible to use ActiveThief to extract deep classifiers trained on a variety of datasets from image and text domains, while querying the model with as few as 10-30% of samples from public datasets, (2) the resulting model exhibits a higher transferability success rate of adversarial examples than prior work, and (3) the attack evades detection by the state-of-the-art model extraction detection method, PRADA. |
| 106 | Chemically Interpretable Graph Interaction Network for Prediction of Pharmacokinetic Properties of Drug-Like Molecules | Yashaswi Pathak, Siddhartha Laghuvarapu, Sarvesh Mehta, U. Deva Priyakumar | Here, we report a novel method based on graph neural network to predict solvation free energies. |
| 107 | FuzzE: Fuzzy Fairness Evaluation of Offensive Language Classifiers on African-American English | Anthony Rios | In this paper, we answer the question, “How can we evaluate the performance of classifiers across minority dialectal languages when they are not present within a particular dataset?” |
| 108 | Learning to Generate Maps from Trajectories | Sijie Ruan, Cheng Long, Jie Bao, Chunyang Li, Zisheng Yu, Ruiyuan Li, Yuxuan Liang, Tianfu He, Yu Zheng | To this end, we propose a deep learning-based map generation framework, i.e., DeepMG, which learns the structure of the existing road network to overcome the noisy GPS positions. |
| 109 | Spatial Classification with Limited Observations Based on Physics-Aware Structural Constraint | Arpan Man Sainju, Wenchong He, Zhe Jiang, Da Yan | To address this issue, we propose a new approach that incorporates physics-aware structural constraints into the model representation. |
| 110 | Effective Decoding in Graph Auto-Encoder Using Triadic Closure | Han Shi, Haozheng Fan, James T. Kwok | In this paper, we utilize the well-known triadic closure property which is exhibited in many real-world networks. |
| 111 | Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting | Chao Song, Youfang Lin, Shengnan Guo, Huaiyu Wan | In this paper, we propose a novel model, named Spatial-Temporal Synchronous Graph Convolutional Networks (STSGCN), for spatial-temporal network data forecasting. |
| 112 | Continuous Multiagent Control Using Collective Behavior Entropy for Large-Scale Home Energy Management | Jianwen Sun, Yan Zheng, Jianye Hao, Zhaopeng Meng, Yang Liu | In this paper, we propose a collective MA-DRL algorithm with continuous action space to provide fine-grained control on a large scale microgrid. |
| 113 | DATA-GRU: Dual-Attention Time-Aware Gated Recurrent Unit for Irregular Multivariate Time Series | Qingxiong Tan, Mang Ye, Baoyao Yang, Siqi Liu, Andy Jinhua Ma, Terry Cheuk-Fung Yip, Grace Lai-Hung Wong, PongChi Yuen | Therefore, we propose a novel end-to-end Dual-Attention Time-Aware Gated Recurrent Unit (DATA-GRU) for IMTS to predict the mortality risk of patients. |
| 114 | Finding Minimum-Weight Link-Disjoint Paths with a Few Common Nodes | Binglin Tao, Mingyu Xiao, Jingyang Zhao | For the restricted version, we build an ILP model and design a fast algorithm by using the techniques of augmenting paths and splitting nodes. |
| 115 | Finding Needles in a Moving Haystack: Prioritizing Alerts with Adversarial Reinforcement Learning | Liang Tong, Aron Laszka, Chao Yan, Ning Zhang, Yevgeniy Vorobeychik | We introduce a novel approach for computing a policy for prioritizing alerts using adversarial reinforcement learning. |
| 116 | Robust Adversarial Objects against Deep Learning Models | Tzungyu Tsai, Kaichen Yang, Tsung-Yi Ho, Yier Jin | In this paper, we propose a novel adversarial attack against PointNet++, a deep neural network that performs classification and segmentation tasks using features learned directly from raw 3D points. |
| 117 | OMuLeT: Online Multi-Lead Time Location Prediction for Hurricane Trajectory Forecasting | Ding Wang, Boyang Liu, Pang-Ning Tan, Lifeng Luo | In this paper, we cast the hurricane trajectory forecasting task as an online multi-lead time location prediction problem and present a framework called OMuLeT to improve path prediction by combining the 6-hourly and 12-hourly forecasts generated from an ensemble of dynamical (physical) hurricane models. |
| 118 | Incorporating Expert-Based Investment Opinion Signals in Stock Prediction: A Deep Learning Framework | Heyuan Wang, Tengjiao Wang, Yi Li | In this study, we provide an in-depth analysis of public stock reviews and their application in stock movement prediction. |
| 119 | Graph-Driven Generative Models for Heterogeneous Multi-Task Learning | Wenlin Wang, Hongteng Xu, Zhe Gan, Bai Li, Guoyin Wang, Liqun Chen, Qian Yang, Wenqi Wang, Lawrence Carin | We propose a novel graph-driven generative model, that unifies multiple heterogeneous learning tasks into the same framework. |
| 120 | Topic Enhanced Sentiment Spreading Model in Social Networks Considering User Interest | Xiaobao Wang, Di Jin, Katarzyna Musial, Jianwu Dang | In this paper, we study an interesting problem of sentiment spreading in social networks. |
| 121 | HDK: Toward High-Performance Deep-Learning-Based Kirchhoff Analysis | Xinying Wang, Olamide Timothy Tawose, Feng Yan, Dongfang Zhao | This paper proposes a high-performance deep-learning-based approach for Kirchhoff analysis, namely HDK. |
| 122 | Actor Critic Deep Reinforcement Learning for Neural Malware Control | Yu Wang, Jack Stokes, Mady Marinescu | In this paper, we propose a new DRL-based system which instead employs a modified actor critic (AC) framework for the emulation halting task. |
| 123 | Urban2Vec: Incorporating Street View Imagery and POIs for Multi-Modal Urban Neighborhood Embedding | Zhecheng Wang, Haoyuan Li, Ram Rajagopal | In this work, we propose Urban2Vec, an unsupervised multi-modal framework which incorporates both street view imagery and point-of-interest (POI) data to learn neighborhood embeddings. |
| 124 | Hiding in Multilayer Networks | Marcin Waniek, Tomasz Michalak, Talal Rahwan | This paper presents the first analysis of the robustness of centrality measures against strategic manipulation in multilayer networks. |
| 125 | A Deep Neural Network Model of Particle Thermal Radiation in Packed Bed | Hao Wu, Shuang Hao | In this work, we propose an analytical model to calculate macroscopic effective conductivity from particle packing structures Then, we develop a deep neural network (DNN) model used as a predictor of the complex view factor function. |
| 126 | DeepDualMapper: A Gated Fusion Network for Automatic Map Extraction Using Aerial Images and Trajectories | Hao Wu, Hanyuan Zhang, Xinyu Zhang, Weiwei Sun, Baihua Zheng, Yuning Jiang | We propose a deep convolutional neural network called DeepDualMapper which fuses the aerial image and trajectory data in a more seamless manner to extract the digital map. |
| 127 | Accelerating and Improving AlphaZero Using Population Based Training | Ti-Rong Wu, Ting-Han Wei, I-Chen Wu | This paper proposes using population based training (PBT) to help tune hyperparameters dynamically and improve strength during training time. |
| 128 | Graph Convolutional Networks with Markov Random Field Reasoning for Social Spammer Detection | Yongji Wu, Defu Lian, Yiheng Xu, Le Wu, Enhong Chen | In this paper, we propose a novel social spammer detection model based on Graph Convolutional Networks (GCNs) that operate on directed social graphs by explicitly considering three types of neighbors. |
| 129 | Generative Adversarial Regularized Mutual Information Policy Gradient Framework for Automatic Diagnosis | Yuan Xia, Jingbo Zhou, Zhenhui Shi, Chao Lu, Haifeng Huang | In this paper, we propose a Generative Adversarial regularized Mutual information Policy gradient framework (GAMP) for automatic diagnosis which aims to make a diagnosis rapidly and accurately. |
| 130 | Generate (Non-Software) Bugs to Fool Classifiers | Hiromu Yakura, Youhei Akimoto, Jun Sakuma | In this paper, we present a systematic approach to generate natural adversarial examples against classification models by employing such natural-appearing perturbations that imitate a certain object or signal. |
| 131 | Fairness-Aware Demand Prediction for New Mobility | An Yan, Bill Howe | To address these biases and improve fairness, we present FairST, a fairness-aware demand prediction model for spatiotemporal urban applications, with emphasis on new mobility. |
| 132 | Beyond Digital Domain: Fooling Deep Learning Based Recognition System in Physical World | Kaichen Yang, Tzungyu Tsai, Honggang Yu, Tsung-Yi Ho, Yier Jin | In this paper, we propose a novel physical adversarial attack targeting object detection models. |
| 133 | Scalable and Generalizable Social Bot Detection through Data Selection | Kai-Cheng Yang, Onur Varol, Pik-Mai Hui, Filippo Menczer | In this paper we propose a framework that uses minimal account metadata, enabling efficient analysis that scales up to handle the full stream of public tweets of Twitter in real time. |
| 134 | Instance-Wise Dynamic Sensor Selection for Human Activity Recognition | Xiaodong Yang, Yiqiang Chen, Hanchao Yu, Yingwei Zhang, Wang Lu, Ruizhe Sun | In this paper, we propose an Instance-wise Dynamic Sensor Selection (IDSS) method for HAR. |
| 135 | Reinforcement-Learning Based Portfolio Management with Augmented Asset Movement Prediction States | Yunan Ye, Hengzhi Pei, Boxin Wang, Pin-Yu Chen, Yada Zhu, Ju Xiao, Bo Li | In this paper, we propose SARL, a novel State-Augmented RL framework for PM. |
| 136 | Attention Based Data Hiding with Generative Adversarial Networks | Chong Yu | In this paper, we propose the novel end-to-end framework to extend its application to data hiding area. |
| 137 | AirNet: A Calibration Model for Low-Cost Air Monitoring Sensors Using Dual Sequence Encoder Networks | Haomin Yu, Qingyong Li, Yangli-ao Geng, Yingjun Zhang, Zhi Wei | In this work, we propose a data-driven model based on deep neural networks, referred to as AirNet, for calibrating low-cost air monitoring sensors. |
| 138 | Towards Hands-Free Visual Dialog Interactive Recommendation | Tong Yu, Yilin Shen, Hongxia Jin | We propose a hands-free visual dialog recommender system to interactively recommend a list of items. |
| 139 | Order Matters: Semantic-Aware Neural Networks for Binary Code Similarity Detection | Zeping Yu, Rui Cao, Qiyi Tang, Sen Nie, Junzhou Huang, Shi Wu | In this paper we propose semantic-aware neural networks to extract the semantic information of the binary code. |
| 140 | MetaLight: Value-Based Meta-Reinforcement Learning for Traffic Signal Control | Xinshi Zang, Huaxiu Yao, Guanjie Zheng, Nan Xu, Kai Xu, Zhenhui Li | In this paper, we propose a novel framework, named as MetaLight, to speed up the learning process in new scenarios by leveraging the knowledge learned from existing scenarios. |
| 141 | Geometry-Constrained Car Recognition Using a 3D Perspective Network | Zeng Rui, Ge Zongyuan, Denman Simon, Sridharan Sridha, Fookes Clinton | We present a novel learning framework for vehicle recognition from a single RGB image. |
| 142 | Generating Adversarial Examples for Holding Robustness of Source Code Processing Models | Huangzhao Zhang, Zhuo Li, Ge Li, Lei Ma, Yang Liu, Zhi Jin | In this paper, we propose a Metropolis-Hastings sampling-based identifier renaming technique, named \fullmethod (\method), which generates adversarial examples for DL models specialized for source code processing. |
| 143 | Spatio-Temporal Graph Structure Learning for Traffic Forecasting | Qi Zhang, Jianlong Chang, Gaofeng Meng, Shiming Xiang, Chunhong Pan | To address these issues, we propose a novel framework named Structure Learning Convolution (SLC) that enables to extend the traditional convolutional neural network (CNN) to graph domains and learn the graph structure for traffic forecasting. |
| 144 | Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction | Weijia Zhang, Hao Liu, Yanchi Liu, Jingbo Zhou, Hui Xiong | To this end, we propose Semi-supervised Hierarchical Recurrent Graph Neural Network (SHARE) for predicting city-wide parking availability. |
| 145 | Shoreline: Data-Driven Threshold Estimation of Online Reserves of Cryptocurrency Trading Platforms | Xitong Zhang, He Zhu, Jiayu Zhou | In this paper, we propose Shoreline, a deep learning-based threshold estimation framework that estimates the optimal threshold of hot wallets from historical wallet activities and dynamic trading networks. |
| 146 | A Novel Learning Framework for Sampling-Based Motion Planning in Autonomous Driving | Yifan Zhang, Jinghuai Zhang, Jindi Zhang, Jianping Wang, Kejie Lu, Jeff Hong | To address this issue, we propose a new learning framework for SBMP. |
| 147 | Dynamic Malware Analysis with Feature Engineering and Feature Learning | Zhaoqi Zhang, Panpan Qi, Wei Wang | In this paper, we propose a novel and low-cost feature extraction approach, and an effective deep neural network architecture for accurate and fast malware detection. |
| 148 | OF-MSRN: Optical Flow-Auxiliary Multi-Task Regression Network for Direct Quantitative Measurement, Segmentation and Motion Estimation | Chengqian Zhao, Cheng Feng, Dengwang Li, Shuo Li | In this paper, we propose a novel optical flow-auxiliary multi-task regression network named OF-MSRN to overcome these challenges. |
| 149 | MaskGEC: Improving Neural Grammatical Error Correction via Dynamic Masking | Zewei Zhao, Houfeng Wang | In this paper, we propose a simple yet effective method to improve the NMT-based GEC models by dynamic masking. |
| 150 | GMAN: A Graph Multi-Attention Network for Traffic Prediction | Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, Jianzhong Qi | In this paper, we focus on the spatio-temporal factors, and propose a graph multi-attention network (GMAN) to predict traffic conditions for time steps ahead at different locations on a road network graph. |
| 151 | Index Tracking with Cardinality Constraints: A Stochastic Neural Networks Approach | Yu Zheng, Bowei Chen, Timothy M. Hospedales, Yongxin Yang | This paper introduces a simple, efficient and scalable connectionist model as an alternative. |
| 152 | Iteratively Questioning and Answering for Interpretable Legal Judgment Prediction | Haoxi Zhong, Yuzhong Wang, Cunchao Tu, Tianyang Zhang, Zhiyuan Liu, Maosong Sun | In this paper, we present QAjudge, a model based on reinforcement learning to visualize the prediction process and give interpretable judgments. |
| 153 | RiskOracle: A Minute-Level Citywide Traffic Accident Forecasting Framework | Zhengyang Zhou, Yang Wang, Xike Xie, Lianliang Chen, Hengchang Liu | In this work, we propose a novel framework RiskOracle, to improve the prediction granularity to minute levels. |
| 154 | Deep Reservoir Computing Meets 5G MIMO-OFDM Systems in Symbol Detection | Zhou Zhou, Lingjia Liu, Vikram Chandrasekhar, Jianzhong Zhang, Yang Yi | In this paper, we consider two ways to extend the shallow architecture to deep RC to improve the performance without sacrificing the underlying benefit: (1) Extend the output layer to a three layer structure which promotes a joint time-frequency processing to neuron states; (2) Sequentially stack RCs to form a deep neural network. |
| 155 | MixedAD: A Scalable Algorithm for Detecting Mixed Anomalies in Attributed Graphs | Mengxiao Zhu, Haogang Zhu | In this paper, we propose the scalable algorithm called MixedAD. |
| 156 | Theory-Based Causal Transfer:Integrating Instance-Level Induction and Abstract-Level Structure Learning | Mark Edmonds, Xiaojian Ma, Siyuan Qi, Yixin Zhu, Hongjing Lu, Song-Chun Zhu | In this paper, we approach the transfer learning challenge from a causal theory perspective. |
| 157 | Deep Spiking Delayed Feedback Reservoirs and Its Application in Spectrum Sensing of MIMO-OFDM Dynamic Spectrum Sharing | Kian Hamedani, Lingjia Liu, Shiya Liu, Haibo He, Yang Yi | In this paper, we introduce a deep spiking delayed feedback reservoir (DFR) model to combine DFR with spiking neuros: DFRs are a new type of recurrent neural networks (RNNs) that are able to capture the temporal correlations in time series while spiking neurons are energy-efficient and biologically plausible neurons models. |
| 158 | People Do Not Just Plan,They Plan to Plan | Mark Ho, David Abel, Jonathan Cohen, Michael Littman, Thomas Griffiths | Here, we formulate this aspect of planning as a meta-reasoning problem and formalize it in terms of a recursive Bellman objective that incorporates both task rewards and information-theoretic planning costs. |
| 159 | Effective AER Object Classification Using Segmented Probability-Maximization Learning in Spiking Neural Networks | Qianhui Liu, Haibo Ruan, Dong Xing, Huajin Tang, Gang Pan | To tackle this issue, we propose an AER object classification model using a novel segmented probability-maximization (SPA) learning algorithm. |
| 160 | Biologically Plausible Sequence Learning with Spiking Neural Networks | Zuozhu Liu, Thiparat Chotibut, Christopher Hillar, Shaowei Lin | Motivated by the celebrated discrete-time model of nervous activity outlined by McCulloch and Pitts in 1943, we propose a novel continuous-time model, the McCulloch-Pitts network (MPN), for sequence learning in spiking neural networks. |
| 161 | Transfer Reinforcement Learning Using Output-Gated Working Memory | Arthur Williams, Joshua Phillips | We propose that working memory-based generalization plays a significant role in a model’s ability to transfer knowledge successfully across tasks. |
| 162 | Machine Number Sense: A Dataset of Visual Arithmetic Problems for Abstract and Relational Reasoning | Wenhe Zhang, Chi Zhang, Yixin Zhu, Song-Chun Zhu | To endow such a crucial cognitive ability to machine intelligence, we propose a dataset, Machine Number Sense (MNS), consisting of visual arithmetic problems automatically generated using a grammar model—And-Or Graph (AOG). |
| 163 | STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits | Uttaran Bhattacharya, Trisha Mittal, Rohan Chandra, Tanmay Randhavane, Aniket Bera, Dinesh Manocha | We present a novel classifier network called STEP, to classify perceived human emotion from gaits, based on a Spatial Temporal Graph Convolutional Network (ST-GCN) architecture. We also release a novel dataset (E-Gait), which consists of 4,227 human gaits annotated with perceived emotions along with thousands of synthetic gaits. |
| 164 | Synch-Graph: Multisensory Emotion Recognition Through Neural Synchrony via Graph Convolutional Networks | Esma Mansouri-Benssassi, Juan Ye | In this paper, we present a novel bio-inspired approach based on neural synchrony in audio-visual multisensory integration in the brain, named Synch-Graph. |
| 165 | M3ER: Multiplicative Multimodal Emotion Recognition using Facial, Textual, and Speech Cues | Trisha Mittal, Uttaran Bhattacharya, Rohan Chandra, Aniket Bera, Dinesh Manocha | We present M3ER, a learning-based method for emotion recognition from multiple input modalities. |
| 166 | To Signal or Not To Signal: Exploiting Uncertain Real-Time Information in Signaling Games for Security and Sustainability | Elizabeth Bondi, Hoon Oh, Haifeng Xu, Fei Fang, Bistra Dilkina, Milind Tambe | We address this shortcoming by proposing a novel game model that integrates signaling and sensor uncertainty; perhaps surprisingly, we show that defenders can still perform well via a signaling strategy that exploits uncertain real-time information. |
| 167 | End-to-End Game-Focused Learning of Adversary Behavior in Security Games | Andrew Perrault, Bryan Wilder, Eric Ewing, Aditya Mate, Bistra Dilkina, Milind Tambe | Motivated by green security, where the defender may only observe an adversary’s response to defense on a limited set of targets, we study the problem of learning a defense that generalizes well to a new set of targets with novel feature values and combinations. |
| 168 | Modeling Electrical Motor Dynamics Using Encoder-Decoder with Recurrent Skip Connection | Sagar Verma, Nicolas Henwood, Marc Castella, Francois Malrait, Jean-Christophe Pesquet | In this paper, we explore the feasibility of modeling the dynamics of an electrical motor by following a data-driven approach, which uses only its inputs and outputs and does not make any assumption on its internal behaviour. |
| 169 | Tensorized LSTM with Adaptive Shared Memory for Learning Trends in Multivariate Time Series | Dongkuan Xu, Wei Cheng, Bo Zong, Dongjin Song, Jingchao Ni, Wenchao Yu, Yanchi Liu, Haifeng Chen, Xiang Zhang | In this paper, we propose a neural network, DeepTrends, for multivariate time series trend prediction. |
| 170 | Deep Unsupervised Binary Coding Networks for Multivariate Time Series Retrieval | Dixian Zhu, Dongjin Song, Yuncong Chen, Cristian Lumezanu, Wei Cheng, Bo Zong, Jingchao Ni, Takehiko Mizoguchi, Tianbao Yang, Haifeng Chen | To this end, we present Deep Unsupervised Binary Coding Networks (DUBCNs) to perform multivariate time series retrieval. |
| 171 | Improved Filtering for the Euclidean Traveling Salesperson Problem in CLP(FD) | Alessandro Bertagnon, Marco Gavanelli | In this work, we propose to use geometric information, present in Euclidean TSP instances, to improve the filtering power. |
| 172 | Chain Length and CSPs Learnable with Few Queries | Christian Bessiere, Cément Carbonnel, George Katsirelos | In this paper we focus on partial membership queries and initiate a systematic investigation of the learning complexity of constraint languages. |
| 173 | Guiding CDCL SAT Search via Random Exploration amid Conflict Depression | Md Solimul Chowdhury, Martin Müller, Jia You | Based on this analysis, we propose an exploration strategy, called expSAT, which randomly samples variable selection sequences in order to learn an updated heuristic from the generated conflicts. |
| 174 | Representative Solutions for Bi-Objective Optimisation | Emir Demirovi?, Nicolas Schwind | We implement our algorithm and empirically illustrate the efficiency on two families of benchmarks. |
| 175 | Dynamic Programming for Predict+Optimise | Emir Demirovi?, Peter J. Stuckey, Tias Guns, James Bailey, Christopher Leckie, Kotagiri Ramamohanarao, Jeffrey Chan | We study the predict+optimise problem, where machine learning and combinatorial optimisation must interact to achieve a common goal. |
| 176 | Accelerating Primal Solution Findings for Mixed Integer Programs Based on Solution Prediction | Jian-Ya Ding, Chao Zhang, Lei Shen, Shengyin Li, Bing Wang, Yinghui Xu, Le Song | To address this issue, we propose to represent a MIP instance using a tripartite graph, based on which a Graph Convolutional Network (GCN) is constructed to predict solution values for binary variables. |
| 177 | Optimization of Chance-Constrained Submodular Functions | Benjamin Doerr, Carola Doerr, Aneta Neumann, Frank Neumann, Andrew Sutton | In this paper, we investigate submodular optimization problems with chance constraints. |
| 178 | ADDMC: Weighted Model Counting with Algebraic Decision Diagrams | Jeffrey Dudek, Vu Phan, Moshe Vardi | We present an algorithm to compute exact literal-weighted model counts of Boolean formulas in Conjunctive Normal Form. |
| 179 | Modelling and Solving Online Optimisation Problems | Alexander Ek, Maria Garcia de la Banda, Andreas Schutt, Peter J. Stuckey, Guido Tack | This paper defines a general framework for automatically solving online optimisation problems. |
| 180 | Justifying All Differences Using Pseudo-Boolean Reasoning | Jan Elffers, Stephan Gocht, Ciaran McCreesh, Jakob Nordstom | We explain how such proofs can be expressed and verified mechanistically, describe an implementation, and discuss the broader implications for proof logging in constraint programming. |
| 181 | A Cardinal Improvement to Pseudo-Boolean Solving | Jan Elffers, Jakob Nordström | We present a technique to remedy this problem by recovering cardinality constraints from CNF on the fly during search. |
| 182 | MIPaaL: Mixed Integer Program as a Layer | Aaron Ferber, Bryan Wilder, Bistra Dilkina, Milind Tambe | We show how to differentiate through a MIP by employing a cutting planes solution approach, an algorithm that iteratively tightens the continuous relaxation by adding constraints removing fractional solutions. |
| 183 | Using Approximation within Constraint Programming to Solve the Parallel Machine Scheduling Problem with Additional Unit Resources | Arthur Godet, Xavier Lorca, Emmanuel Hebrard, Gilles Simonin | In this paper, we consider the Parallel Machine Scheduling Problem with Additional Unit Resources, which consists in scheduling a set of n jobs on m parallel unrelated machines and subject to exactly one of r unit resources. |
| 184 | SPAN: A Stochastic Projected Approximate Newton Method | Xunpeng Huang, Xianfeng Liang, Zhengyang Liu, Lei Li, Yue Yu, Yitan Li | In this paper, we propose SPAN, a novel approximate and fast Newton method. |
| 185 | Modelling Diversity of Solutions | Linnea Ingmar, Maria Garcia de la Banda, Peter J. Stuckey, Guido Tack | This paper describes a general framework for finding k diverse solutions to a combinatorial problem (be it satisfaction, single-objective or multi-objective), various approaches to solve problems in the framework, their implementations, and an experimental evaluation of their practicality. |
| 186 | Incremental Symmetry Breaking Constraints for Graph Search Problems | Avraham Itzhakov, Michael Codish | This paper introduces incremental symmetry breaking constraints for graph search problems which are complete and compact. |
| 187 | Finding Most Compatible Phylogenetic Trees over Multi-State Characters | Tuukka Korhonen, Matti Järvisalo | In this work we develop a new hybrid approach to solving maximum compatibility for multi-state characters, making use of both declarative optimization techniques (specifically maximum satisfiability, MaxSAT) and an adaptation of the Bouchitt’e-Todinca approach to triangulation-based graph optimization problems. |
| 188 | FourierSAT: A Fourier Expansion-Based Algebraic Framework for Solving Hybrid Boolean Constraints | Anastasios Kyrillidis, Anshumali Shrivastava, Moshe Vardi, Zhiwei Zhang | By such a reduction to continuous optimization, we propose an algebraic framework for solving systems consisting of different types of constraints. |
| 189 | Augmenting the Power of (Partial) MaxSat Resolution with Extension | Javier Larrosa, Emma Rollon | In this paper we augment the MaxSAT resolution proof system with an extension rule. |
| 190 | Solving Set Cover and Dominating Set via Maximum Satisfiability | Zhendong Lei, Shaowei Cai | In this paper, we develop an efficient local search solver for MaxSAT instances of this kind. |
| 191 | Finding Good Subtrees for Constraint Optimization Problems Using Frequent Pattern Mining | Hongbo Li, Jimmy Lee, He Mi, Minghao Yin | In this paper, we propose a method employing frequent pattern mining (FPM), a classic datamining technique, to find good subtrees for solving constraint optimization problems. |
| 192 | An Effective Hard Thresholding Method Based on Stochastic Variance Reduction for Nonconvex Sparse Learning | Guannan Liang, Qianqian Tong, Chunjiang Zhu, Jinbo Bi | We propose a hard thresholding method based on stochastically controlled stochastic gradients (SCSG-HT) to solve a family of sparsity-constrained empirical risk minimization problems. |
| 193 | Accelerating Column Generation via Flexible Dual Optimal Inequalities with Application to Entity Resolution | Vishnu Suresh Lokhande, Shaofei Wang, Maneesh Singh, Julian Yarkony | In this paper, we introduce a new optimization approach to Entity Resolution. |
| 194 | Smart Predict-and-Optimize for Hard Combinatorial Optimization Problems | Jayanta Mandi, Emir Demirovi?, Peter J. Stuckey, Tias Guns | To this end, we investigate ways to relax the problem as well as warm-starting the learning and the solving. |
| 195 | Grammar Filtering for Syntax-Guided Synthesis | Kairo Morton, William Hallahan, Elven Shum, Ruzica Piskac, Mark Santolucito | In this work, we propose a system for using machine learning in tandem with automated reasoning techniques to solve Syntax Guided Synthesis (SyGuS) style PBE problems. |
| 196 | D-SPIDER-SFO: A Decentralized Optimization Algorithm with Faster Convergence Rate for Nonconvex Problems | Taoxing Pan, Jun Liu, Jie Wang | To tackle this problem, we propose a decentralized variant of SPIDER-SFO, called decentralized SPIDER-SFO (D-SPIDER-SFO). |
| 197 | Estimating the Density of States of Boolean Satisfiability Problems on Classical and Quantum Computing Platforms | Tuhin Sahai, Anurag Mishra, Jose Miguel Pasini, Susmit Jha | We present the overall approach and compare results from the D-Wave quantum annealer against the best-known classical algorithms such as the Hamze-de Freitas-Selby (HFS) algorithm and satisfiability modulo theory (SMT) solvers. |
| 198 | Efficient Algorithms for Generating Provably Near-Optimal Cluster Descriptors for Explainability | Prathyush Sambaturu, Aparna Gupta, Ian Davidson, S. S. Ravi, Anil Vullikanti, Andrew Warren | Since this problem is NP-hard in general, we develop approximation algorithms with provable performance guarantees for the problem. |
| 199 | Probabilistic Inference for Predicate Constraint Satisfaction | Yuki Satake, Hiroshi Unno, Hinata Yanagi | In this paper, we present a novel constraint solving method for a class of predicate Constraint Satisfaction Problems (pCSP) where each constraint is represented by an arbitrary clause of first-order predicate logic over predicate variables. |
| 200 | Hard Examples for Common Variable Decision Heuristics | Marc Vinyals | We give a negative answer by building a family of formulas that have resolution proofs of polynomial size but require exponential time to decide in CDCL with common heuristics such as VMTF, CHB, and certain implementations of VSIDS and LRB. |
| 201 | Multiple Graph Matching and Clustering via Decayed Pairwise Matching Composition | Tianzhe Wang, Zetian Jiang, Junchi Yan | Seeing its practical importance, we propose a novel approach for multiple graph matching and clustering. |
| 202 | Constructing Minimal Perfect Hash Functions Using SAT Technology | Sean Weaver, Marijn Heule | In this article, we propose two SAT-based constructions of MPHFs. |
| 203 | Explaining Propagators for String Edit Distance Constraints | Felix Winter, Nysret Musliu, Peter Stuckey | In this work, we propose a novel global constraint to formulate restrictions on the minimum edit distance for such problems. |
| 204 | Deep Neural Network Approximated Dynamic Programming for Combinatorial Optimization | Shenghe Xu, Shivendra S. Panwar, Murali Kodialam, T.V. Lakshman | In this paper, we propose a general framework for combining deep neural networks (DNNs) with dynamic programming to solve combinatorial optimization problems. |
| 205 | Generating Interactive Worlds with Text | Angela Fan, Jack Urbanek, Pratik Ringshia, Emily Dinan, Emma Qian, Siddharth Karamcheti, Shrimai Prabhumoye, Douwe Kiela, Tim Rocktaschel, Arthur Szlam, Jason Weston | In this work, we investigate a machine learning approach for world creation using content from the multi-player text adventure game environment LIGHT (Urbanek et al. 2019). |
| 206 | Deep Reinforcement Learning for General Game Playing | Adrian Goldwaser, Michael Thielscher | This work applies deep reinforcement learning to General Game Playing, extending the AlphaZero algorithm and finds that it can provide competitive results. |
| 207 | Narrative Planning Model Acquisition from Text Summaries and Descriptions | Thomas Hayton, Julie Porteous, Joao Ferreira, Alan Lindsay | We present a solution which analyses input NL text summaries, and builds structured representations from which a pddl model is output (fully automated or author in-the-loop). |
| 208 | FET-GAN: Font and Effect Transfer via K-shot Adaptive Instance Normalization | Wei Li, Yongxing He, Yanwei Qi, Zejian Li, Yongchuan Tang | To address these problems, we propose FET-GAN, a novel end-to-end framework to implement visual effects transfer with font variation among multiple text effects domains. Besides, we have collected a font dataset including 100 fonts of more than 800 Chinese and English characters. |
| 209 | A Character-Centric Neural Model for Automated Story Generation | Danyang Liu, Juntao Li, Meng-Hsuan Yu, Ziming Huang, Gongshen Liu, Dongyan Zhao, Rui Yan | To fill this gap, we propose a character-centric neural storytelling model, where a story is created encircling the given character, i.e., each part of a story is conditioned on a given character and corresponded context environment. |
| 210 | Fast and Robust Face-to-Parameter Translation for Game Character Auto-Creation | Tianyang Shi, Zhengxia Zuo, Yi Yuan, Changjie Fan, Tianyang Shi, Zhengxia Zuo, Yi Yuan, Changjie Fan | This paper proposes a game character auto-creation framework that generates in-game characters according to a player’s input face photo. |
| 211 | Draft and Edit: Automatic Storytelling Through Multi-Pass Hierarchical Conditional Variational Autoencoder | Meng-Hsuan Yu, Juntao Li, Danyang Liu, Dongyan Zhao, Rui Yan, Bo Tang, Haisong Zhang | In this paper, we proposed a multi-pass hierarchical conditional variational autoencoder model to overcome the challenges and limitations in existing automatic storytelling models. |
| 212 | Distance-Based Equilibria in Normal-Form Games | Erman Acar, Reshef Meir | We propose a simple uncertainty modification for the agent model in normal-form games; at any given strategy profile, the agent can access only a set of “possible profiles” that are within a certain distance from the actual action profile. |
| 213 | Swap Stability in Schelling Games on Graphs | Aishwarya Agarwal, Edith Elkind, Jiarui Gan, Alexandros Voudouris | We study a recently introduced class of strategic games that is motivated by and generalizes Schelling’s well-known residential segregation model. |
| 214 | The Impact of Selfishness in Hypergraph Hedonic Games | Alessandro Aloisio, Michele Flammini, Cosimo Vinci | We consider a class of coalition formation games that can be succinctly represented by means of hypergraphs and properly generalizes symmetric additively separable hedonic games. |
| 215 | Multiagent Evaluation Mechanisms | Tal Alon, Magdalen Dobson, Ariel Procaccia, Inbal Talgam-Cohen, Jamie Tucker-Foltz | Our goal is to craft evaluation mechanisms that incentivize the agents to invest effort in desirable actions; a notable application is the design of course grading schemes. |
| 216 | Peeking Behind the Ordinal Curtain: Improving Distortion via Cardinal Queries | Georgios Amanatidis, Georgios Birmpas, Aris Filos-Ratsikas, Alexandros Voudouris | In this paper, we take a more expressive approach, and consider mechanisms that are allowed to further ask a few cardinal queries in order to gain partial access to the underlying values that the agents have for the alternatives. |
| 217 | Multiple Birds with One Stone: Beating 1/2 for EFX and GMMS via Envy Cycle Elimination | Georgios Amanatidis, Evangelos Markakis, Apostolos Ntokos | In this work, we propose a simple algorithm that is universally fair in the sense that it returns allocations that have good approximation guarantees with respect to four such fairness notions at once. |
| 218 | All-Pay Bidding Games on Graphs | Guy Avni, Rasmus Ibsen-Jensen, Josef Tkadlec | In this paper we introduce and study all-pay bidding games, a class of two player, zero-sum games on graphs. |
| 219 | Facility Location Problem with Capacity Constraints: Algorithmic and Mechanism Design Perspectives | Haris Aziz, Hau Chan, Barton Lee, Bo Li, Toby Walsh | We consider the facility location problem in the one-dimensional setting where each facility can serve a limited number of agents from the algorithmic and mechanism design perspectives. |
| 220 | Fair Division of Mixed Divisible and Indivisible Goods | Xiaohui Bei, Zihao Li, Jinyan Liu, Shengxin Liu, Xinhang Lu | In this work, we propose a new fairness notion envy-freeness for mixed goods (EFM), which is a direct generalization of both EF and EF1 to the mixed goods setting. |
| 221 | Individual-Based Stability in Hedonic Diversity Games | Niclas Boehmer, Edith Elkind | In this work, we extend and strengthen these results in several ways. |
| 222 | Adapting Stable Matchings to Evolving Preferences | Robert Bredereck, Jiehua Chen, Dušan Knop, Junjie Luo, Rolf Niedermeier | We address this by proposing “incrementalized versions” of Stable Marriage and Stable Roommates. |
| 223 | Parameterized Algorithms for Finding a Collective Set of Items | Robert Bredereck, Piotr Faliszewski, Andrzej Kaczmarczyk, Dušan Knop, Rolf Niedermeier | We extend the work of Skowron et al. (AIJ, 2016) by considering the parameterized complexity of the following problem. |
| 224 | Electing Successive Committees: Complexity and Algorithms | Robert Bredereck, Andrzej Kaczmarczyk, Rolf Niedermeier | We introduce successive committees elections. |
| 225 | Approval-Based Apportionment | Markus Brill, Paul Gölz, Dominik Peters, Ulrike Schmidt-Kraepelin, Kai Wilker | We study a generalization of this setting, in which voters cast approval ballots over parties, such that each voter can support multiple parties. |
| 226 | Refining Tournament Solutions via Margin of Victory | Markus Brill, Ulrike Schmidt-Kraepelin, Warut Suksompong | In this paper, we propose a general framework for refining tournament solutions. |
| 227 | Persuading Voters: It’s Easy to Whisper, It’s Hard to Speak Loud | Matteo Castiglioni, Andrea Celli, Nicola Gatti | We focus on the following natural question: is it possible to influence the outcome of a voting process through the strategic provision of information to voters who update their beliefs rationally? |
| 228 | Election Control in Social Networks via Edge Addition or Removal | Matteo Castiglioni, Diodato Ferraioli, Nicola Gatti | In this paper, instead, we assume the set of influencers and their messages to be given, and we ask whether a manipulator (e.g., the platform) can alter the outcome of the election by adding or removing edges in the social network. |
| 229 | Private Bayesian Persuasion with Sequential Games | Andrea Celli, Stefano Coniglio, Nicola Gatti | Instead, in contrast with previous hardness results for ex interim persuasion, we show that, for games with two receivers, an optimal ex ante persuasive signaling scheme can be computed in polynomial time thanks to the novel algorithm we propose, based on the ellipsoid method. |
| 230 | Favorite-Candidate Voting for Eliminating the Least Popular Candidate in a Metric Space | Xujin Chen, Minming Li, Chenhao Wang | We study single-candidate voting embedded in a metric space, where both voters and candidates are points in the space, and the distances between voters and candidates specify the voters’ preferences over candidates. |
| 231 | Manipulating Districts to Win Elections: Fine-Grained Complexity | Eduard Eiben, Fedor Fomin, Fahad Panolan, Kirill Simonov | Lewenberg, Lev, and Rosenschein [AAMAS 2017] initiated the algorithmic study of a geographically-based manipulation problem, where voters must vote at the ballot box closest to them. |
| 232 | On Swap Convexity of Voting Rules | Svetlana Obraztsova, Edith Elkind, Piotr Faliszewski | In this paper, we (1) propose several families of voting rules that are convex in the sense of Obraztsova et al.; (2) put forward a weaker notion of convexity that is satisfied by most common voting rules; (3) prove impossibility results for a variant of this definition that considers all, rather than some shortest paths. |
| 233 | Analysis of One-to-One Matching Mechanisms via SAT Solving: Impossibilities for Universal Axioms | Ulle Endriss | We develop a powerful approach that makes modern SAT solving techniques available as a tool to support the axiomatic analysis of economic matching mechanisms. |
| 234 | Iterative Delegations in Liquid Democracy with Restricted Preferences | Bruno Escoffier, Hugo Gilbert, Adèle Pass-Lanneau | In this paper, we investigate the stability of the delegation process in liquid democracy when voters have restricted types of preference on the agent representing them (e.g., single-peaked preferences). |
| 235 | Coarse Correlation in Extensive-Form Games | Gabriele Farina, Tommaso Bianchi, Tuomas Sandholm | In this paper, we consider two instantiations of the idea of coarse correlation in extensive-form games: normal-form coarse-correlated equilibrium (NFCCE), already defined in the literature, and extensive-form coarse-correlated equilibrium (EFCCE), a new solution concept that we introduce. |
| 236 | Designing Committees for Mitigating Biases | Michal Feldman, Yishay Mansour, Noam Nisan, Sigal Oren, Moshe Tennenholtz | In this paper, we suggest that putting the decision in the hands of a committee instead of a single person can reduce this bias. |
| 237 | Strategyproof Mechanisms for Friends and Enemies Games | Michele Flammini, Bojana Kodric, Giovanna Varricchio | We investigate strategyproof mechanisms for Friends and Enemies Games, a subclass of Hedonic Games in which every agent classifies any other one as a friend or as an enemy. |
| 238 | Preventing Arbitrage from Collusion When Eliciting Probabilities | Rupert Freeman, David M. Pennock, Dominik Peters, Bo Waggoner | We consider two approaches to protect against colluding agents. |
| 239 | VCG under Sybil (False-Name) Attacks – A Bayesian Analysis | Yotam Gafni, Ron Lavi, Moshe Tennenholtz | In service of that we introduce a novel notion, termed the granularity threshold, that characterizes VCG Bayesian resilience to false-name attacks as a function of the bidder type distribution. |
| 240 | Bidding in Smart Grid PDAs: Theory, Analysis and Strategy | Susobhan Ghosh, Sujit Gujar, Praveen Paruchuri, Easwar Subramanian, Sanjay Bhat | In this paper, we perform an equilibrium analysis of single unit single-shot double auctions with a certain clearing price and payment rule, which we refer to as ACPR, and find it intractable to analyze as number of participating agents increase. |
| 241 | Beyond Pairwise Comparisons in Social Choice: A Setwise Kemeny Aggregation Problem | Hugo Gilbert, Tom Portoleau, Olivier Spanjaard | In this paper, we advocate the use of setwise contests for aggregating a set of input rankings into an output ranking. |
| 242 | Contiguous Cake Cutting: Hardness Results and Approximation Algorithms | Paul W. Goldberg, Alexandros Hollender, Warut Suksompong | We study the fair allocation of a cake, which serves as a metaphor for a divisible resource, under the requirement that each agent should receive a contiguous piece of the cake. |
| 243 | Strongly Budget Balanced Auctions for Multi-Sided Markets | Rica Gonen, Erel Segal-Halevi | We attempt to address these settings. |
| 244 | The Complexity of Computing Maximin Share Allocations on Graphs | Gianluigi Greco, Francesco Scarcello | In this paper we consider this notion within a setting where bundles of goods must induce connected subsets over an underlying graph. |
| 245 | Fair Division Through Information Withholding | Hadi Hosseini, Sujoy Sikdar, Rohit Vaish, Hejun Wang, Lirong Xia | We observe that in practice, envy-freeness can be achieved by withholding only a small number of goods overall. |
| 246 | Model and Reinforcement Learning for Markov Games with Risk Preferences | Wenjie Huang, Viet Hai Pham, William Benjamin Haskell | We motivate and propose a new model for non-cooperative Markov game which considers the interactions of risk-aware players. |
| 247 | A Simple, Fast, and Safe Mediator for Congestion Management | Kei Ikegami, Kyohei Okumura, Takumi Yoshikawa | We propose a mediator based on a version of best response dynamics (BRD). |
| 248 | Repeated Multimarket Contact with Private Monitoring: A Belief-Free Approach | Atsushi Iwasaki, Tadashi Sekiguchi, Shun Yamamoto, Makoto Yokoo | We thus focus on two-state automaton strategies such that the players are cooperative in at least one market even when he or she punishes a traitor. |
| 249 | A Multiarmed Bandit Based Incentive Mechanism for a Subset Selection of Customers for Demand Response in Smart Grids | Jain Shweta, Gujar Sujit | Towards this, we propose a novel combinatorial multi-armed bandit (MAB) algorithm, which we refer to as \namemab\ to learn the uncertainties along with an auction to elicit true costs incurred by the consumers. |
| 250 | Double-Oracle Sampling Method for Stackelberg Equilibrium Approximation in General-Sum Extensive-Form Games | Jan Karwowski, Jacek Mańdziuk | The paper presents a new method for approximating Strong Stackelberg Equilibrium in general-sum sequential games with imperfect information and perfect recall. |
| 251 | Strategy-Proof and Non-Wasteful Multi-Unit Auction via Social Network | Takehiro Kawasaki, Nathanael Barrot, Seiji Takanashi, Taiki Todo, Makoto Yokoo | Strategy-Proof and Non-Wasteful Multi-Unit Auction via Social Network |
| 252 | On the Max-Min Fair Stochastic Allocation of Indivisible Goods | Yasushi Kawase, Hanna Sumita | We propose an (approximation) algorithm to find a stochastic allocation that maximizes the minimum utility among the agents. |
| 253 | An Analysis Framework for Metric Voting based on LP Duality | David Kempe | We provide a framework based on LP-duality and flow interpretations of the dual which provides a simpler and more unified way for proving upper bounds on the distortion of social choice rules. |
| 254 | Communication, Distortion, and Randomness in Metric Voting | David Kempe | We show that any one-round deterministic voting mechanism in which each voter communicates only the candidates she ranks in a given set of k positions must have distortion at least 2n-k/k; we give a mechanism achieving an upper bound of O(n/k), which matches the lower bound up to a constant. |
| 255 | Information Elicitation Mechanisms for Statistical Estimation | Yuqing Kong, Grant Schoenebeck, Biaoshuai Tao, Fang-Yi Yu | We study learning statistical properties from strategic agents with private information. |
| 256 | Perpetual Voting: Fairness in Long-Term Decision Making | Martin Lackner | In this paper we introduce a new voting formalism to support long-term collective decision making: perpetual voting rules. |
| 257 | Defending with Shared Resources on a Network | Minming Li, Long Tran-Thanh, Xiaowei Wu | In this paper we consider a defending problem on a network. |
| 258 | Structure Learning for Approximate Solution of Many-Player Games | Zun Li, Michael Wellman | We introduce an iterative structure-learning approach to search for approximate solutions of many-player games, assuming only black-box simulation access to noisy payoff samples. |
| 259 | Adaptive Quantitative Trading: An Imitative Deep Reinforcement Learning Approach | Yang Liu, Qi Liu, Hongke Zhao, Zhen Pan, Chuanren Liu | To address the challenges, we propose an adaptive trading model, namely iRDPG, to automatically develop QT strategies by an intelligent trading agent. |
| 260 | Limitations of Incentive Compatibility on Discrete Type Spaces | Taylor Lundy, Hu Fu | In this work, we explore limitations of this approach, by studying whether all dominant strategy incentive compatible mechanisms on a set T of discrete types can be extended to the convex hull of T. Dobzinski, Fu and Kleinberg (2015) answered the question affirmatively for all settings where types are single dimensional. |
| 261 | Mechanism Design with Predicted Task Revenue for Bike Sharing Systems | Hongtao Lv, Chaoli Zhang, Zhenzhe Zheng, Tie Luo, Fan Wu, Guihai Chen | In this paper, we propose an incentive mechanism called TruPreTar to incentivize users to park bicycles at locations desired by the platform toward rebalancing supply and demand. |
| 262 | Lifting Preferences over Alternatives to Preferences over Sets of Alternatives: The Complexity of Recognizing Desirable Families of Sets | Jan Maly | In this paper, we determine the computational complexity of recognizing such families. |
| 263 | The Effectiveness of Peer Prediction in Long-Term Forecasting | Mandal Debmalya, Radanović Goran, Parkes David | The Effectiveness of Peer Prediction in Long-Term Forecasting |
| 264 | The Surprising Power of Hiding Information in Facility Location | Safwan Hossain, Evi Micha, Nisarg Shah | We revisit this problem in a more general framework. |
| 265 | Can We Predict the Election Outcome from Sampled Votes? | Evi Micha, Nisarg Shah | We propose a framework in which we are given the ranked preferences of k out of n individuals sampled from a distribution, and the goal is to predict what a given voting rule would output if applied on the underlying preferences of all n individuals. |
| 266 | Price of Fairness in Budget Division and Probabilistic Social Choice | Marcin Michorzewski, Dominik Peters, Piotr Skowron | We assume that agents have approval preferences over projects, and their utility is the fraction of the budget spent on approved projects. |
| 267 | Robust Market Equilibria with Uncertain Preferences | Riley Murray, Christian Kroer, Alex Peysakhovich, Parikshit Shah | In this paper, we show how concepts from classical market equilibrium can be extended to reflect such uncertainty. |
| 268 | Practical Frank–Wolfe Method with Decision Diagrams for Computing Wardrop Equilibrium of Combinatorial Congestion Games | Kengo Nakamura, Shinsaku Sakaue, Norihito Yasuda | In this paper, we propose a practical algorithm for such hard equilibrium computation problems. |
| 269 | Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms during High-Demand Hours | Vedant Nanda, Pan Xu, Karthik Abhinav Sankararaman, John Dickerson, Aravind Srinivasan | To balance these conflicting goals, we present a flexible, non-adaptive algorithm, NAdap, that allows the platform designer to control the profit and fairness of the system via parameters α and β respectively. |
| 270 | Comparing Election Methods Where Each Voter Ranks Only Few Candidates | Matthias Bentert, Piotr Skowron | We establish theoretical bounds on the approximation ratios and complement our theoretical analysis with computer simulations. |
| 271 | Solving Online Threat Screening Games using Constrained Action Space Reinforcement Learning | Sanket Shah, Sinha Arunesh, Varakantham Pradeep, Perrault Andrew, Tambe Milind | To address this, we propose an online threat screening model in which the screening strategy is determined adaptively as a passenger arrives while satisfying a hard bound on acceptable risk of not screening a threat. |
| 272 | Reinforcement Mechanism Design: With Applications to Dynamic Pricing in Sponsored Search Auctions | Weiran Shen, Binghui Peng, Hanpeng Liu, Michael Zhang, Ruohan Qian, Yan Hong, Zhi Guo, Zongyao Ding, Pengjun Lu, Pingzhong Tang | We examine such a social system in the setting of sponsored search auctions and tackle the search engine’s dynamic pricing problem by combining the tools from both mechanism design and the AI domain. |
| 273 | Complexity of Computing the Shapley Value in Games with Externalities | Oskar Skibski | We study the complexity of computing the Shapley value in games with externalities. |
| 274 | Path Planning Problems with Side Observations—When Colonels Play Hide-and-Seek | Dong Quan Vu, Patrick Loiseau, Alonso Silva, Long Tran-Thanh | In this work, we show that the online CB and HS games can be cast as path planning problems with side-observations (SOPPP): at each stage, a learner chooses a path on a directed acyclic graph and suffers the sum of losses that are adversarially assigned to the corresponding edges; and she then receives semi-bandit feedback with side-observations (i.e., she observes the losses on the chosen edges plus some others). |
| 275 | Multi-Type Resource Allocation with Partial Preferences | Haibin Wang, Sujoy Sikdar, Xiaoxi Guo, Lirong Xia, Yongzhi Cao, Hanpin Wang | We propose multi-type probabilistic serial (MPS) and multi-type random priority (MRP) as extensions of the well-known PS and RP mechanisms to the multi-type resource allocation problems (MTRAs) with partial preferences. |
| 276 | Nice Invincible Strategy for the Average-Payoff IPD | Shiheng Wang, Fangzhen Lin | In this paper, we consider what we call invincible strategies. |
| 277 | Bounded Incentives in Manipulating the Probabilistic Serial Rule | Zihe Wang, Zhide Wei, Jie Zhang | In this paper, we characterize the extent to which an individual agent can increase its utility by strategic manipulation. |
| 278 | Deep Learning—Powered Iterative Combinatorial Auctions | Jakob Weissteiner, Sven Seuken | In this paper, we study the design of deep learning-powered iterative combinatorial auctions (ICAs). |
| 279 | A Multi-Unit Profit Competitive Mechanism for Cellular Traffic Offloading | Jun Wu, Yu Qiao, Lei Zhang, Chongjun Wang, Meilin Liu | Based on the framework of random sampling and profit extraction, we aim to design a prior-free mechanism which guarantees a profit competitive to the omniscient single-price auction. |
| 280 | Algorithms for Manipulating Sequential Allocation | Mingyu Xiao, Jiaxing Ling | In this paper, we give a novel algorithm that solves the problem in polynomial time for each fixed number of agents. |
| 281 | Computing Equilibria in Binary Networked Public Goods Games | Sixie Yu, Kai Zhou, Jeffrey Brantingham, Yevgeniy Vorobeychik | In this paper, we examine a specific type of public goods game where players are networked and each has binary actions, and focus on the algorithmic aspects of such games. |
| 282 | Computing Team-Maxmin Equilibria in Zero-Sum Multiplayer Extensive-Form Games | Youzhi Zhang, Bo An | The study of finding the equilibrium for multiplayer games is challenging. |
| 283 | A Unifying View on Individual Bounds and Heuristic Inaccuracies in Bidirectional Search | Vidal Alcázar, Pat Riddle, Mike Barley | In this paper we define individual bounds within the lower-bound framework and show how both Kaindl and Kainz’s and Sadhukhan’s methods can be generalized thus creating new bounds. |
| 284 | An Interactive Regret-Based Genetic Algorithm for Solving Multi-Objective Combinatorial Optimization Problems | Nawal Benabbou, Cassandre Leroy, Thibaut Lust | We propose a new approach consisting in combining genetic algorithms and regret-based incremental preference elicitation for solving multi-objective combinatorial optimization problems with unknown preferences. |
| 285 | Local Search with Dynamic-Threshold Configuration Checking and Incremental Neighborhood Updating for Maximum k-plex Problem | Peilin Chen, Hai Wan, Shaowei Cai, Jia Li, Haicheng Chen | In this paper, we propose a novel strategy, named Dynamic-threshold Configuration Checking (DCC), to reduce the cycling problem of local search. |
| 286 | Envelope-Based Approaches to Real-Time Heuristic Search | Kevin Gall, Bence Cserna, Wheeler Ruml | In this paper, we investigate the alternative paradigm in which the search expands a single ever-growing envelope of states. |
| 287 | Runtime Analysis of Somatic Contiguous Hypermutation Operators in MOEA/D Framework | Zhengxin Huang, Yuren Zhou | In this paper, we present a runtime analysis of using two CHM operators in MOEA/D framework for solving five benchmark MOPs, including four bi-objective and one many-objective problems. |
| 288 | Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems | Sami Khairy, Ruslan Shaydulin, Lukasz Cincio, Yuri Alexeev, Prasanna Balaprakash | In this paper, we formulate the problem of finding optimal QAOA parameters as a learning task in which the knowledge gained from solving training instances can be leveraged to find high-quality solutions for unseen test instances. |
| 289 | How the Duration of the Learning Period Affects the Performance of Random Gradient Selection Hyper-Heuristics | Andrei Lissovoi, Pietro Oliveto, John Alasdair Warwicker | In this paper, we examine the impact of the learning period on the performance of the hyper-heuristic for standard unimodal benchmark functions with different characteristics: Ridge, where the HH has to learn that RLS1 is always the best low-level heuristic, and OneMax, where different low-level heuristics are preferable in different areas of the search space. |
| 290 | On Performance Estimation in Automatic Algorithm Configuration | Shengcai Liu, Ke Tang, Yunwei Lei, Xin Yao | Over the last decade, research on automated parameter tuning, often referred to as automatic algorithm configuration (AAC), has made significant progress. |
| 291 | A Learning Based Branch and Bound for Maximum Common Subgraph Related Problems | Yanli Liu, Chu-Min Li, Hua Jiang, Kun He | We propose a branching heuristic inspired from reinforcement learning with a goal of reaching a tree leaf as early as possible to greatly reduce the search tree size. |
| 292 | Cakewalk Sampling | Uri Patish, Shimon Ullman | Since the effectiveness of this strategy depends on the sampling distribution, we derive a robust learning algorithm that adapts sampling distributions towards good local optima of arbitrary objective functions. |
| 293 | Subset Selection by Pareto Optimization with Recombination | Chao Qian, Chao Bian, Chao Feng | In this paper, we propose the PORSS algorithm by incorporating recombination, a characterizing feature of EAs, into POSS. |
| 294 | Asymptotic Risk of Bézier Simplex Fitting | Akinori Tanaka, Akiyoshi Sannai, Ken Kobayashi, Naoki Hamada | In this paper, we analyze the asymptotic risks of those B’ezier simplex fitting methods and derive the optimal subsample ratio for the inductive skeleton fitting. |
| 295 | Trading Convergence Rate with Computational Budget in High Dimensional Bayesian Optimization | Hung Tran-The, Sunil Gupta, Santu Rana, Svetha Venkatesh | We propose a novel approach where the acquisition function only requires maximisation on a discrete set of low dimensional subspaces embedded in the original high-dimensional search space. |
| 296 | Reduction and Local Search for Weighted Graph Coloring Problem | Yiyuan Wang, Shaowei Cai, Shiwei Pan, Ximing Li, Monghao Yin | This paper explores techniques for solving WGCP, including a lower bound and a reduction rule based on clique sampling, and a local search algorithm based on two selection rules and a new variant of configuration checking. |
| 297 | Enumerating Maximal <em>k</em>-Plexes with Worst-Case Time Guarantee | Yi Zhou, Jingwei Xu, Zhenyu Guo, Mingyu Xiao, Yan Jin | In this paper, we investigate the problem of enumerating all maximal k-plexes and present FaPlexen, an enumeration algorithm which integrates the “pivot” heuristic and new branching schemes. |
| 298 | A Human-AI Loop Approach for Joint Keyword Discovery and Expectation Estimation in Micropost Event Detection | Akansha Bhardwaj, Jie Yang, Philippe Cudré-Mauroux | This paper introduces a Human-AI loop approach to jointly discover informative keywords for model training while estimating their expectation. |
| 299 | Just Ask: An Interactive Learning Framework for Vision and Language Navigation | Ta-Chung Chi, Minmin Shen, Mihail Eric, Seokhwan Kim, Dilek Hakkani-tur | We propose an interactive learning framework to endow the agent with the ability to ask for users’ help in such situations. |
| 300 | Asymptotically Unambitious Artificial General Intelligence | Michael Cohen, Badri Vellambi, Marcus Hutter | We present the first algorithm we are aware of for asymptotically unambitious AGI, where “unambitiousness” includes not seeking arbitrary power. |
| 301 | A Framework for Engineering Human/Agent Teaming Systems | Rick Evertsz, John Thangarajah | The performance of human participants in the study indicates that their ability to work in concert with the non-player characters in the game is significantly enhanced by the timely presentation of a diagrammatic representation of team cognition. |
| 302 | What Is It You Really Want of Me? Generalized Reward Learning with Biased Beliefs about Domain Dynamics | Ze Gong, Yu Zhang | In this paper, we remove this restrictive assumption by considering that the human may have an inaccurate understanding of the robot. |
| 303 | Explainable Reinforcement Learning through a Causal Lens | Prashan Madumal, Tim Miller, Liz Sonenberg, Frank Vetere | In this paper, we use causal models to derive causal explanations of the behaviour of model-free reinforcement learning agents. |
| 304 | Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks | Woo-Jeoung Nam, Shir Gur, Jaesik Choi, Lior Wolf, Seong-Whan Lee | In this paper, we propose Relative Attributing Propagation (RAP), which decomposes the output predictions of DNNs with a new perspective of separating the relevant (positive) and irrelevant (negative) attributions according to the relative influence between the layers. |
| 305 | Human-Machine Collaboration for Fast Land Cover Mapping | Caleb Robinson, Anthony Ortiz, Kolya Malkin, Blake Elias, Andi Peng, Dan Morris, Bistra Dilkina, Nebojsa Jojic | We propose incorporating human labelers in a model fine-tuning system that provides immediate user feedback. |
| 306 | Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior Explanations | Sarath Sreedharan, Tathagata Chakraborti, Christian Muise, Subbarao Kambhampati | In this work, we present a new planning formalism called Expectation-Aware planning for decision making with humans in the loop where the human’s expectations about an agent may differ from the agent’s own model. |
| 307 | Corpus-Level End-to-End Exploration for Interactive Systems | Zhiwen Tang, Grace Hui Yang | In this paper, we present a novel corpus-level end-to-end exploration (CE3) method to address these issues. |
| 308 | Learning to Interactively Learn and Assist | Mark Woodward, Chelsea Finn, Karol Hausman | In this paper, we propose such interactive learning as an alternative to reward or demonstration-driven learning. |
| 309 | CG-GAN: An Interactive Evolutionary GAN-Based Approach for Facial Composite Generation | Nicola Zaltron, Luisa Zurlo, Sebastian Risi | In this paper, we improve the efficiency of composite creation by removing the reliance on expert knowledge and letting the system learn to represent faces from examples. |
| 310 | Querying to Find a Safe Policy under Uncertain Safety Constraints in Markov Decision Processes | Shun Zhang, Edmund Durfee, Satinder Singh | Our goal is an algorithm that queries about as few potential side-effects as possible to find a safe policy, or to prove that none exists. |
| 311 | CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines | Arjun Akula, Shuai Wang, Song-Chun Zhu | We present CoCoX (short for Conceptual and Counterfactual Explanations), a model for explaining decisions made by a deep convolutional neural network (CNN). |
| 312 | Towards Awareness of Human Relational Strategies in Virtual Agents | Ian Beaver, Cynthia Freeman, Abdullah Mueen | We compare the usage of such language in human-human service interactions. |
| 313 | Regression under Human Assistance | Abir De, Paramita Koley, Niloy Ganguly, Manuel Gomez-Rodriguez | In this paper, we take a first step towards the development of machine learning models that are optimized to operate under different automation levels. More specifically, we first introduce the problem of ridge regression under human assistance and show that it is NP-hard. |
| 314 | MIMAMO Net: Integrating Micro- and Macro-Motion for Video Emotion Recognition | Didan Deng, Zhaokang Chen, Yuqian Zhou, Bertram Shi | In this paper, we propose to combine micro- and macro-motion features to improve video emotion recognition with a two-stream recurrent network, named MIMAMO (Micro-Macro-Motion) Net. |
| 315 | Conditional Generative Neural Decoding with Structured CNN Feature Prediction | Changde Du, Changying Du, Lijie Huang, Huiguang He | In this paper, we present a novel conditional deep generative neural decoding approach with structured intermediate feature prediction. |
| 316 | GaSPing for Utility | Mengyang Gu, Debarun Bhattacharjya, Dharmashankar Subramanian | We introduce a Bayesian nonparametric method involving Gaussian stochastic processes for estimating a utility function from direct elicitation responses. |
| 317 | Harnessing GANs for Zero-Shot Learning of New Classes in Visual Speech Recognition | Yaman Kumar, Dhruva Sahrawat, Shubham Maheshwari, Debanjan Mahata, Amanda Stent, Yifang Yin, Rajiv Ratn Shah, Roger Zimmermann | To solve this problem, we present a novel approach to zero-shot learning by generating new classes using Generative Adversarial Networks (GANs), and show how the addition of unseen class samples increases the accuracy of a VSR system by a significant margin of 27% and allows it to handle speaker-independent out-of-vocabulary phrases. |
| 318 | Graph-Based Decoding Model for Functional Alignment of Unaligned fMRI Data | Weida Li, Mingxia Liu, Fang Chen, Daoqiang Zhang | In this paper, a cross-subject graph that depicts the (dis)similarities between samples across subjects is used as a priori for developing a more flexible framework that suits an assortment of fMRI datasets. |
| 319 | Multi-Source Domain Adaptation for Visual Sentiment Classification | Chuang Lin, Sicheng Zhao, Lei Meng, Tat-Seng Chua | In this paper, we propose a novel multi-source domain adaptation (MDA) method, termed Multi-source Sentiment Generative Adversarial Network (MSGAN), for visual sentiment classification. |
| 320 | Learning Graph Convolutional Network for Skeleton-Based Human Action Recognition by Neural Searching | Wei Peng, Xiaopeng Hong, Haoyu Chen, Guoying Zhao | Besides, we introduce multiple-hop modules and expect to break the limitation of representational capacity caused by one-order approximation. |
| 321 | UCF-STAR: A Large Scale Still Image Dataset for Understanding Human Actions | Marjaneh Safaei, Pooyan Balouchian, Hassan Foroosh | To benchmark and demonstrate the benefits of UCF-STAR as a large-scale dataset, and to show the role of “latent” motion information in recognizing human actions in still images, we present a novel approach relying on predicting temporal information, yielding higher accuracy on 5 widely-used datasets. To address the first challenge, we introduce a dataset for STill image Action Recognition (STAR), containing over $1M$ images across 50 different human body-motion action categories. |
| 322 | Towards Socially Responsible AI: Cognitive Bias-Aware Multi-Objective Learning | Procheta Sen, Debasis Ganguly | To alleviate this problem, we propose a bias-aware multi-objective learning framework that given a set of identity attributes (e.g. gender, ethnicity etc.) and a subset of sensitive categories of the possible classes of prediction outputs, learns to reduce the frequency of predicting certain combinations of them, e.g. predicting stereotypes such as ‘most blacks use abusive language’, or ‘fear is a virtue of women’. |
| 323 | Reinforcing an Image Caption Generator Using Off-Line Human Feedback | Paul Hongsuck Seo, Piyush Sharma, Tomer Levinboim, Bohyung Han, Radu Soricut | In this paper, we show that the signal from instance-level human caption ratings can be leveraged to improve captioning models, even when the amount of caption ratings is several orders of magnitude less than the caption training data. |
| 324 | Instance-Adaptive Graph for EEG Emotion Recognition | Tengfei Song, Suyuan Liu, Wenming Zheng, Yuan Zong, Zhen Cui | To tackle the individual differences and characterize the dynamic relationships among different EEG regions for EEG emotion recognition, in this paper, we propose a novel instance-adaptive graph method (IAG), which employs a more flexible way to construct graphic connections so as to present different graphic representations determined by different input instances. |
| 325 | Variational Pathway Reasoning for EEG Emotion Recognition | Tong Zhang, Zhen Cui, Chunyan Xu, Wenming Zheng, Jian Yang | Deeply inspired by this mechanism, we propose a heuristic Variational Pathway Reasoning (VPR) method to deal with EEG-based emotion recognition. |
| 326 | Crowd-Assisted Disaster Scene Assessment with Human-AI Interactive Attention | Daniel (Yue) Zhang, Yifeng Huang, Yang Zhang, Dong Wang | In this paper, we develop an interactive Disaster Scene Assessment (iDSA) scheme that allows AI algorithms to directly interact with humans to identify the salient regions of the disaster images in DSA applications. |
| 327 | BAR — A Reinforcement Learning Agent for Bounding-Box Automated Refinement | Morgane Ayle, Jimmy Tekli, Julia El-Zini, Boulos El-Asmar, Mariette Awad | In this work, we introduce BAR (Bounding-box Automated Refinement), a reinforcement learning agent that learns to correct inaccurate bounding-boxes that are weakly generated by certain detection methods, or wrongly annotated by a human, using either an offline training method with Deep Reinforcement Learning (BAR-DRL), or an online one using Contextual Bandits (BAR-CB). |
| 328 | Cost-Accuracy Aware Adaptive Labeling for Active Learning | Ruijiang Gao, Maytal Saar-Tsechansky | In this paper, we propose a new algorithm for selecting instances, labelers (and their corresponding costs and labeling accuracies), that employs generalization bound of learning with label noise to select informative instances and labelers so as to achieve higher generalization accuracy at a lower cost. |
| 329 | HirePeer: Impartial Peer-Assessed Hiring at Scale in Expert Crowdsourcing Markets | Yasmine Kotturi, Anson Kahng, Ariel Procaccia, Chinmay Kulkarni | This paper presents HirePeer, a novel alternative approach to hiring at scale that leverages peer assessment to elicit honest assessments of fellow workers’ job application materials, which it then aggregates using an impartial ranking algorithm. |
| 330 | Fine-Grained Machine Teaching with Attention Modeling | Jiacheng Liu, Xiaofeng Hou, Feilong Tang | In this work, we propose a new machine teaching framework called Attentive Machine Teaching (AMT). |
| 331 | Learning and Reasoning for Robot Sequential Decision Making under Uncertainty | Saeid Amiri, Mohammad Shokrolah Shirazi, Shiqi Zhang | The key contribution of this work is a robot sdm framework, called lcorpp, that supports the simultaneous capabilities of supervised learning for passive state estimation, automated reasoning with declarative human knowledge, and planning under uncertainty toward achieving long-term goals. |
| 332 | Query Rewriting for Ontology-Mediated Conditional Answers | Medina Andresel, Magdalena Ortiz, Mantas Simkus | We propose an extension of OMQs that allows us to make certain assumptions—for example, about parts of the data that may be unavailable at query time, or costly to query—and retrieve conditional answers, that is, tuples that become certain query answers when the assumptions hold. |
| 333 | Revisiting the Foundations of Abstract Argumentation – Semantics Based on Weak Admissibility and Weak Defense | Ringo Baumann, Gerhard Brewka, Markus Ulbricht | After showing that these key concepts are compatible as in the classical case we introduce new versions of the classical Dung-style semantics including complete, preferred and grounded semantics. |
| 334 | Forgetting an Argument | Ringo Baumann, Dov Gabbay, Odinaldo Rodrigues | In this paper, we convey the idea of forgetting to another major AI formalism, namely Dung-style argumentation frameworks. |
| 335 | Checking Chase Termination over Ontologies of Existential Rules with Equality | David Carral, Jacopo Urbani | We empirically show that this solution is not efficient in practice and propose an alternative approach. |
| 336 | Model-Based Diagnosis with Uncertain Observations | Dean Cazes, Meir Kalech | In this work, we explore how to address the case where there is uncertainty over a given observation. |
| 337 | ParamE: Regarding Neural Network Parameters as Relation Embeddings for Knowledge Graph Completion | Feihu Che, Dawei Zhang, Jianhua Tao, Mingyue Niu, Bocheng Zhao | In this paper, we propose a new knowledge graph embedding model called ParamE which can utilize the two advantages together. |
| 338 | Answering Conjunctive Queries with Inequalities in <em>DL-Lite</em><sub>ℛ</sub> | Gianluca Cima, Maurizio Lenzerini, Antonella Poggi | In the context of the Description Logic DL-Liteℛ≠, i.e., DL-Liteℛ without UNA and with inequality axioms, we address the problem of adding to unions of conjunctive queries (UCQs) one of the simplest forms of negation, namely, inequality. |
| 339 | Epistemic Integrity Constraints for Ontology-Based Data Management | Marco Console, Maurizio Lenzerini | In this paper, we establish a novel framework for integrity constraints in the OBDM scenarios, based on the notion of knowledge state of the information system. |
| 340 | Hypothetical Answers to Continuous Queries over Data Streams | Luís Cruz-Filipe, Isabel Nunes, Graça Gaspar | In this paper we present a semantics for queries and corresponding answers that covers such hypothetical answers, together with an online algorithm for updating the set of facts that are consistent with the currently available information. |
| 341 | ElGolog: A High-Level Programming Language with Memory of the Execution History | Giuseppe De Giacomo, Yves Lespérance, Eugenia Ternovska | In this paper, drawing inspiration from McCarthy’s Elephant 2000, we propose an extended version of Golog, called ElGolog, that supports rich tests about the execution history, where tests are expressed in a first-order variant of two-way linear dynamic logic that uses ElGolog programs with converse. |
| 342 | Efficient Model-Based Diagnosis of Sequential Circuits | Alexander Feldman, Ingo Pill, Franza Wotawa, Ion Matei, Johan de Kleer | In this paper we introduce Finite Trace Next Logic (FTNL) models of sequential circuits and propose an enhanced algorithm for computing minimal-cardinality diagnoses. |
| 343 | Proportional Belief Merging | Adrian Haret, Martin Lackner, Andreas Pfandler, Johannes P. Wallner | In this paper we introduce proportionality to belief merging. |
| 344 | Structural Decompositions of Epistemic Logic Programs | Markus Hecher, Michael Morak, Stefan Woltran | In this paper, we give first results in this direction and show that central ELP problems can be solved in linear time for ELPs exhibiting structural properties in terms of bounded treewidth. |
| 345 | Going Deep: Graph Convolutional Ladder-Shape Networks | Ruiqi Hu, Shirui Pan, Guodong Long, Qinghua Lu, Liming Zhu, Jing Jiang | To solve this problem, we present graph convolutional ladder-shape networks (GCLN), a novel graph neural network architecture that transmits messages from shallow layers to deeper layers to overcome the over-smoothing problem and dramatically extend the scale of the neural networks with improved performance. |
| 346 | Aggregation of Perspectives Using the Constellations Approach to Probabilistic Argumentation | Anthony Hunter, Kawsar Noor | In this paper, we consider how we can construct this probability distribution from data. |
| 347 | Least General Generalizations in Description Logic: Verification and Existence | Jean Christoph Jung, Carsten Lutz, Frank Wolter | To obtain results in the presence of a TBox, we establish a close link between the problems studied in this paper and concept learning from positive and negative examples. |
| 348 | Complexity and Expressive Power of Disjunction and Negation in Limit Datalog | Mark Kaminski, Bernardo Cuenca Grau, Egor V. Kostylev, Ian Horrocks | In this paper, we study the complexity and expressive power of limit Datalog programs extended with disjunction in the heads of rules and non-monotonic negation under the stable model semantics. |
| 349 | Logics for Sizes with Union or Intersection | Caleb Kisby, Saul Blanco, Alex Kruckman, Lawrence Moss | This paper presents the most basic logics for reasoning about the sizes of sets that admit either the union of terms or the intersection of terms. |
| 350 | FastLAS: Scalable Inductive Logic Programming Incorporating Domain-Specific Optimisation Criteria | Mark Law, Alessandra Russo, Elisa Bertino, Krysia Broda, Jorge Lobo | This paper presents a new general notion of a scoring function over hypotheses that allows a user to express domain-specific optimisation criteria. |
| 351 | Automatic Verification of Liveness Properties in the Situation Calculus | Jian Li, Yongmei Liu | In this paper, we consider the following theorem-proving problem: given an action theory and a goal, check whether the goal is achievable in every model of the action theory. |
| 352 | Path Ranking with Attention to Type Hierarchies | Weiyu Liu, Angel Daruna, Zsolt Kira, Sonia Chernova | We introduce Attentive Path Ranking, a novel path pattern representation that leverages type hierarchies of entities to both avoid ambiguity and maintain generalization. |
| 353 | K-BERT: Enabling Language Representation with Knowledge Graph | Weijie Liu, Peng Zhou, Zhe Zhao, Zhiruo Wang, Qi Ju, Haotang Deng, Ping Wang | For machines to achieve this capability, we propose a knowledge-enabled language representation model (K-BERT) with knowledge graphs (KGs), in which triples are injected into the sentences as domain knowledge. |
| 354 | Explanations for Inconsistency-Tolerant Query Answering under Existential Rules | Thomas Lukasiewicz, Enrico Malizia, Cristian Molinaro | In this paper, we address the problem of explaining query answers for existential rules under three popular inconsistency-tolerant semantics, namely, the ABox repair, the intersection of repairs, and the intersection of closed repairs semantics. |
| 355 | Resilient Logic Programs: Answer Set Programs Challenged by Ontologies | Sanja Lukumbuzya, Magdalena Ortiz, Mantas šimkus | We introduce resilient logic programs (RLPs) that couple a non-monotonic logic program and a first-order (FO) theory or description logic (DL) ontology. |
| 356 | Commonsense Knowledge Base Completion with Structural and Semantic Context | Chaitanya Malaviya, Chandra Bhagavatula, Antoine Bosselut, Yejin Choi | In this paper, we present novel KB completion models that can address these challenges by exploiting the structural and semantic context of nodes. |
| 357 | Blameworthiness in Security Games | Pavel Naumov, Jia Tao | The paper defines blameworthiness of the defender and the attacker in security games using the principle of alternative possibilities and provides a sound and complete logical system for reasoning about blameworthiness in such games. |
| 358 | Deciding Acceptance in Incomplete Argumentation Frameworks | Andreas Niskanen, Daniel Neugebauer, Matti Järvisalo, Jörg Rothe | We address this current shortcoming by developing algorithms for NP-hard and coNP-hard variants of acceptance problems over incomplete AFs via harnessing Boolean satisfiability (SAT) solvers. |
| 359 | Rule-Guided Compositional Representation Learning on Knowledge Graphs | Guanglin Niu, Yongfei Zhang, Bo Li, Peng Cui, Si Liu, Jingyang Li, Xiaowei Zhang | In this paper, we propose a novel Rule and Path-based Joint Embedding (RPJE) scheme, which takes full advantage of the explainability and accuracy of logic rules, the generalization of KG embedding as well as the supplementary semantic structure of paths. |
| 360 | Learning Query Inseparable εℒℋ Ontologies | Ana Ozaki, Cosimo Persia, Andrea Mazzullo | We investigate the complexity of learning query inseparable εℒℋ ontologies in a variant of Angluin’s exact learning model. |
| 361 | Graph Representations for Higher-Order Logic and Theorem Proving | Aditya Paliwal, Sarah Loos, Markus Rabe, Kshitij Bansal, Christian Szegedy | In this paper, we consider several graphical representations of higher-order logic and evaluate them against the HOList benchmark for higher-order theorem proving. |
| 362 | Relatedness and TBox-Driven Rule Learning in Large Knowledge Bases | Giuseppe Pirrò | We present RARL, an approach to discover rules of the form body ⇒ head in large knowledge bases (KBs) that typically include a set of terminological facts (TBox) and a set of TBox-compliant assertional facts (ABox). |
| 363 | A Framework for Measuring Information Asymmetry | Yakoub Salhi | We propose in this work a general logic-based framework for measuring the information asymmetry between two parties. |
| 364 | Adversarial Deep Network Embedding for Cross-Network Node Classification | Xiao Shen, Quanyu Dai, Fu-lai Chung, Wei Lu, Kup-Sze Choi | In this paper, the task of cross-network node classification, which leverages the abundant labeled nodes from a source network to help classify unlabeled nodes in a target network, is studied. |
| 365 | Contextual Parameter Generation for Knowledge Graph Link Prediction | George Stoica, Otilia Stretcu, Emmanouil Antonios Platanios, Tom Mitchell, Barnabás Póczos | We propose to use contextual parameter generation to address this limitation. |
| 366 | InteractE: Improving Convolution-Based Knowledge Graph Embeddings by Increasing Feature Interactions | Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, Nilesh Agrawal, Partha Talukdar | In this paper, we analyze how increasing the number of these interactions affects link prediction performance, and utilize our observations to propose InteractE. |
| 367 | Query Answering with Guarded Existential Rules under Stable Model Semantics | Hai Wan, Guohui Xiao, Chenglin Wang, Xianqiao Liu, Junhong Chen, Zhe Wang | In this paper, we study the problem of query answering with guarded existential rules (also called GNTGDs) under stable model semantics. |
| 368 | COTSAE: CO-Training of Structure and Attribute Embeddings for Entity Alignment | Kai Yang, Shaoqin Liu, Junfeng Zhao, Yasha Wang, Bing Xie | To solve these problems, we propose COTSAE that combines the structure and attribute information of entities by co-training two embedding learning components, respectively. |
| 369 | Ranking-Based Semantics for Sets of Attacking Arguments | Bruno Yun, Srdjan Vesic, Madalina Croitoru | In this paper, we study a more general case when sets of arguments can jointly attack an argument. |
| 370 | Few-Shot Knowledge Graph Completion | Chuxu Zhang, Huaxiu Yao, Chao Huang, Meng Jiang, Zhenhui Li, Nitesh V. Chawla | In this work, we propose a novel few-shot relation learning model (FSRL) that aims at discovering facts of new relations with few-shot references. |
| 371 | Towards Universal Languages for Tractable Ontology Mediated Query Answering | Heng Zhang, Yan Zhang, Jia-Huai You, Zhiyong Feng, Guifei Jiang | In this paper, we focus on three families of tractable OMQA-languages, including first-order rewritable languages and languages whose data complexity of the query answering is in AC0 or PTIME. |
| 372 | On the Expressivity of ASK Queries in SPARQL | Xiaowang Zhang, Jan Van den Bussche, Kewen Wang, Heng Zhang, Xuanxing Yang, Zhiyong Feng | The work in this paper provides a guideline for future SPARQL query optimization and implementation. |
| 373 | Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction | Zhanqiu Zhang, Jianyu Cai, Yongdong Zhang, Jie Wang | To address this challenge, we propose a novel knowledge graph embedding model—namely, Hierarchy-Aware Knowledge Graph Embedding (HAKE)—which maps entities into the polar coordinate system. |
| 374 | A Practical Approach to Forgetting in Description Logics with Nominals | Yizheng Zhao, Renate Schmidt, Yuejie Wang, Xuanming Zhang, Hao Feng | In particular, we develop a practical method for forgetting concept and role names from ontologies specified in the description logic ALCO, extending the basic ALC with nominals. |
| 375 | Deciding the Loosely Guarded Fragment and Querying Its Horn Fragment Using Resolution | Sen Zheng, Renate Schmidt | In this paper, we present a resolution decision procedure for the loosely guarded fragment, and use such a procedure to answer Boolean conjunctive queries against the Horn loosely guarded fragment. |
| 376 | LTLƒ Synthesis with Fairness and Stability Assumptions | Shufang Zhu, Giuseppe De Giacomo, Geguang Pu, Moshe Y. Vardi | In this work we show that in interesting cases we can avoid such a detour to LTL synthesis and keep the simplicity of LTLƒ synthesis. |
| 377 | Learning to Reason: Leveraging Neural Networks for Approximate DNF Counting | Ralph Abboud, Ismail Ceylan, Thomas Lukasiewicz | In this paper, we propose a neural model counting approach for weighted #DNF that combines approximate model counting with deep learning, and accurately approximates model counts in linear time when width is bounded. |
| 378 | Quantized Compressive Sampling of Stochastic Gradients for Efficient Communication in Distributed Deep Learning | Afshin Abdi, Faramarz Fekri | We propose Quantized Compressive Sampling (QCS) of SG that addresses the above two issues while achieving an arbitrarily large compression gain. |
| 379 | Indirect Stochastic Gradient Quantization and Its Application in Distributed Deep Learning | Afshin Abdi, Faramarz Fekri | We have proposed and theoretically analyzed different indirect SG quantization (ISGQ) methods. |
| 380 | Image-Adaptive GAN Based Reconstruction | Shady Abu Hussein, Tom Tirer, Raja Giryes | In this paper, we suggest to mitigate the limited representation capabilities of generators by making them image-adaptive and enforcing compliance of the restoration with the observations via back-projections. |
| 381 | DeGAN: Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier | Sravanti Addepalli, Gaurav Kumar Nayak, Anirban Chakraborty, Venkatesh Babu Radhakrishnan | We propose to bridge the gap between the abundance of available data and lack of relevant data, for the future learning tasks of a given trained network. |
| 382 | Bounds and Complexity Results for Learning Coalition-Based Interaction Functions in Networked Social Systems | Abhijin Adiga, Chris Kuhlman, Madhav Marathe, S. Ravi, Daniel Rosenkranz, Richard Stearns, Anil Vullikanti | Using a discrete dynamical system model for a networked social system, we consider the problem of learning a class of local interaction functions in such networks. |
| 383 | Learning Optimal Decision Trees Using Caching Branch-and-Bound Search | Gaël Aglin, Siegfried Nijssen, Pierre Schaus | In this paper, we introduce a new efficient algorithm, DL8.5, for finding optimal decision trees, based on the use of itemset mining techniques. |
| 384 | Detecting Semantic Anomalies | Faruk Ahmed, Aaron Courville | We critically appraise the recent interest in out-of-distribution (OOD) detection and question the practical relevance of existing benchmarks. |
| 385 | Exact and Efficient Inference for Collective Flow Diffusion Model via Minimum Convex Cost Flow Algorithm | Yasunori Akagi, Takuya Nishimura, Yusuke Tanaka, Takeshi Kurashima, Hiroyuki Toda | In this paper, we propose an exact and efficient method for MAP inference in CFDM. |
| 386 | Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise | Albert Akhriev, Jakub Marecek, Andrea Simonetto | In tracking of time-varying low-rank models of time-varying matrices, we present a method robust to both uniformly-distributed measurement noise and arbitrarily-distributed “sparse” noise. |
| 387 | An Implicit Form of Krasulina’s k-PCA Update without the Orthonormality Constraint | Ehsan Amid, Manfred K. Warmuth | We shed new insights on the two commonly used updates for the online k-PCA problem, namely, Krasulina’s and Oja’s updates. |
| 388 | Kriging Convolutional Networks | Gabriel Appleby, Linfeng Liu, Li-Ping Liu | Inspired by the recent progress of graph neural networks, we introduce Kriging Convolutional Networks (KCN), a method of combining advantages of Graph Neural Networks (GNN) and kriging. |
| 389 | Efficient Inference of Optimal Decision Trees | Florent Avellaneda | In this paper, we propose a novel approach for inferring an optimal decision tree with a minimum depth based on the incremental generation of Boolean formulas. |
| 390 | Few Shot Network Compression via Cross Distillation | Haoli Bai, Jiaxiang Wu, Irwin King, Michael Lyu | To address the problem, we propose cross distillation, a novel layer-wise knowledge distillation approach. |
| 391 | A Three-Level Optimization Model for Nonlinearly Separable Clustering | Liang Bai, Jiye Liang | To get rid of the deficiency, we propose a three-level optimization model for nonlinearly separable clustering which divides the clustering problem into three sub-problems: a linearly separable clustering on the object set, a nonlinearly separable clustering on the cluster set and an ensemble clustering on the partition set. |
| 392 | Learning-Based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching | Yunsheng Bai, Hao Ding, Ken Gu, Yizhou Sun, Wei Wang | In this paper, we address the problem of graph similarity computation from another perspective, by directly matching two sets of node embeddings without the need to use fixed-dimensional vectors to represent whole graphs for their similarity computation. |
| 393 | Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees | Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik | This data-independent discretization, however, might miss pockets of nearly-optimal parameters: prior research has presented scenarios where the only viable parameters lie within an arbitrarily small region. |
| 394 | Scalable Attentive Sentence Pair Modeling via Distilled Sentence Embedding | Oren Barkan, Noam Razin, Itzik Malkiel, Ori Katz, Avi Caciularu, Noam Koenigstein | In this paper, we introduce Distilled Sentence Embedding (DSE) – a model that is based on knowledge distillation from cross-attentive models, focusing on sentence-pair tasks. |
| 395 | Midas: Microcluster-Based Detector of Anomalies in Edge Streams | Siddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin, Christos Faloutsos | In this work, we propose Midas, which focuses on detecting microcluster anomalies, or suddenly arriving groups of suspiciously similar edges, such as lockstep behavior, including denial of service attacks in network traffic data. |
| 396 | Exploratory Combinatorial Optimization with Reinforcement Learning | Thomas Barrett, William Clements, Jakob Foerster, Alex Lvovsky | We instead propose that the agent should seek to continuously improve the solution by learning to explore at test time. Previous works construct the solution subset incrementally, adding one element at a time, however, the irreversible nature of this approach prevents the agent from revising its earlier decisions, which may be necessary given the complexity of the optimization task. |
| 397 | Event-Driven Continuous Time Bayesian Networks | Debarun Bhattacharjya, Karthikeyan Shanmugam, Tian Gao, Nicholas Mattei, Kush Varshney, Dharmashankar Subramanian | We introduce a novel event-driven continuous time Bayesian network (ECTBN) representation to model situations where a system’s state variables could be influenced by occurrences of events of various types. |
| 398 | An Efficient Evolutionary Algorithm for Subset Selection with General Cost Constraints | Chao Bian, Chao Feng, Chao Qian, Yang Yu | In this paper, we study the problem of selecting a subset from a ground set to maximize a monotone objective function f such that a monotone cost function c is bounded by an upper limit. |
| 399 | A Stochastic Derivative-Free Optimization Method with Importance Sampling: Theory and Learning to Control | Adel Bibi, El Houcine Bergou, Ozan Sener, Bernard Ghanem, Peter Richtarik | In this paper, we propose the first derivative free optimization method with importance sampling and derive new improved complexity results on non-convex, convex and strongly convex functions. |
| 400 | Proximal Distilled Evolutionary Reinforcement Learning | Cristian Bodnar, Ben Day, Pietro Lió | We propose a novel algorithm called Proximal Distilled Evolutionary Reinforcement Learning (PDERL) that is characterised by a hierarchical integration between evolution and learning. |
| 401 | Efficient Verification of ReLU-Based Neural Networks via Dependency Analysis | Elena Botoeva, Panagiotis Kouvaros, Jan Kronqvist, Alessio Lomuscio, Ruth Misener | We introduce an efficient method for the verification of ReLU-based feed-forward neural networks. |
| 402 | Information-Theoretic Understanding of Population Risk Improvement with Model Compression | Yuheng Bu, Weihao Gao, Shaofeng Zou, Venugopal Veeravalli | We show that model compression can improve the population risk of a pre-trained model, by studying the tradeoff between the decrease in the generalization error and the increase in the empirical risk with model compression. |
| 403 | A Multi-Scale Approach for Graph Link Prediction | Lei Cai, Shuiwang Ji | In this work, we propose a novel node aggregation method that can transform the enclosing subgraph into different scales and preserve the relationship between two target nodes for link prediction. |
| 404 | Deterministic Value-Policy Gradients | Qingpeng Cai, Ling Pan, Pingzhong Tang | In this paper we consider the deterministic value gradients to improve the sample efficiency of deep reinforcement learning algorithms. |
| 405 | Predicting Propositional Satisfiability via End-to-End Learning | Chris Cameron, Rex Chen, Jason Hartford, Kevin Leyton-Brown | Our work leverages deep network models which capture a key invariance exhibited by SAT problems: satisfiability status is unaffected by reordering variables and clauses. |
| 406 | Active Ordinal Querying for Tuplewise Similarity Learning | Gregory Canal, Stefano Fenu, Christopher Rozell | This work generalizes triplet queries to tuple queries of arbitrary size that ask an oracle to rank multiple objects against a reference, and introduces an efficient and robust adaptive selection method called InfoTuple that uses a novel approach to mutual information maximization. |
| 407 | Fatigue-Aware Bandits for Dependent Click Models | Junyu Cao, Wei Sun, Zuo-Jun (Max) Shen, Markus Ettl | We propose an extension of the Dependent Click Model (DCM) to describe users’ behavior. |
| 408 | Generalization Error Bounds of Gradient Descent for Learning Over-Parameterized Deep ReLU Networks | Yuan Cao, Quanquan Gu | In this work, we derive an algorithm-dependent generalization error bound for deep ReLU networks, and show that under certain assumptions on the data distribution, gradient descent (GD) with proper random initialization is able to train a sufficiently over-parameterized DNN to achieve arbitrarily small generalization error. |
| 409 | Exponential Family Graph Embeddings | Abdulkadir Celikkanat, Fragkiskos D. Malliaros | In this paper, we emphasize on exponential family distributions to capture rich interaction patterns between nodes in random walk sequences. |
| 410 | Asking the Right Questions to the Right Users: Active Learning with Imperfect Oracles | Shayok Chakraborty | In this paper, we propose a novel framework to address the challenging problem of active learning in the presence of multiple imperfect oracles. |
| 411 | Lifelong Learning with a Changing Action Set | Yash Chandak, Georgios Theocharous, Chris Nota, Philip Thomas | In this paper, we present first steps towards developing an algorithm that autonomously adapts to an action set whose size changes over time. |
| 412 | Reinforcement Learning When All Actions Are Not Always Available | Yash Chandak, Georgios Theocharous, Blossom Metevier, Philip Thomas | In this paper we argue that existing RL algorithms for SAS-MDPs can suffer from potential divergence issues, and present new policy gradient algorithms for SAS-MDPs that incorporate variance reduction techniques unique to this setting, and provide conditions for their convergence. |
| 413 | A Restricted Black-Box Adversarial Framework Towards Attacking Graph Embedding Models | Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Wenwu Zhu, Junzhou Huang | To validate the generalization of GF-Attack, we construct the attacker on four popular graph embedding models. |
| 414 | Robust Data Programming with Precision-guided Labeling Functions | Oishik Chatterjee, Ganesh Ramakrishnan, Sunita Sarawagi | We propose an elegant method of incorporating these guides into the generative model. |
| 415 | A New Ensemble Adversarial Attack Powered by Long-Term Gradient Memories | Zhaohui Che, Ali Borji, Guangtao Zhai, Suiyi Ling, Jing Li, Patrick Le Callet | In this paper, we propose a novel black-box attack, dubbed Serial-Mini-Batch-Ensemble-Attack (SMBEA). |
| 416 | Toward A Thousand Lights: Decentralized Deep Reinforcement Learning for Large-Scale Traffic Signal Control | Chacha Chen, Hua Wei, Nan Xu, Guanjie Zheng, Ming Yang, Yuanhao Xiong, Kai Xu, Zhenhui Li | In this paper, we tackle the problem of multi-intersection traffic signal control, especially for large-scale networks, based on RL techniques and transportation theories. |
| 417 | HoMM: Higher-Order Moment Matching for Unsupervised Domain Adaptation | Chao Chen, Zhihang Fu, Zhihong Chen, Sheng Jin, Zhaowei Cheng, Xinyu Jin, Xian-sheng Hua | From the perspective of moment matching, most existing discrepancy-based methods are designed to match the second-order or lower moments, which however, have limited expression of statistical characteristic for non-Gaussian distributions. |
| 418 | Online Knowledge Distillation with Diverse Peers | Defang Chen, Jian-Ping Mei, Can Wang, Yan Feng, Chun Chen | In this work, we propose Online Knowledge Distillation with Diverse peers (OKDDip), which performs two-level distillation during training with multiple auxiliary peers and one group leader. |
| 419 | Measuring and Relieving the Over-Smoothing Problem for Graph Neural Networks from the Topological View | Deli Chen, Yankai Lin, Wei Li, Peng Li, Jie Zhou, Xu Sun | In this work, we present a systematic and quantitative study on the over-smoothing issue of GNNs. |
| 420 | ECGadv: Generating Adversarial Electrocardiogram to Misguide Arrhythmia Classification System | Huangxun Chen, Chenyu Huang, Qianyi Huang, Qian Zhang, Wei Wang | Thus, this paper takes a step to thoroughly explore adversarial attacks on the DNN-powered ECG diagnosis system. |
| 421 | LS-Tree: Model Interpretation When the Data Are Linguistic | Jianbo Chen, Michael Jordan | Leveraging a parse tree, we propose to assign least-squares-based importance scores to each word of an instance by exploiting syntactic constituency structure. |
| 422 | Generative Adversarial Networks for Video-to-Video Domain Adaptation | Jiawei Chen, Yuexiang Li, Kai Ma, Yefeng Zheng | In this work, we propose a novel generative adversarial network (GAN), namely VideoGAN, to transfer the video-based data across different domains. |
| 423 | Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback | Jiawei Chen, Can Wang, Sheng Zhou, Qihao Shi, Jingbang Chen, Yan Feng, Chun Chen | To achieve both adaptive weights assignment and efficient model learning, we propose a fast adaptively weighted matrix factorization (FAWMF) based on variational auto-encoder. |
| 424 | Variational Metric Scaling for Metric-Based Meta-Learning | Jiaxin Chen, Li-ming Zhan, Xiao-Ming Wu, Fu-lai Chung | In this paper, we recast metric-based meta-learning from a Bayesian perspective and develop a variational metric scaling framework for learning a proper metric scaling parameter. |
| 425 | A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks | Jinghui Chen, Dongruo Zhou, Jinfeng Yi, Quanquan Gu | In this paper, we focus on the problem of developing efficient and effective optimization-based adversarial attack algorithms. |
| 426 | Weakly Supervised Disentanglement by Pairwise Similarities | Junxiang Chen, Kayhan Batmanghelich | We propose a new method for weakly supervised disentanglement of latent variables within the framework of Variational Autoencoder. |
| 427 | Outlier Detection Ensemble with Embedded Feature Selection | Li Cheng, Yijie Wang, Xinwang Liu, Bin Li | In this paper, we propose an outlier detection ensemble framework with embedded feature selection (ODEFS), to address this issue. |
| 428 | Multi-View Clustering in Latent Embedding Space | Man-Sheng Chen, Ling Huang, Chang-Dong Wang, Dong Huang | In light of this, this paper proposes a novel approach termed Multi-view Clustering in Latent Embedding Space (MCLES), which is able to cluster the multi-view data in a learned latent embedding space while simultaneously learning the global structure and the cluster indicator matrix in a unified optimization framework. |
| 429 | Adversarial-Learned Loss for Domain Adaptation | Minghao Chen, Shuai Zhao, Haifeng Liu, Deng Cai | In order to combine the strengths of these two methods, we propose a novel method called Adversarial-Learned Loss for Domain Adaptation (ALDA). |
| 430 | Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting | Weiqi Chen, Ling Chen, Yu Xie, Wei Cao, Yusong Gao, Xiaojie Feng | In this paper, we propose the Multi-Range Attentive Bicomponent GCN (MRA-BGCN), a novel deep learning model for traffic forecasting. |
| 431 | AutoDAL: Distributed Active Learning with Automatic Hyperparameter Selection | Xu Chen, Brett Wujek | With this as motivation, we propose a novel automated learning system for distributed active learning (AutoDAL) to address these challenges. |
| 432 | Optimal Attack against Autoregressive Models by Manipulating the Environment | Yiding Chen, Xiaojin Zhu | We describe an optimal adversarial attack formulation against autoregressive time series forecast using Linear Quadratic Regulator (LQR). |
| 433 | Multi-View Partial Multi-Label Learning with Graph-Based Disambiguation | Ze-Sen Chen, Xuan Wu, Qing-Guo Chen, Yao Hu, Min-Ling Zhang | In this paper, the problem of multi-view partial multi-label learning (MVPML) is studied, where the set of associated labels are assumed to be candidate ones and only partially valid. |
| 434 | Compressed Self-Attention for Deep Metric Learning | Ziye Chen, Mingming Gong, Yanwu Xu, Chaohui Wang, Kun Zhang, Bo Du | In this paper, we aim to enhance self-attention (SA) mechanism for deep metric learning in visual perception, by capturing richer contextual dependencies in visual data. |
| 435 | Semi-Supervised Learning under Class Distribution Mismatch | Yanbei Chen, Xiatian Zhu, Wei Li, Shaogang Gong | Whilst demonstrating impressive performance boost, existing SSL methods artificially assume that small labelled data and large unlabelled data are drawn from the same class distribution. |
| 436 | InstaNAS: Instance-Aware Neural Architecture Search | An-Chieh Cheng, Chieh Hubert Lin, Da-Cheng Juan, Wei Wei, Min Sun | In this paper, we propose InstaNAS—an instance-aware NAS framework—that employs a controller trained to search for a “distribution of architectures” instead of a single architecture; This allows the model to use sophisticated architectures for the difficult samples, which usually comes with large architecture related cost, and shallow architectures for those easy samples. |
| 437 | Distilling Portable Generative Adversarial Networks for Image Translation | Hanting Chen, Yunhe Wang, Han Shu, Changyuan Wen, Chunjing Xu, Boxin Shi, Chao Xu, Chang Xu | To promote the capability of student generator, we include a student discriminator to measure the distances between real images, and images generated by student and teacher generators. |
| 438 | Towards Better Forecasting by Fusing Near and Distant Future Visions | Jiezhu Cheng, Kaizhu Huang, Zibin Zheng | To address this problem, we propose Multi-Level Construal Neural Network (MLCNN), a novel multi-task deep learning framework. |
| 439 | Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples | Minhao Cheng, Jinfeng Yi, Pin-Yu Chen, Huan Zhang, Cho-Jui Hsieh | In this paper, we study the much more challenging problem of crafting adversarial examples for sequence-to-sequence (seq2seq) models, whose inputs are discrete text strings and outputs have an almost infinite number of possibilities. |
| 440 | Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions | Weiyu Cheng, Yanyan Shen, Linpeng Huang | In this work, we propose the Adaptive Factorization Network (AFN), a new model that learns arbitrary-order cross features adaptively from data. |
| 441 | Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets | Ziqiang Cheng, Yang Yang, Wei Wang, Wenjie Hu, Yueting Zhuang, Guojie Song | In this paper, we propose to extract time-aware shapelets by designing a two-level timing factor. |
| 442 | Suspicion-Free Adversarial Attacks on Clustering Algorithms | Anshuman Chhabra, Abhishek Roy, Prasant Mohapatra | In this paper, we seek to bridge this gap by proposing a black-box adversarial attack for clustering models for linearly separable clusters. |
| 443 | A General Approach to Fairness with Optimal Transport | Chiappa Silvia, Jiang Ray, Stepleton Tom, Pacchiano Aldo, Jiang Heinrich, Aslanides John | We propose a general approach to fairness based on transporting distributions corresponding to different sensitive attributes to a common distribution. |
| 444 | Active Learning in the Geometric Block Model | Eli Chien, Antonia Tulino, Jaime Llorca | In this work, we initiate the study of active learning in the geometric block model. |
| 445 | Deep Mixed Effect Model Using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare | Ingyo Chung, Saehoon Kim, Juho Lee, Kwang Joon Kim, Sung Ju Hwang, Eunho Yang | We present a personalized and reliable prediction model for healthcare, which can provide individually tailored medical services such as diagnosis, disease treatment, and prevention. |
| 446 | A Constraint-Based Approach to Learning and Explanation | Gabriele Ciravegna, Francesco Giannini, Stefano Melacci, Marco Maggini, Marco Gori | In this paper we propose a novel approach to learning of constraints which is based on information theoretic principles. |
| 447 | Representing Closed Transformation Paths in Encoded Network Latent Space | Marissa Connor, Christopher Rozell | In this work, we incorporate a generative manifold model into the latent space of an autoencoder in order to learn the low-dimensional manifold structure from the data and adapt the latent space to accommodate this structure. |
| 448 | Forgetting to Learn Logic Programs | Andrew Cropper | We introduce Forgetgol, a multi-task ILP learner which supports forgetting. |
| 449 | Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking | Eric Crawford, Joelle Pineau | In the current work, we propose an architecture that scales well to the large-scene, many-object setting by employing spatially invariant computations (convolutions and spatial attention) and representations (a spatially local object specification scheme). |
| 450 | Label Error Correction and Generation through Label Relationships | Zijun Cui, Yong Zhang, Qiang Ji | For this reason, we propose to capture and leverage label relationships at different levels to improve fine-grained label annotation quality and to generate labels. |
| 451 | A Tale of Two-Timescale Reinforcement Learning with the Tightest Finite-Time Bound | Gal Dalal, Balazs Szorenyi, Gugan Thoppe | Here, we provide convergence rate bounds for this suite of algorithms. |
| 452 | Explainable Data Decompositions | Sebastian Dalleiger, Jilles Vreeken | As the search space is large and unstructured, we propose the deterministic DISC algorithm to efficiently discover high-quality decompositions via an alternating optimization approach. |
| 453 | A Skip-Connected Evolving Recurrent Neural Network for Data Stream Classification under Label Latency Scenario | Monidipa Das, Mahardhika Pratama, Jie Zhang, Yew Soon Ong | We propose SkipE-RNN, a self-evolutionary recurrent neural network with dynamically evolving skipped-recurrent-connection for the best utilization of previously observed label information while classifying the current data. |
| 454 | DNNs as Layers of Cooperating Classifiers | Marelie Davel, Marthinus Theunissen, Arnold Pretorius, Etienne Barnard | We describe how these two systems arise naturally from the gradient-based optimization process, and demonstrate the classification ability of the two systems, individually and in collaboration. |
| 455 | Making Existing Clusterings Fairer: Algorithms, Complexity Results and Insights | Ian Davidson, S.S Ravi | We explore the area of fairness in clustering from the different perspective of modifying clusterings from existing algorithms to make them fairer whilst retaining their quality. |
| 456 | Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning | Kristopher De Asis, Alan Chan, Silviu Pitis, Richard Sutton, Daniel Graves | We explore fixed-horizon temporal difference (TD) methods, reinforcement learning algorithms for a new kind of value function that predicts the sum of rewards over a fixed number of future time steps. |
| 457 | Capsule Routing via Variational Bayes | Fabio De Sousa Ribeiro, Georgios Leontidis, Stefanos Kollias | In this paper, we propose a new capsule routing algorithm derived from Variational Bayes for fitting a mixture of transforming gaussians, and show it is possible transform our capsule network into a Capsule-VAE. |
| 458 | System Identification with Time-Aware Neural Sequence Models | Thomas Demeester | In particular, we introduce a ‘time-aware’ and stationary extension of existing models (including the Gated Recurrent Unit) that allows them to deal with unevenly sampled system observations by adapting to the observation times, while facilitating higher-order temporal behavior. |
| 459 | Reinforcing Neural Network Stability with Attractor Dynamics | Hanming Deng, Yang Hua, Tao Song, Zhengui Xue, Ruhui Ma, Neil Robertson, Haibing Guan | In this paper, we take a step further to be the first to reinforce this stability of DNNs without changing their original structure and verify the impact of the reinforced stability on the network representation from various aspects. |
| 460 | Optimizing Discrete Spaces via Expensive Evaluations: A Learning to Search Framework | Aryan Deshwal, Syrine Belakaria, Janardhan Rao Doppa, Alan Fern | We consider the problem of optimizing expensive black-box functions over discrete spaces (e.g., sets, sequences, graphs). |
| 461 | Integrating Overlapping Datasets Using Bivariate Causal Discovery | Anish Dhir, Ciaran M. Lee | In this work we adapt and extend these so-called bivariate causal discovery algorithms to the problem of learning consistent causal structures from multiple datasets with overlapping variables belonging to the same generating process, providing a sound and complete algorithm that outperforms previous approaches on synthetic and real data. |
| 462 | Improving the Robustness of Wasserstein Embedding by Adversarial PAC-Bayesian Learning | Daizong Ding, Mi Zhang, Xudong Pan, Min Yang, Xiangnan He | Based on this, we propose an algorithm called Adversarial PAC-Bayesian Learning (APBL) in order to minimize the generalization error bound. |
| 463 | Gradient-Aware Model-Based Policy Search | Pierluca D'Oro, Alberto Maria Metelli, Andrea Tirinzoni, Matteo Papini, Marcello Restelli | In this paper, we introduce a novel model-based policy search approach that exploits the knowledge of the current agent policy to learn an approximate transition model, focusing on the portions of the environment that are most relevant for policy improvement. |
| 464 | Fairness in Network Representation by Latent Structural Heterogeneity in Observational Data | Xin Du, Yulong Pei, Wouter Duivesteijn, Mykola Pechenizkiy | In this paper, we argue that latent structural heterogeneity in the observational data could bias the classical network representation model. |
| 465 | On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning | Aritra Dutta, El Houcine Bergou, Ahmed M. Abdelmoniem, Chen-Yu Ho, Atal Narayan Sahu, Marco Canini, Panos Kalnis | In this paper, we prove that layer-wise compression is, in theory, better, because the convergence rate is upper bounded by that of entire-model compression for a wide range of biased and unbiased compression methods. |
| 466 | An Information-Theoretic Quantification of Discrimination with Exempt Features | Sanghamitra Dutta, Praveen Venkatesh, Piotr Mardziel, Anupam Datta, Pulkit Grover | In this work, we propose a novel information-theoretic decomposition of the total discrimination (in a counterfactual sense) into a non-exempt component, which quantifies the part of the discrimination that cannot be accounted for by the critical features, and an exempt component, which quantifies the remaining discrimination. |
| 467 | Unsupervised Metric Learning with Synthetic Examples | Ujjal Kr Dutta, Mehrtash Harandi, C. Chandra Sekhar | In this paper, we address the less-studied problem of learning a metric in an unsupervised manner. |
| 468 | Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning | Jicong Fan, Yuqian Zhang, Madeleine Udell | We develop a new formulation of the resulting problem using the kernel trick together with a new relaxation of the rank objective, and propose an efficient optimization method. |
| 469 | Distributionally Robust Counterfactual Risk Minimization | Louis Faury, Ugo Tanielian, Elvis Dohmatob, Elena Smirnova, Flavian Vasile | This manuscript introduces the idea of using Distributionally Robust Optimization (DRO) for the Counterfactual Risk Minimization (CRM) problem. |
| 470 | Regularized Training and Tight Certification for Randomized Smoothed Classifier with Provable Robustness | Huijie Feng, Chunpeng Wu, Guoyang Chen, Weifeng Zhang, Yang Ning | In this work, we derive a new regularized risk, in which the regularizer can adaptively encourage the accuracy and robustness of the smoothed counterpart when training the base classifier. |
| 471 | Privacy-Preserving Gaussian Process Regression – A Modular Approach to the Application of Homomorphic Encryption | Peter Fenner, Edward Pyzer-Knapp | In this paper, we show that a modular approach, which applies FHE to only the sensitive steps of a workflow that need protection, allows one party to make predictions on their data using a Gaussian process regression model built from another party’s data, without either party gaining access to the other’s data, in a way which is both accurate and efficient. |
| 472 | Learning Triple Embeddings from Knowledge Graphs | Valeria Fionda, Giuseppe Pirrò | The goal of this paper is to introduce Triple2Vec, a new technique to directly embed knowledge graph triples. |
| 473 | Training Decision Trees as Replacement for Convolution Layers | Wolfgang Fuhl, Gjergji Kasneci, Wolfgang Rosenstiel, Enkeljda Kasneci | We present an alternative layer to convolution layers in convolutional neural networks (CNNs). |
| 474 | Induction of Subgoal Automata for Reinforcement Learning | Daniel Furelos-Blanco, Mark Law, Alessandra Russo, Krysia Broda, Anders Jonsson | In this work we present ISA, a novel approach for learning and exploiting subgoals in reinforcement learning (RL). |
| 475 | Fast and Deep Graph Neural Networks | Claudio Gallicchio, Alessio Micheli | Efficiency is gained by many aspects, including the use of small and very sparse networks, where the weights of the recurrent units are left untrained under the stability condition introduced in this work. |
| 476 | On the Parameterized Complexity of Clustering Incomplete Data into Subspaces of Small Rank | Robert Ganian, Iyad Kanj, Sebastian Ordyniak, Stefan Szeider | We consider a fundamental matrix completion problem where we are given an incomplete matrix and a set of constraints modeled as a CSP instance. |
| 477 | Adaptive Convolutional ReLUs | Hongyang Gao, Lei Cai, Shuiwang Ji | In this work, we propose a novel activation function, known as the adaptive convolutional ReLU (ConvReLU), that can better mimic brain neuron activation behaviors and overcome the dying ReLU problem. |
| 478 | Infinity Learning: Learning Markov Chains from Aggregate Steady-State Observations | Jianfei Gao, Mohamed A. Zahran, Amit Sheoran, Sonia Fahmy, Bruno Ribeiro | To overcome this optimization challenge, we propose ∞-SGD, a principled stochastic gradient descent method that uses randomly-stopped estimators to avoid infinite sums required by the steady state computation, while learning even when only a subset of the CTMC states can be observed. |
| 479 | Tensor-SVD Based Graph Learning for Multi-View Subspace Clustering | Quanxue Gao, Wei Xia, Zhizhen Wan, Deyan Xie, Pu Zhang | To improve the robustness and clustering performance, we study the weighted tensor-nuclear norm based on t-SVD and develop an efficient algorithm to optimize the weighted tensor-nuclear norm minimization (WTNNM) problem. |
| 480 | Cross-Modal Subspace Clustering via Deep Canonical Correlation Analysis | Quanxue Gao, Huanhuan Lian, Qianqian Wang, Gan Sun | To tackle this problem, in this paper, we propose an unsupervised framework named Cross-Modal Subspace Clustering via Deep Canonical Correlation Analysis (CMSC-DCCA), which incorporates the correlation constraint with a self-expressive layer to make full use of information among the inter-modal data and the intra-modal data. |
| 481 | A Multi-Channel Neural Graphical Event Model with Negative Evidence | Tian Gao, Dharmashankar Subramanian, Karthikeyan Shanmugam, Debarun Bhattacharjya, Nicholas Mattei | We propose a non-parametric deep neural network approach in order to estimate the underlying intensity functions. |
| 482 | Revisiting Bilinear Pooling: A Coding Perspective | Zhi Gao, Yuwei Wu, Xiaoxun Zhang, Jindou Dai, Yunde Jia, Mehrtash Harandi | In this paper, we prove that bilinear pooling is indeed a similarity-based coding-pooling formulation. |
| 483 | Improved Algorithms for Conservative Exploration in Bandits | Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta | In this paper, we study the conservative learning problem in the contextual linear bandit setting and introduce a novel algorithm, the Conservative Constrained LinUCB (CLUCB2). |
| 484 | Modeling Dialogues with Hashcode Representations: A Nonparametric Approach | Sahil Garg, Irina Rish, Guillermo Cecchi, Palash Goyal, Sarik Ghazarian, Shuyang Gao, Greg Ver Steeg, Aram Galstyan | We propose a novel dialogue modeling framework, the first-ever nonparametric kernel functions based approach for dialogue modeling, which learns hashcodes as text representations; unlike traditional deep learning models, it handles well relatively small datasets, while also scaling to large ones. |
| 485 | Reinforcement Learning with Non-Markovian Rewards | Maor Gaon, Ronen Brafman | We describe and evaluate empirically four combinations of the classical RL algorithm Q-learning and R-max with automata learning algorithms to obtain new RL algorithms for domains with NMR. |
| 486 | Diachronic Embedding for Temporal Knowledge Graph Completion | Rishab Goel, Seyed Mehran Kazemi, Marcus Brubaker, Pascal Poupart | In this paper, we build novel models for temporal KG completion through equipping static models with a diachronic entity embedding function which provides the characteristics of entities at any point in time. |
| 487 | Adversarially Robust Distillation | Micah Goldblum, Liam Fowl, Soheil Feizi, Tom Goldstein | We find that a large amount of robustness may be inherited by the student even when distilled on only clean images. |
| 488 | Robust Gradient-Based Markov Subsampling | Tieliang Gong, Quanhan Xi, Chen Xu | To tackle this issue, we propose a gradient-based Markov subsampling (GMS) algorithm to achieve robust estimation. |
| 489 | Online Metric Learning for Multi-Label Classification | Xiuwen Gong, Dong Yuan, Wei Bao | Accordingly, to fill the current research gap, we propose a novel online metric learning paradigm for multi-label classification. |
| 490 | Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development | Yongshun Gong, Zhibin Li, Jian Zhang, Wei Liu, Jinfeng Yi | In this paper, this specific problem is termed as potential passenger flow (PPF) prediction, which is a novel and important study connected with urban computing and intelligent transportation systems. |
| 491 | AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows | Aditya Grover, Christopher Chute, Rui Shu, Zhangjie Cao, Stefano Ermon | We propose AlignFlow, a generative modeling framework that models each domain via a normalizing flow. |
| 492 | Robust Stochastic Bandit Algorithms under Probabilistic Unbounded Adversarial Attack | Ziwei Guan, Kaiyi Ji, Donald J. Bucci Jr., Timothy Y. Hu, Joseph Palombo, Michael Liston, Yingbin Liang | We propose a novel sample median-based and exploration-aided UCB algorithm (called med-E-UCB) and a median-based ϵ-greedy algorithm (called med-ϵ-greedy). |
| 493 | Nonlinear Mixup: Out-Of-Manifold Data Augmentation for Text Classification | Hongyu Guo | To cope with this limitation, we propose “nonlinear Mixup”. |
| 494 | IWE-Net: Instance Weight Network for Locating Negative Comments and its application to improve Traffic User Experience | Lan-Zhe Guo, Feng Kuang, Zhang-Xun Liu, Yu-Feng Li, Nan Ma, Xiao-Hu Qie | In this paper, we propose the CWSL method to address this problem based on Didi ride-sharing comment data. |
| 495 | AdaFilter: Adaptive Filter Fine-Tuning for Deep Transfer Learning | Yunhui Guo, Yandong Li, Liqiang Wang, Tajana Rosing | We propose an adaptive fine-tuning approach, called AdaFilter, which selects only a part of the convolutional filters in the pre-trained model to optimize on a per-example basis. |
| 496 | High Tissue Contrast MRI Synthesis Using Multi-Stage Attention-GAN for Segmentation | Mohammad Hamghalam, Baiying Lei, Tianfu Wang | This paper demonstrates the potential benefits of image-to-image translation techniques to generate synthetic high tissue contrast (HTC) images. |
| 497 | Robust Federated Learning via Collaborative Machine Teaching | Yufei Han, Xiangliang Zhang | In our study, we echo this challenge by proposing a collaborative and privacy-preserving machine teaching method. |
| 498 | Interpretable and Differentially Private Predictions | Frederik Harder, Matthias Bauer, Mijung Park | In this paper, we propose a family of simple models with the aim of approximating complex models using several locally linear maps per class to provide high classification accuracy, as well as differentially private explanations on the classification. |
| 499 | null | Tao He, Lianli Gao, Jingkuan Song, Xin Wang, Kejie Huang, Yuanfang Li | null |
| 500 | SNEQ: Semi-Supervised Attributed Network Embedding with Attention-Based Quantisation | Tao He, Lianli Gao, Jingkuan Song, Xin Wang, Kejie Huang, Yuanfang Li | In this paper, we present a novel semi-supervised network embedding and compression method, SNEQ, that is competitive with state-of-art embedding methods while being far more space- and time-efficient. |
| 501 | Heterogeneous Transfer Learning with Weighted Instance-Correspondence Data | Yuwei He, Xiaoming Jin, Guiguang Ding, Yuchen Guo, Jungong Han, Jiyong Zhang, Sicheng Zhao | Specifically, we propose a novel heterogeneous transfer learning method named Transfer Learning with Weighted Correspondence (TLWC), which utilizes IC data to adapt the source domain to the target domain. |
| 502 | EPOC: Efficient Perception via Optimal Communication | Masoumeh Heidari Kapourchali, Bonny Banerjee | We propose an agent model capable of actively and selectively communicating with other agents to predict its environmental state efficiently. |
| 503 | Eigenvalue Normalized Recurrent Neural Networks for Short Term Memory | Kyle Helfrich, Qiang Ye | In this paper, we address this issue by proposing an architecture that expands upon an orthogonal/unitary RNN with a state that is generated by a recurrent matrix with eigenvalues in the unit disc. |
| 504 | Reasoning on Knowledge Graphs with Debate Dynamics | Marcel Hildebrandt, Jorge Andres Quintero Serna, Yunpu Ma, Martin Ringsquandl, Mitchell Joblin, Volker Tresp | We propose a novel method for automatic reasoning on knowledge graphs based on debate dynamics. |
| 505 | An Attention-Based Graph Neural Network for Heterogeneous Structural Learning | Huiting Hong, Hantao Guo, Yucheng Lin, Xiaoqing Yang, Zang Li, Jieping Ye | In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations. |
| 506 | End-to-End Unpaired Image Denoising with Conditional Adversarial Networks | Zhiwei Hong, Xiaocheng Fan, Tao Jiang, Jianxing Feng | In this paper, we present an end-to-end unpaired image denoising framework (UIDNet) that denoises images with only unpaired clean and noisy training images. |
| 507 | TellTail: Fast Scoring and Detection of Dense Subgraphs | Bryan Hooi, Kijung Shin, Hemank Lamba, Christos Faloutsos | We propose a novel application of extreme value theory to the dense subgraph problem, which allows us to propose measures and algorithms which evaluate the surprisingness of a subgraph probabilistically, without requiring restrictive assumptions (e.g. Erdős-Renyi). |
| 508 | Query-Driven Multi-Instance Learning | Yen-Chi Hsu, Cheng-Yao Hong, Ming-Sui Lee, Tyng-Luh Liu | We introduce a query-driven approach (qMIL) to multi-instance learning where the queries aim to uncover the class labels embodied in a given bag of instances. |
| 509 | Towards Interpretation of Pairwise Learning | Mengdi Huai, Di Wang, Chenglin Miao, Aidong Zhang | To tackle this problem, in this paper, we study feature importance scoring as a specific approach to the problem of interpreting the predictions of black-box pairwise models. |
| 510 | DWM: A Decomposable Winograd Method for Convolution Acceleration | Di Huang, Xishan Zhang, Rui Zhang, Tian Zhi, Deyuan He, Jiaming Guo, Chang Liu, Qi Guo, Zidong Du, Shaoli Liu, Tianshi Chen, Yunji Chen | In this paper, we propose a novel Decomposable Winograd Method (DWM), which breaks through the limitation of original Winograd’s minimal filtering algorithm to a wide and general convolutions. |
| 511 | Unsupervised Nonlinear Feature Selection from High-Dimensional Signed Networks | Qiang Huang, Tingyu Xia, Huiyan Sun, Makoto Yamada, Yi Chang | To this end, in this paper, we propose a nonlinear unsupervised feature selection method for signed networks, called SignedLasso. |
| 512 | Feature Variance Regularization: A Simple Way to Improve the Generalizability of Neural Networks | Ranran Huang, Hanbo Sun, Ji Liu, Lu Tian, Li Wang, Yi Shan, Yu Wang | To improve the generalization ability of neural networks, we propose a novel regularization method that regularizes the empirical risk using a penalty on the empirical variance of the features. |
| 513 | Meta-Learning PAC-Bayes Priors in Model Averaging | Yimin Huang, Weiran Huang, Liang Li, Zhenguo Li | In this paper, we mainly consider the scenario in which we have a common model set used for model averaging instead of selecting a single final model via a model selection procedure to account for this model’s uncertainty in order to improve reliability and accuracy of inferences. |
| 514 | DIANet: Dense-and-Implicit Attention Network | Zhongzhan Huang, Senwei Liang, Mingfu Liang, Haizhao Yang | Our paper proposes a novel-and-simple framework that shares an attention module throughout different network layers to encourage the integration of layer-wise information and this parameter-sharing module is referred to as Dense-and-Implicit-Attention (DIA) unit. |
| 515 | Collaborative Graph Convolutional Networks: Unsupervised Learning Meets Semi-Supervised Learning | Binyuan Hui, Pengfei Zhu, Qinghua Hu | In this paper, we propose a multi-task graph learning model, called collaborative graph convolutional networks (CGCN). |
| 516 | Control Flow Graph Embedding Based on Multi-Instance Decomposition for Bug Localization | Xuan Huo, Ming Li, Zhi-Hua Zhou | In this paper, we propose a novel model named CG-CNN, which is a multi-instance learning framework that enhances the unified features for bug localization by exploiting structural and sequential nature from the control flow graph. |
| 517 | Word-Level Contextual Sentiment Analysis with Interpretability | Tomoki Ito, Kota Tsubouchi, Hiroki Sakaji, Tatsuo Yamashita, Kiyoshi Izumi | This study aims to develop a WCSA method with interpretability and practicality. |
| 518 | Semi-Supervised Learning for Maximizing the Partial AUC | Tomoharu Iwata, Akinori Fujino, Naonori Ueda | In this paper, we propose a semi-supervised learning method for maximizing the pAUC, which trains a classifier with a small amount of labeled data and a large amount of unlabeled data. |
| 519 | Co-Occurrence Estimation from Aggregated Data with Auxiliary Information | Tomoharu Iwata, Naoki Marumo | We propose a method for estimating the co-occurrence of items from aggregated data with auxiliary information. |
| 520 | Class Prior Estimation with Biased Positives and Unlabeled Examples | Shantanu Jain, Justin Delano, Himanshu Sharma, Predrag Radivojac | We start by making a set of assumptions to model the sampling bias. |
| 521 | Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensembles | Siddhartha Jain, Ge Liu, Jonas Mueller, David Gifford | Here we describe Maximize Overall Diversity (MOD), an approach to improve ensemble-based uncertainty estimates by encouraging larger overall diversity in ensemble predictions across all possible inputs. |
| 522 | Invariant Representations through Adversarial Forgetting | Ayush Jaiswal, Daniel Moyer, Greg Ver Steeg, Wael AbdAlmageed, Premkumar Natarajan | We propose a novel approach to achieving invariance for deep neural networks in the form of inducing amnesia to unwanted factors of data through a new adversarial forgetting mechanism. |
| 523 | Bounding Regret in Empirical Games | Steven Jecmen, Arunesh Sinha, Zun Li, Long Tran-Thanh | We propose an efficient algorithm Super-Arm UCB (SAUCB) for the problem and a number of variants. |
| 524 | An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks | Giyoung Jeon, Haedong Jeong, Jaesik Choi | In this paper, we present an explorative sampling algorithm to analyze generation mechanism of DGNNs. To handle a large number of boundaries, we obtain the essential set of boundaries using optimization. |
| 525 | DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets | Yonghyun Jeong, Hyunjin Choi, Byoungjip Kim, Youngjune Gwon | We propose DefogGAN, a generative approach to the problem of inferring state information hidden in the fog of war for real-time strategy (RTS) games. |
| 526 | Sequential Recommendation with Relation-Aware Kernelized Self-Attention | Mingi Ji, Weonyoung Joo, Kyungwoo Song, Yoon-Yeong Kim, Il-Chul Moon | Therefore, we introduce a latent space to the self-attention, and the latent space models the recommendation context from relation as a multivariate skew-normal distribution with a kernelized covariance matrix from co-occurrences, item characteristics, and user information. |
| 527 | Maximum Margin Multi-Dimensional Classification | Bin-Bin Jia, Min-Ling Zhang | In this paper, a first attempt towards maximum margin multi-dimensional classification is investigated. |
| 528 | Representation Learning with Multiple Lipschitz-Constrained Alignments on Partially-Labeled Cross-Domain Data | Songlei Jian, Liang Hu, Longbing Cao, Kai Lu | To address this challenge, we propose a new cross-domain representation learning approach: MUltiple Lipschitz-constrained AligNments (MULAN) on partially-labeled cross-domain data. |
| 529 | Algorithmic Improvements for Deep Reinforcement Learning Applied to Interactive Fiction | Vishal Jain, William Fedus, Hugo Larochelle, Doina Precup, Marc G. Bellemare | In this paper we emphasize this latter point and consider the design of a deep reinforcement learning agent that can play from feedback alone. |
| 530 | Generative Exploration and Exploitation | Jiechuan Jiang, Zongqing Lu | In this paper, we propose a novel method called Generative Exploration and Exploitation (GENE) to overcome sparse reward. |
| 531 | Long Short-Term Sample Distillation | Liang Jiang, Zujie Wen, Zhongping Liang, Yafang Wang, Gerard de Melo, Zhe Li, Liangzhuang Ma, Jiaxing Zhang, Xiaolong Li, Yuan Qi | Based on this notion, in this paper, we propose Long Short-Term Sample Distillation, a novel training policy that simultaneously leverages multiple phases of the previous training process to guide the later training updates to a neural network, while efficiently proceeding in just one single generation pass. |
| 532 | Rank Aggregation via Heterogeneous Thurstone Preference Models | Tao Jin, Pan Xu, Quanquan Gu, Farzad Farnoud | We propose the Heterogeneous Thurstone Model (HTM) for aggregating ranked data, which can take the accuracy levels of different users into account. |
| 533 | GraLSP: Graph Neural Networks with Local Structural Patterns | Yilun Jin, Guojie Song, Chuan Shi | In this paper, we propose GraLSP, a GNN framework which explicitly incorporates local structural patterns into the neighborhood aggregation through random anonymous walks. |
| 534 | Dynamic Instance Normalization for Arbitrary Style Transfer | Yongcheng Jing, Xiao Liu, Yukang Ding, Xinchao Wang, Errui Ding, Mingli Song, Shilei Wen | In this paper, we propose a new and generalized normalization module, termed as Dynamic Instance Normalization (DIN), that allows for flexible and more efficient arbitrary style transfers. |
| 535 | InvNet: Encoding Geometric and Statistical Invariances in Deep Generative Models | Ameya Joshi, Minsu Cho, Viraj Shah, Balaji Pokuri, Soumik Sarkar, Baskar Ganapathysubramanian, Chinmay Hegde | We propose a new conditional generative modeling approach (InvNet) that efficiently enables modeling discrete-valued images, while allowing control over their parameterized geometric and statistical properties. |
| 536 | More Accurate Learning of k-DNF Reference Classes | Brendan Juba, Hengxuan Li | We present new algorithms for computing k-DNF reference classes and establish much stronger approximation guarantees for their error rates. |
| 537 | Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks | Sekitoshi Kanai, Yasutoshi Ida, Yasuhiro Fujiwara, Masanori Yamada, Shuichi Adachi | We propose Absum, which is a regularization method for improving adversarial robustness of convolutional neural networks (CNNs). |
| 538 | Towards Oracle Knowledge Distillation with Neural Architecture Search | Minsoo Kang, Jonghwan Mun, Bohyung Han | We present a novel framework of knowledge distillation that is capable of learning powerful and efficient student models from ensemble teacher networks. |
| 539 | Large-Scale Multi-View Subspace Clustering in Linear Time | Zhao Kang, Wangtao Zhou, Zhitong Zhao, Junming Shao, Meng Han, Zenglin Xu | To fill this gap, we propose a large-scale MVSC (LMVSC) algorithm with linear order complexity. |
| 540 | Nonlinear System Identification via Tensor Completion | Nikos Kargas, Nicholas D. Sidiropoulos | In this work, we show that identifying a general nonlinear function y = ƒ(x1,…,xN) from input-output examples can be formulated as a tensor completion problem and under certain conditions provably correct nonlinear system identification is possible. |
| 541 | Gradient Boosts the Approximate Vanishing Ideal | Hiroshi Kera, Yoshihiko Hasegawa | In this paper, we propose general methods that equip monomial-order-free algorithms with several advantageous theoretical properties. |
| 542 | Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy | Ramtin Keramati, Christoph Dann, Alex Tamkin, Emma Brunskill | In this paper, we present the first algorithm for sample-efficient learning of CVaR-optimal policies in Markov decision processes based on the optimism in the face of uncertainty principle. |
| 543 | Options of Interest: Temporal Abstraction with Interest Functions | Khimya Khetarpal, Martin Klissarov, Maxime Chevalier-Boisvert, Pierre-Luc Bacon, Doina Precup | The options framework describes such behaviours as consisting of a subset of states in which they can initiate, an internal policy and a stochastic termination condition. |
| 544 | Plug-in, Trainable Gate for Streamlining Arbitrary Neural Networks | Jaedeok Kim, Chiyoun Park, Hyun-Joo Jung, Yoonsuck Choe | To tackle this problem we introduce a novel concept of a trainable gate function. |
| 545 | A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domains | Harsha Kokel, Phillip Odom, Shuo Yang, Sriraam Natarajan | Inspired by this, we consider the problem of using such influence statements in the successful gradient-boosting framework. |
| 546 | Learning Student Networks with Few Data | Shumin Kong, Tianyu Guo, Shan You, Chang Xu | In this paper, we tackle the challenge of learning student networks with few data by investigating the ground-truth data-generating distribution underlying these few data. |
| 547 | Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes | Ranganath Krishnan, Mahesh Subedar, Omesh Tickoo | We propose MOdel Priors with Empirical Bayes using DNN (MOPED) method to choose informed weight priors in Bayesian neural networks. |
| 548 | Stable Prediction with Model Misspecification and Agnostic Distribution Shift | Kun Kuang, Ruoxuan Xiong, Peng Cui, Susan Athey, Bo Li | To address these problems, we propose a novel Decorrelated Weighting Regression (DWR) algorithm which jointly optimizes a variable decorrelation regularizer and a weighted regression model. |
| 549 | Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation | Mohit Kumar, Samuel Kolb, Stefano Teso, Luc De Raedt | We introduce a novel setting for learning combinatorial optimisation problems from contextual examples. |
| 550 | Google Research Football: A Novel Reinforcement Learning Environment | Karol Kurach, Anton Raichuk, Piotr Stańczyk, Michał Zając, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, Sylvain Gelly | We introduce the Google Research Football Environment, a new reinforcement learning environment where agents are trained to play football in an advanced, physics-based 3D simulator. We also provide a diverse set of simpler scenarios with the Football Academy and showcase several promising research directions. |
| 551 | Correcting Predictions for Approximate Bayesian Inference | Tomasz Kuśmierczyk, Joseph Sakaya, Arto Klami | We present a novel approach that corrects for inaccuracies in posterior inference by altering the decision-making process. |
| 552 | Improved Subsampled Randomized Hadamard Transform for Linear SVM | Zijian Lei, Liang Lan | Based on our analysis, we propose importance sampling and deterministic top-r sampling to produce effective low-dimensional embedding instead of uniform sampling SRHT. |
| 553 | A Simple and Efficient Tensor Calculus | Sören Laue, Matthias Mitterreiter, Joachim Giesen | Recently, an algorithm for computing higher order derivatives of tensor expressions like Jacobians or Hessians has been introduced that is a few orders of magnitude faster than previous state-of-the-art approaches. |
| 554 | Proximity Preserving Binary Code Using Signed Graph-Cut | Inbal Lavi, Shai Avidan, Yoram Singer, Yacov Hel-Or | We introduce a binary embedding framework, called Proximity Preserving Code (PPC), which learns similarity and dissimilarity between data points to create a compact and affinity-preserving binary code. |
| 555 | Residual Neural Processes | Byung-Jun Lee, Seunghoon Hong, Kee-Eung Kim | In this paper, we propose a simple yet effective remedy; the Residual Neural Process (RNP) that leverages traditional BLL for faster training and better prediction. |
| 556 | Residual Continual Learning | Janghyeon Lee, Donggyu Joo, Hyeong Gwon Hong, Junmo Kim | We propose a novel continual learning method called Residual Continual Learning (ResCL). |
| 557 | Monte-Carlo Tree Search in Continuous Action Spaces with Value Gradients | Jongmin Lee, Wonseok Jeon, Geon-Hyeong Kim, Kee-Eung Kim | In this paper, we introduce Value-Gradient UCT (VG-UCT), which combines traditional MCTS with gradient-based optimization of action particles. |
| 558 | URNet: User-Resizable Residual Networks with Conditional Gating Module | Sangho Lee, Simyung Chang, Nojun Kwak | We propose User-Resizable Residual Networks (URNet), which allows users to adjust the computational cost of the network as needed during evaluation. |
| 559 | Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents | Xian Yeow Lee, Sambit Ghadai, Kai Liang Tan, Chinmay Hegde, Soumik Sarkar | We propose the white-box Myopic Action Space (MAS) attack algorithm that distributes the attacks across the action space dimensions. |
| 560 | Robustness Certificates for Sparse Adversarial Attacks by Randomized Ablation | Alexander Levine, Soheil Feizi | In this paper, we extend this technique to the L0 threat model. |
| 561 | Stochastically Robust Personalized Ranking for LSH Recommendation Retrieval | Dung D. Le, Hady W. Lauw | In this paper, we propose a framework named øurmodel, which factors in the stochasticity of LSH hash functions when learning real-valued user and item latent vectors, eventually improving the recommendation accuracy after LSH indexing. |
| 562 | Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition Under Reshuffling | Chao Li, Mohammad Emtiyaz Khan, Zhun Sun, Gang Niu, Bo Han, Shengli Xie, Qibin Zhao | To this end, we focus on latent convex tensor decomposition (LCTD), a practically widely-used CTD model, and rigorously prove a sufficient condition for its exact-recovery property. |
| 563 | Infrared-Visible Cross-Modal Person Re-Identification with an X Modality | Diangang Li, Xing Wei, Xiaopeng Hong, Yihong Gong | With this idea, we propose an X-Infrared-Visible (XIV) ReID cross-modal learning framework. |
| 564 | Automated Spectral Kernel Learning | Jian Li, Yong Liu, Weiping Wang | In this paper, we propose an efficient learning framework that incorporates the process of finding suitable kernels and model training. |
| 565 | Graph Attention Based Proposal 3D ConvNets for Action Detection | Jin Li, Xianglong Liu, Zhuofan Zong, Wanru Zhao, Mingyuan Zhang, Jingkuan Song | To address this problem, we propose graph attention based proposal 3D ConvNets (AGCN-P-3DCNNs) for video action detection. |
| 566 | Symmetric Metric Learning with Adaptive Margin for Recommendation | Mingming Li, Shuai Zhang, Fuqing Zhu, Wanhui Qian, Liangjun Zang, Jizhong Han, Songlin Hu | Metric learning based methods have attracted extensive interests in recommender systems. |
| 567 | Practical Federated Gradient Boosting Decision Trees | Qinbin Li, Zeyi Wen, Bingsheng He | In this paper, we focus on horizontal federated learning, where data samples with the same features are distributed among multiple parties. |
| 568 | New Efficient Multi-Spike Learning for Fast Processing and Robust Learning | Shenglan Li, Qiang Yu | Based on it, we develop two new multi-spike learning rules together with an event-driven scheme being presented to improve the processing efficiency. |
| 569 | Solving General Elliptical Mixture Models through an Approximate Wasserstein Manifold | Shengxi Li, Zeyang Yu, Min Xiang, Danilo Mandic | To relieve this issue, we introduce an efficient optimisation method on a statistical manifold defined under an approximate Wasserstein distance, which allows for explicit metrics and computable operations, thus significantly stabilising and improving the EMM estimation. |
| 570 | Coupled-View Deep Classifier Learning from Multiple Noisy Annotators | Shikun Li, Shiming Ge, Yingying Hua, Chunhui Zhang, Hao Wen, Tengfei Liu, Weiqiang Wang | Inspired by that, this paper proposes to learn deep classifier from multiple noisy annotators via a coupled-view learning approach, where the learning view from data is represented by deep neural networks for data classification and the learning view from labels is described by a Naive Bayes classifier for label aggregation. |
| 571 | Stochastic Online Learning with Probabilistic Graph Feedback | Shuai Li, Wei Chen, Zheng Wen, Kwong-Sak Leung | We consider a problem of stochastic online learning with general probabilistic graph feedback, where each directed edge in the feedback graph has probability pij. |
| 572 | Relation Inference among Sensor Time Series in Smart Buildings with Metric Learning | Shuheng Li, Dezhi Hong, Hongning Wang | Our key insight is that, as equipment is connected or sensors co-locate in the same physical environment, they are affected by the same real-world events, e.g., a fan turning on or a person entering the room, thus exhibiting correlated changes in their time series data. |
| 573 | Co-GCN for Multi-View Semi-Supervised Learning | Shu Li, Wen-Tao Li, Wei Wang | In this paper, we bring Graph Convolutional Network (GCN) into multi-view learning and propose a novel multi-view semi-supervised learning method Co-GCN by adaptively exploiting the graph information from the multiple views with combined Laplacians. |
| 574 | Tweedie-Hawkes Processes: Interpreting the Phenomena of Outbreaks | Tianbo Li, Yiping Ke | In this paper, we propose a Bayesian model called Tweedie-Hawkes Processes (THP), which is able to model the outbreaks of events and find out the dominant factors behind. |
| 575 | Neural Graph Embedding for Neural Architecture Search | Wei Li, Shaogang Gong, Xiatian Zhu | In this work, we address this limitation by introducing a novel idea of neural graph embedding (NGE). |
| 576 | Understanding the Disharmony between Weight Normalization Family and Weight Decay | Xiang Li, Shuo Chen, Jian Yang | In this paper, we theoretically prove that ½λ∥W∥2 improves optimization only by modulating the effective learning rate and fairly has no influence on generalization when the weight normalization family is compositely employed. |
| 577 | Do Subsampled Newton Methods Work for High-Dimensional Data? | Xiang Li, Shusen Wang, Zhihua Zhang | This paper theoretically justifies the effectiveness of subsampled Newton methods on strongly convex empirical risk minimization with high dimensional data. |
| 578 | FlowScope: Spotting Money Laundering Based on Graphs | Xiangfeng Li, Shenghua Liu, Zifeng Li, Xiaotian Han, Chuan Shi, Bryan Hooi, He Huang, Xueqi Cheng | Instead, we propose to model the transactions using a multipartite graph, and detect the complete flow of money from source to destination using a scalable algorithm, FlowScope. |
| 579 | On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks | Xiangrui Li, Xin Li, Deng Pan, Dongxiao Zhu | In this paper, motivated to explain the reweighting mechanism, we explicate the learning property of those two loss functions by analyzing the necessary condition (e.g., gradient equals to zero) after training CNNs to converge to a local minimum. |
| 580 | IVFS: Simple and Efficient Feature Selection for High Dimensional Topology Preservation | Xiaoyun Li, Chenxi Wu, Ping Li | In this paper, we propose a simple and effective feature selection algorithm to enhance sample similarity preservation through a new perspective, topology preservation, which is represented by persistent diagrams from the context of computational topology. |
| 581 | A Forest from the Trees: Generation through Neighborhoods | Yang Li, Tianxiang Gao, Junier Oliva | In this work, we propose to learn a generative model using both learned features (through a latent space) and memories (through neighbors). |
| 582 | Efficient Automatic CASH via Rising Bandits | Yang Li, Jiawei Jiang, Jinyang Gao, Yingxia Shao, Ce Zhang, Bin Cui | To alleviate this issue, we propose the alternating optimization framework, where the HPO problem for each ML algorithm and the algorithm selection problem are optimized alternately. |
| 583 | Learning Signed Network Embedding via Graph Attention | Yu Li, Yuan Tian, Jiawei Zhang, Yi Chang | Thus in this paper, we propose a novel network embedding framework SNEA to learn Signed Network Embedding via graph Attention. |
| 584 | RTN: Reparameterized Ternary Network | Yuhang Li, Xin Dong, Sai Qian Zhang, Haoli Bai, Yuanpeng Chen, Wei Wang | In this work, we study the extremely low-bit networks which have tremendous speed-up, memory saving with quantized activation and weights. |
| 585 | Learning to Auto Weight: Entirely Data-Driven and Highly Efficient Weighting Framework | Zhenmao Li, Yichao Wu, Ken Chen, Yudong Wu, Shunfeng Zhou, Jiaheng Liu, Jiaheng Liu, Junjie Yan | In this paper, we propose a novel example weighting framework called Learning to Auto Weight (LAW). |
| 586 | Adaptive Two-Dimensional Embedded Image Clustering | Zhihui Li, Lina Yao, Sen Wang, Salil Kanhere, Xue Li, Huaxiang Zhang | To overcome the drawbacks, we propose a novel image clustering framework that can work directly on matrices of images instead of flattened vectors. |
| 587 | Tensor Completion for Weakly-Dependent Data on Graph for Metro Passenger Flow Prediction | Ziyue Li, Nurettin Dorukhan Sergin, Hao Yan, Chen Zhang, Fugee Tsung | As shown in the preliminary study, weakly dependencies can worsen the low-rank tensor completion performance. |
| 588 | LMLFM: Longitudinal Multi-Level Factorization Machine | Junjie Liang, Dongkuan Xu, Yiwei Sun, Vasant Honavar | We propose Longitudinal Multi-Level Factorization Machine (LMLFM), to the best of our knowledge, the first model to address these challenges in learning predictive models from longitudinal data. |
| 589 | Instance Enhancement Batch Normalization: An Adaptive Regulator of Batch Noise | Senwei Liang, Zhongzhan Huang, Mingfu Liang, Haizhao Yang | Therefore, we propose an attention-based BN called Instance Enhancement Batch Normalization (IEBN) that recalibrates the information of each channel by a simple linear transformation. |
| 590 | Differentiable Algorithm for Marginalising Changepoints | Hyoungjin Lim, Gwonsoo Che, Wonyeol Lee, Hongseok Yang | We present an algorithm for marginalising changepoints in time-series models that assume a fixed number of unknown changepoints. |
| 591 | OOGAN: Disentangling GAN with One-Hot Sampling and Orthogonal Regularization | Bingchen Liu, Yizhe Zhu, Zuohui Fu, Gerard de Melo, Ahmed Elgammal | Exploring the potential of GANs for unsupervised disentanglement learning, this paper proposes a novel GAN-based disentanglement framework with One-Hot Sampling and Orthogonal Regularization (OOGAN). |
| 592 | Random Fourier Features via Fast Surrogate Leverage Weighted Sampling | Fanghui Liu, Xiaolin Huang, Yudong Chen, Jie Yang, Johan Suykens | In this paper, we propose a fast surrogate leverage weighted sampling strategy to generate refined random Fourier features for kernel approximation. |
| 593 | EC-GAN: Inferring Brain Effective Connectivity via Generative Adversarial Networks | Jinduo Liu, Junzhong Ji, Guangxu Xun, Liuyi Yao, Mengdi Huai, Aidong Zhang | In this paper, we propose a novel framework for inferring effective connectivity based on generative adversarial networks (GAN), named as EC-GAN. |
| 594 | A Cluster-Weighted Kernel K-Means Method for Multi-View Clustering | Jing Liu, Fuyuan Cao, Xiao-Zhi Gao, Liqin Yu, Jiye Liang | In this paper, we propose a cluster-weighted kernel k-means method for multi-view clustering. |
| 595 | Attribute Propagation Network for Graph Zero-Shot Learning | Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang | In this paper, we aim to optimize the attribute space for ZSL by training a propagation mechanism to refine the semantic attributes of each class based on its neighbors and related classes on a graph of classes. |
| 596 | AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates | Ning Liu, Xiaolong Ma, Zhiyuan Xu, Yanzhi Wang, Jian Tang, Jieping Ye | This work proposes AutoCompress, an automatic structured pruning framework with the following key performance improvements: (i) effectively incorporate the combination of structured pruning schemes in the automatic process; (ii) adopt the state-of-art ADMM-based structured weight pruning as the core algorithm, and propose an innovative additional purification step for further weight reduction without accuracy loss; and (iii) develop effective heuristic search method enhanced by experience-based guided search, replacing the prior deep reinforcement learning technique which has underlying incompatibility with the target pruning problem. |
| 597 | Stochastic Loss Function | Qingliang Liu, Jinmei Lai | To improve learning efficiency, we develop Stochastic Loss Function (SLF) to dynamically and automatically generating appropriate gradients to train deep networks in the same round of back-propagation, while maintaining the completeness and differentiability of the training pipeline. |
| 598 | An ADMM Based Framework for AutoML Pipeline Configuration | Sijia Liu, Parikshit Ram, Deepak Vijaykeerthy, Djallel Bouneffouf, Gregory Bramble, Horst Samulowitz, Dakuo Wang, Andrew Conn, Alexander Gray | We propose a novel AutoML scheme by leveraging the alternating direction method of multipliers (ADMM). |
| 599 | Layerwise Sparse Coding for Pruned Deep Neural Networks with Extreme Compression Ratio | Xiao Liu, Wenbin Li, Jing Huo, Lili Yao, Yang Gao | To tackle this issue, we propose a layerwise sparse coding (LSC) method to maximize the compression ratio by extremely reducing the amount of meta-data. |
| 600 | Weighted-Sampling Audio Adversarial Example Attack | Xiaolei Liu, Kun Wan, Yufei Ding, Xiaosong Zhang, Qingxin Zhu | In this paper, we propose weighted-sampling audio adversarial examples, focusing on the numbers and the weights of distortion to reinforce the attack. |
| 601 | Independence Promoted Graph Disentangled Networks | Yanbei Liu, Xiao Wang, Shu Wu, Zhitao Xiao | In this paper, we propose a novel Independence Promoted Graph Disentangled Networks (IPGDN) to learn disentangled node representation while enhancing the independence among node representations. |
| 602 | Adaptive Activation Network and Functional Regularization for Efficient and Flexible Deep Multi-Task Learning | Yingru Liu, Xuewen Yang, Dongliang Xie, Xin Wang, Li Shen, Haozhi Huang, Niranjan Balasubramanian | In this paper, we propose a novel deep learning model called Task Adaptive Activation Network (TAAN) that can automatically learn the optimal network architecture for MTL. |
| 603 | Diversified Interactive Recommendation with Implicit Feedback | Yong Liu, Yingtai Xiao, Qiong Wu, Chunyan Miao, Juyong Zhang, Binqiang Zhao, Haihong Tang | In this paper, we propose a novel diversified recommendation model, named Diversified Contextual Combinatorial Bandit (DC2B), for interactive recommendation with users’ implicit feedback. |
| 604 | IPO: Interior-Point Policy Optimization under Constraints | Yongshuai Liu, Jiaxin Ding, Xin Liu | In this paper, we study reinforcement learning (RL) algorithms to solve real-world decision problems with the objective of maximizing the long-term reward as well as satisfying cumulative constraints. |
| 605 | Collaborative Sampling in Generative Adversarial Networks | Yuejiang Liu, Parth Kothari, Alexandre Alahi | In this work, we propose a collaborative sampling scheme between the generator and the discriminator for improved data generation. |
| 606 | Uncertainty Aware Graph Gaussian Process for Semi-Supervised Learning | Zhao-Yang Liu, Shao-Yuan Li, Songcan Chen, Yao Hu, Sheng-Jun Huang | Considering that Gaussian process generalizes well with few labels and can naturally model uncertainty, in this paper, we propose an Uncertainty aware Graph Gaussian Process based approach (UaGGP) for GSSL. |
| 607 | Interactive Rare-Category-of-Interest Mining from Large Datasets | Zhenguang Liu, Sihao Hu, Yifang Yin, Jianhai Chen, Kevin Chiew, Luming Zhang, Zetian Wu | In this paper, we contribute a new model named IRim, which can interactively mine rare category data examples of interest over large datasets. As a side contribution, we construct and release two benchmark datasets which to our knowledge are the first public datasets tailored for rare category mining task. |
| 608 | Towards Fine-Grained Temporal Network Representation via Time-Reinforced Random Walk | Zhining Liu, Dawei Zhou, Yada Zhu, Jinjie Gu, Jingrui He | To bridge this gap, in this paper, we propose a fine-grained temporal network embedding framework named FiGTNE, which aims to learn a comprehensive network representation that preserves the rich and complex network context in the temporal network. |
| 609 | Incentivized Exploration for Multi-Armed Bandits under Reward Drift | Zhiyuan Liu, Huazheng Wang, Fan Shen, Kai Liu, Lijun Chen | We study incentivized exploration for the multi-armed bandit (MAB) problem where the players receive compensation for exploring arms other than the greedy choice and may provide biased feedback on reward. |
| 610 | Structured Sparsification of Gated Recurrent Neural Networks | Ekaterina Lobacheva, Nadezhda Chirkova, Alexander Markovich, Dmitry Vetrov | Specifically, in addition to the sparsification of individual weights and neurons, we propose sparsifying the preactivations of gates. |
| 611 | Cost-Effective Incentive Allocation via Structured Counterfactual Inference | Romain Lopez, Chenchen Li, Xiang Yan, Junwu Xiong, Michael Jordan, Yuan Qi, Le Song | We address a practical problem ubiquitous in modern marketing campaigns, in which a central agent tries to learn a policy for allocating strategic financial incentives to customers and observes only bandit feedback. |
| 612 | Structured Output Learning with Conditional Generative Flows | You Lu, Bert Huang | In this paper, we propose conditional Glow (c-Glow), a conditional generative flow for structured output learning. |
| 613 | Enhancing Nearest Neighbor Based Entropy Estimator for High Dimensional Distributions via Bootstrapping Local Ellipsoid | Chien Lu, Jaakko Peltonen | We argue that the inaccuracy of the classical kNN estimator in high dimensional spaces results from the local uniformity assumption and the proposed method mitigates the local uniformity assumption by two crucial extensions, a local ellipsoid-based volume correction and a correction acceptance testing procedure. |
| 614 | Learning from the Past: Continual Meta-Learning with Bayesian Graph Neural Networks | Yadan Luo, Zi Huang, Zheng Zhang, Ziwei Wang, Mahsa Baktashmotlagh, Yang Yang | In this paper, we propose a novel Continual Meta-Learning approach with Bayesian Graph Neural Networks (CML-BGNN) that mathematically formulates meta-learning as continual learning of a sequence of tasks. |
| 615 | Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment | You-Wei Luo, Chuan-Xian Ren, Pengfei Ge, Ke-Kun Huang, Yu-Feng Yu | In this paper, a Riemannian manifold learning framework is proposed to achieve transferability and discriminability consistently. |
| 616 | Fastened CROWN: Tightened Neural Network Robustness Certificates | Zhaoyang Lyu, Ching-Yun Ko, Zhifeng Kong, Ngai Wong, Dahua Lin, Luca Daniel | Fastened CROWN: Tightened Neural Network Robustness Certificates |
| 617 | Memory Augmented Graph Neural Networks for Sequential Recommendation | Chen Ma, Liheng Ma, Yingxue Zhang, Jianing Sun, Xue Liu, Mark Coates | To tackle these challenges, we propose a memory augmented graph neural network (MA-GNN) to capture both the long- and short-term user interests. |
| 618 | Inefficiency of K-FAC for Large Batch Size Training | Linjian Ma, Gabe Montague, Jiayu Ye, Zhewei Yao, Amir Gholami, Kurt Keutzer, Michael Mahoney | In this work, we perform an extensive analysis of large batch size training for two popular methods that is Stochastic Gradient Descent (SGD) as well as Kronecker-Factored Approximate Curvature (K-FAC) method. |
| 619 | Temporal Pyramid Recurrent Neural Network | Qianli Ma, Zhenxi Lin, Enhuan Chen, Garrison Cottrell | In this paper, a novel RNN structure called temporal pyramid RNN (TP-RNN) is proposed to achieve these two goals. |
| 620 | Adversarial Dynamic Shapelet Networks | Qianli Ma, Wanqing Zhuang, Sen Li, Desen Huang, Garrison Cottrell | In this paper, we propose a novel shapelet learning model called Adversarial Dynamic Shapelet Networks (ADSNs). |
| 621 | Online Planner Selection with Graph Neural Networks and Adaptive Scheduling | Tengfei Ma, Patrick Ferber, Siyu Huo, Jie Chen, Michael Katz | Owing to the recent development of structural graph representations of planning tasks, we propose a graph neural network (GNN) approach to selecting candidate planners. |
| 622 | The HSIC Bottleneck: Deep Learning without Back-Propagation | Wan-Duo Kurt Ma, J. P. Lewis, W. Bastiaan Kleijn | We introduce the HSIC (Hilbert-Schmidt independence criterion) bottleneck for training deep neural networks. |
| 623 | Projective Quadratic Regression for Online Learning | Wenye Ma | In this paper, We propose a projective quadratic regression (PQR) model. |
| 624 | Particle Filter Recurrent Neural Networks | Xiao Ma, Peter Karkus, David Hsu, Wee Sun Lee | To tackle highly variable and multi-modal real-world data, we introduce Particle Filter Recurrent Neural Networks (PF-RNNs), a new RNN family that explicitly models uncertainty in its internal structure: while an RNN relies on a long, deterministic latent state vector, a PF-RNN maintains a latent state distribution, approximated as a set of particles. |
| 625 | Reinforcement Learning from Imperfect Demonstrations under Soft Expert Guidance | Mingxuan Jing, Xiaojian Ma, Wenbing Huang, Fuchun Sun, Chao Yang, Bin Fang, Huaping Liu | In this paper, we study Reinforcement Learning from Demonstrations (RLfD) that improves the exploration efficiency of Reinforcement Learning (RL) by providing expert demonstrations. |
| 626 | PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-Time Execution on Mobile Devices | Xiaolong Ma, Fu-Ming Guo, Wei Niu, Xue Lin, Jian Tang, Kaisheng Ma, Bin Ren, Yanzhi Wang | In this paper, we introduce PCONV, comprising a new sparsity dimension, – fine-grained pruning patterns inside the coarse-grained structures. |
| 627 | Count-Based Exploration with the Successor Representation | Marlos C. Machado, Marc G. Bellemare, Michael Bowling | In this paper we introduce a simple approach for exploration in reinforcement learning (RL) that allows us to develop theoretically justified algorithms in the tabular case but that is also extendable to settings where function approximation is required. |
| 628 | Graph-Hist: Graph Classification from Latent Feature Histograms with Application to Bot Detection | Thomas Magelinski, David Beskow, Kathleen M. Carley | Inspired by this, we introduce Graph-Hist: an end-to-end architecture that extracts a graph’s latent local features, bins nodes together along 1-D cross sections of the feature space, and classifies the graph based on this multi-channel histogram. |
| 629 | Learning Agent Communication under Limited Bandwidth by Message Pruning | Hangyu Mao, Zhengchao Zhang, Zhen Xiao, Zhibo Gong, Yan Ni | To handle this problem, we propose a gating mechanism to adaptively prune less beneficial messages. |
| 630 | Multi-Zone Unit for Recurrent Neural Networks | Fandong Meng, Jinchao Zhang, Yang Liu, Jie Zhou | In this paper, we introduce a new Multi-zone Unit (MZU) for RNNs. |
| 631 | Neural Inheritance Relation Guided One-Shot Layer Assignment Search | Rang Meng, Weijie Chen, Di Xie, Yuan Zhang, Shiliang Pu | Inspired by this neural inheritance relation, we propose an efficient one-shot layer assignment search approach via inherited sampling. |
| 632 | Regularized Wasserstein Means for Aligning Distributional Data | Liang Mi, Wen Zhang, Yalin Wang | We propose to align distributional data from the perspective of Wasserstein means. |
| 633 | Deep Embedded Non-Redundant Clustering | Lukas Miklautz, Dominik Mautz, Muzaffer Can Altinigneli, Christian Böhm, Claudia Plant | In this paper, we propose the novel Embedded Non-Redundant Clustering algorithm (ENRC). |
| 634 | Differentiable Reasoning on Large Knowledge Bases and Natural Language | Pasquale Minervini, Matko Bošnjak, Tim Rocktäschel, Sebastian Riedel, Edward Grefenstette | We show that GNTPs perform on par with NTPs at a fraction of their cost while achieving competitive link prediction results on large datasets, providing explanations for predictions, and inducing interpretable models. |
| 635 | Improved Knowledge Distillation via Teacher Assistant | Seyed Iman Mirzadeh, Mehrdad Farajtabar, Ang Li, Nir Levine, Akihiro Matsukawa, Hassan Ghasemzadeh | To alleviate this shortcoming, we introduce multi-step knowledge distillation, which employs an intermediate-sized network (teacher assistant) to bridge the gap between the student and the teacher. |
| 636 | On Adaptivity in Information-Constrained Online Learning | Siddharth Mitra, Aditya Gopalan | For the problem of label efficient prediction, which is a budgeted version of prediction with expert advice, we present an online algorithm whose regret depends optimally on the number of labels allowed and Q* (the quadratic variation of the losses of the best action in hindsight), along with a parameter-free counterpart whose regret depends optimally on Q (the quadratic variation of the losses of all the actions). |
| 637 | Metareasoning in Modular Software Systems: On-the-Fly Configuration Using Reinforcement Learning with Rich Contextual Representations | Aditya Modi, Debadeepta Dey, Alekh Agarwal, Adith Swaminathan, Besmira Nushi, Sean Andrist, Eric Horvitz | We present metareasoning techniques which consider a rich representation of the input, monitor the state of the entire pipeline, and adjust the configuration of modules on-the-fly so as to maximize the utility of a system’s operation. |
| 638 | Self-Supervised Learning for Generalizable Out-of-Distribution Detection | Sina Mohseni, Mandar Pitale, JBS Yadawa, Zhangyang Wang | We propose a new technique relying on self-supervision for generalizable out-of-distribution (OOD) feature learning and rejecting those samples at the inference time. |
| 639 | Learning Weighted Model Integration Distributions | Paolo Morettin, Samuel Kolb, Stefano Teso, Andrea Passerini | We propose lariat, a novel method to tackle this challenging problem. |
| 640 | An Intrinsically-Motivated Approach for Learning Highly Exploring and Fast Mixing Policies | Mirco Mutti, Marcello Restelli | In this paper, we propose a novel surrogate objective for learning highly exploring and fast mixing policies, which focuses on maximizing a lower bound to the entropy of the steady-state distribution induced by the policy. |
| 641 | Efficiently Enumerating Substrings with Statistically Significant Frequencies of Locally Optimal Occurrences in Gigantic String | Atsuyoshi Nakamura, Ichigaku Takigawa, Hiroshi Mamitsuka | We propose new frequent substring pattern mining which can enumerate all substrings with statistically significant frequencies of their locally optimal occurrences from a given single sequence. |
| 642 | Pairwise Fairness for Ranking and Regression | Harikrishna Narasimhan, Andrew Cotter, Maya Gupta, Serena Wang | We present pairwise fairness metrics for ranking models and regression models that form analogues of statistical fairness notions such as equal opportunity, equal accuracy, and statistical parity. |
| 643 | Bayesian Optimization for Categorical and Category-Specific Continuous Inputs | Dang Nguyen, Sunil Gupta, Santu Rana, Alistair Shilton, Svetha Venkatesh | To optimize such functions, we propose a new method that formulates the problem as a multi-armed bandit problem, wherein each category corresponds to an arm with its reward distribution centered around the optimum of the objective function in continuous variables. |
| 644 | Reliable Multilabel Classification: Prediction with Partial Abstention | Vu-Linh Nguyen, Eyke Hullermeier | In this paper, we study an extension of the setting of MLC, in which the learner is allowed to partially abstain from a prediction, that is, to deliver predictions on some but not necessarily all class labels. |
| 645 | On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models | Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, Ying Nian Wu | Based on this insight, we introduce an ML method with purely noise-initialized MCMC, high-quality short-run synthesis, and the same budget as ML with informative MCMC initialization such as CD or PCD. |
| 646 | Brain-Mediated Transfer Learning of Convolutional Neural Networks | Satoshi Nishida, Yusuke Nakano, Antoine Blanc, Naoya Maeda, Masataka Kado, Shinji Nishimoto | To examine if the internal representation of the brain could be used to achieve more efficient TL, we introduce a method for TL mediated by human brains. |
| 647 | Maximum Likelihood Embedding of Logistic Random Dot Product Graphs | Luke J. O'Connor, Muriel Medard, Soheil Feizi | Here, we consider a closely related latent space model, the Logistic RDPG, which uses a logistic link function to map from latent positions to edge likelihoods. |
| 648 | Radial and Directional Posteriors for Bayesian Deep Learning | Changyong Oh, Kamil Adamczewski, Mijung Park | We propose a new variational family for Bayesian neural networks. |
| 649 | Weighted Automata Extraction from Recurrent Neural Networks via Regression on State Spaces | Takamasa Okudono, Masaki Waga, Taro Sekiyama, Ichiro Hasuo | We present a method to extract a weighted finite automaton (WFA) from a recurrent neural network (RNN). |
| 650 | Cut-Based Graph Learning Networks to Discover Compositional Structure of Sequential Video Data | Kyoung-Woon On, Eun-Sol Kim, Yu-Jung Heo, Byoung-Tak Zhang | Here, we propose Cut-Based Graph Learning Networks (CB-GLNs) for learning video data by discovering these complex structures of the video. |
| 651 | Uncorrected Least-Squares Temporal Difference with Lambda-Return | Takayuki Osogami | We design Uncorrected LSTD(λ) in such a way that, when λ = 1, Uncorrected LSTD(1) is equivalent to the least-squares method for the linear regression of Monte Carlo (MC) return at every step, while conventional LSTD(1) has this equivalence only at the end of an episode, since the MC return is corrected to be unbiased. |
| 652 | Linear Bandits with Feature Feedback | Urvashi Oswal, Aniruddha Bhargava, Robert Nowak | This paper explores a new form of the linear bandit problem in which the algorithm receives the usual stochastic rewards as well as stochastic feedback about which features are relevant to the rewards, the latter feedback being the novel aspect. |
| 653 | Overcoming Catastrophic Forgetting by Neuron-Level Plasticity Control | Inyoung Paik, Sangjun Oh, Taeyeong Kwak, Injung Kim | To address the issue of catastrophic forgetting in neural networks, we propose a novel, simple, and effective solution called neuron-level plasticity control (NPC). |
| 654 | Adversarial Localized Energy Network for Structured Prediction | Pingbo Pan, Ping Liu, Yan Yan, Tianbao Yang, Yi Yang | To boost the efficiency and accuracy of the energy-based models on structured output prediction, we propose a novel method analogous to the adversarial learning framework. |
| 655 | Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks | Fabio Pardo, Vitaly Levdik, Petar Kormushev | To tackle this problem we propose to use convolutional network architectures to generate Q-values and updates for a large number of goals at once. |
| 656 | EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs | Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao Schardl, Charles Leiserson | To resolve this challenge, we propose EvolveGCN, which adapts the graph convolutional network (GCN) model along the temporal dimension without resorting to node embeddings. |
| 657 | Unsupervised Attributed Multiplex Network Embedding | Chanyoung Park, Donghyun Kim, Jiawei Han, Hwanjo Yu | We present a simple yet effective unsupervised network embedding method for attributed multiplex network called DMGI, inspired by Deep Graph Infomax (DGI) that maximizes the mutual information between local patches of a graph, and the global representation of the entire graph. |
| 658 | Achieving Fairness in the Stochastic Multi-Armed Bandit Problem | Vishakha Patil, Ganesh Ghalme, Vineet Nair, Y. Narahari | Our primary contribution is to obtain a complete characterization of a class of Fair-MAB algorithms via two parameters: the unfairness tolerance and the learning algorithm used as a black-box. |
| 659 | Motif-Matching Based Subgraph-Level Attentional Convolutional Network for Graph Classification | Hao Peng, Jianxin Li, Qiran Gong, Yuanxin Ning, Senzhang Wang, Lifang He | In this work, we present a novel motif-based attentional graph convolution neural network for graph classification, which can learn more discriminative and richer graph features. |
| 660 | A Bayesian Approach for Estimating Causal Effects from Observational Data | Johan Pensar, Topi Talvitie, Antti Hyttinen, Mikko Koivisto | We present a novel Bayesian method for the challenging task of estimating causal effects from passively observed data when the underlying causal DAG structure is unknown. |
| 661 | Generalized Hidden Parameter MDPs:Transferable Model-Based RL in a Handful of Trials | Christian Perez, Felipe Petroski Such, Theofanis Karaletsos | We propose Generalized Hidden Parameter MDPs (GHP-MDPs) that describe a family of MDPs where both dynamics and reward can change as a function of hidden parameters that vary across tasks. |
| 662 | CAG: A Real-Time Low-Cost Enhanced-Robustness High-Transferability Content-Aware Adversarial Attack Generator | Huy Phan, Yi Xie, Siyu Liao, Jie Chen, Bo Yuan | To address the drawbacks, we propose a Content-aware Adversarial Attack Generator (CAG) to achieve real-time, low-cost, enhanced-robustness and high-transferability adversarial attack. |
| 663 | Diversified Bayesian Nonnegative Matrix Factorization | Qiao Maoying, Yu Jun, Liu Tongliang, Wang Xinchao, Tao Dacheng | In this paper, we approach this issue using a Bayesian framework. |
| 664 | Stochastic Approximate Gradient Descent via the Langevin Algorithm | Yixuan Qiu, Xiao Wang | We introduce a novel and efficient algorithm called the stochastic approximate gradient descent (SAGD), as an alternative to the stochastic gradient descent for cases where unbiased stochastic gradients cannot be trivially obtained. |
| 665 | Temporal Network Embedding with High-Order Nonlinear Information | Zhenyu Qiu, Wenbin Hu, Jia Wu, Weiwei Liu, Bo Du, Xiaohua Jia | In this paper, we propose a high-order nonlinear information preserving (HNIP) embedding method to address these issues. |
| 666 | A New Burrows Wheeler Transform Markov Distance | Edward Raff, Charles Nicholas, Mark McLean | Prior work inspired by compression algorithms has described how the Burrows Wheeler Transform can be used to create a distance measure for bioinformatics problems. |
| 667 | How Should an Agent Practice? | Janarthanan Rajendran, Richard Lewis, Vivek Veeriah, Honglak Lee, Satinder Singh | We present a method for learning intrinsic reward functions to drive the learning of an agent during periods of practice in which extrinsic task rewards are not available. |
| 668 | Synthesizing Action Sequences for Modifying Model Decisions | Goutham Ramakrishnan, Yun Chan Lee, Aws Albarghouthi | We present a novel and general approach that combines search-based program synthesis and test-time adversarial attacks to construct action sequences over a domain-specific set of actions. |
| 669 | ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations | Ekagra Ranjan, Soumya Sanyal, Partha Talukdar | In this work, we propose ASAP (Adaptive Structure Aware Pooling), a sparse and differentiable pooling method that addresses the limitations of previous graph pooling architectures. |
| 670 | Abstract Interpretation of Decision Tree Ensemble Classifiers | Francesco Ranzato, Marco Zanella | In this work we push forward this line of research by designing a general and principled abstract interpretation-based framework for the formal verification of robustness and stability properties of decision tree ensemble models. |
| 671 | Optimizing Nondecomposable Data Dependent Regularizers via Lagrangian Reparameterization Offers Significant Performance and Efficiency Gains | Sathya N. Ravi, Abhay Venkatesh, Glenn M. Fung, Vikas Singh | We present a detailed analysis of runtime and convergence properties of our algorithm. |
| 672 | DARB: A Density-Adaptive Regular-Block Pruning for Deep Neural Networks | Ren Ao, Zhang Tao, Wang Yuhao, Lin Sheng, Dong Peiyan, Chen Yen-kuang, Xie Yuan, Wang Yanzhi | In this work, we examine the structural characteristics of the irregularly pruned weight matrices, such as the diverse redundancy of different rows, the sensitivity of different rows to pruning, and the position characteristics of retained weights. |
| 673 | Delay-Adaptive Distributed Stochastic Optimization | Zhaolin Ren, Zhengyuan Zhou, Linhai Qiu, Ajay Deshpande, Jayant Kalagnanam | The contribution of this paper is twofold. |
| 674 | Fairness for Robust Log Loss Classification | Ashkan Rezaei, Rizal Fathony, Omid Memarrast, Brian Ziebart | We present the theoretical benefits of our approach in terms of its convexity and asymptotic convergence. |
| 675 | On the Role of Weight Sharing During Deep Option Learning | Matthew Riemer, Ignacio Cases, Clemens Rosenbaum, Miao Liu, Gerald Tesauro | In this work we note that while this key assumption of the policy gradient theorems of option-critic holds in the tabular case, it is always violated in practice for the deep function approximation setting. |
| 676 | Ensembles of Locally Independent Prediction Models | Andrew Ross, Weiwei Pan, Leo Celi, Finale Doshi-Velez | To address this issue, we introduce a new diversity metric and associated method of training ensembles of models that extrapolate differently on local patches of the data manifold. |
| 677 | Actionable Ethics through Neural Learning | Daniele Rossini, Danilo Croce, Sara Mancini, Massimo Pellegrino, Roberto Basili | For this reason we introduce the notion of Embedding Principles of ethics by Design (EPbD) as a comprehensive inductive framework. |
| 678 | Generative Continual Concept Learning | Mohammad Rostami, Soheil Kolouri, Praveen Pilly, James McClelland | Inspired by the Parallel Distributed Processing learning and the Complementary Learning Systems theories, we develop a computational model that is able to expand its previously learned concepts efficiently to new domains using a few labeled samples. |
| 679 | Linear Context Transform Block | Dongsheng Ruan, Jun Wen, Nenggan Zheng, Min Zheng | Through linear transform of the normalized context features, we model global context for each channel independently. |
| 680 | Chained Representation Cycling: Learning to Estimate 3D Human Pose and Shape by Cycling Between Representations | Nadine Rueegg, Christoph Lassner, Michael Black, Konrad Schindler, Nadine Rueegg, Christoph Lassner, Michael Black, Konrad Schindler | To go one step further, we propose a new architecture that facilitates unsupervised, or lightly supervised, learning. |
| 681 | Weakly Supervised Sequence Tagging from Noisy Rules | Esteban Safranchik, Shiying Luo, Stephen Bach | We propose a framework for training sequence tagging models with weak supervision consisting of multiple heuristic rules of unknown accuracy. |
| 682 | Random Intersection Graphs and Missing Data | Dror Salti, Yakir Berchenko | In this paper we demonstrate the relationship between these two different topics and take a novel view of the data matrix as a random intersection graph. |
| 683 | Rank3DGAN: Semantic Mesh Generation Using Relative Attributes | Yassir Saquil, Qun-Ce Xu, Yong-Liang Yang, Peter Hall | In this paper, we investigate a novel problem of using generative adversarial networks in the task of 3D shape generation according to semantic attributes. |
| 684 | Weighted Sampling for Combined Model Selection and Hyperparameter Tuning | Dimitrios Sarigiannis, Thomas Parnell, Haralampos Pozidis | In this work, we propose a novel sampling distribution as an alternative to uniform sampling and prove theoretically that it has a better chance of finding the best configuration in a worst-case setting. |
| 685 | Graph Representation Learning via Ladder Gamma Variational Autoencoders | Arindam Sarkar, Nikhil Mehta, Piyush Rai | We present a probabilistic framework for community discovery and link prediction for graph-structured data, based on a novel, gamma ladder variational autoencoder (VAE) architecture. |
| 686 | Learning Counterfactual Representations for Estimating Individual Dose-Response Curves | Patrick Schwab, Lorenz Linhardt, Stefan Bauer, Joachim M. Buhmann, Walter Karlen | Here, we present a novel machine-learning approach towards learning counterfactual representations for estimating individual dose-response curves for any number of treatments with continuous dosage parameters with neural networks. Building on the established potential outcomes framework, we introduce performance metrics, model selection criteria, model architectures, and open benchmarks for estimating individual dose-response curves. |
| 687 | Uncertainty-Aware Deep Classifiers Using Generative Models | Murat Sensoy, Lance Kaplan, Federico Cerutti, Maryam Saleki | In this work we develop a novel neural network model that is able to express both aleatoric and epistemic uncertainty to distinguish decision boundary and out-of-distribution regions of the feature space. |
| 688 | Empirical Bounds on Linear Regions of Deep Rectifier Networks | Thiago Serra, Srikumar Ramalingam | In this work, we approximate the number of linear regions through empirical bounds based on features of the trained network and probabilistic inference. |
| 689 | Universal Adversarial Training | Ali Shafahi, Mahyar Najibi, Zheng Xu, John Dickerson, Larry S. Davis, Tom Goldstein | We propose a simple optimization-based universal attack that reduces the top-1 accuracy of various network architectures on ImageNet to less than 20%, while learning the universal perturbation 13× faster than the standard method. |
| 690 | Sequential Mode Estimation with Oracle Queries | Dhruti Shah, Tuhinangshu Choudhury, Nikhil Karamchandani, Aditya Gopalan | We consider the problem of adaptively PAC-learning a probability distribution 𝒫’s mode by querying an oracle for information about a sequence of i.i.d. samples X1, X2, … generated from 𝒫. |
| 691 | Online Active Learning of Reject Option Classifiers | Kulin Shah, Naresh Manwani | In this paper, we propose novel algorithms for active learning of reject option classifiers. |
| 692 | Improved PAC-Bayesian Bounds for Linear Regression | Vera Shalaeva, Alireza Fakhrizadeh Esfahani, Pascal Germain, Mihaly Petreczky | In this paper, we improve the PAC-Bayesian error bound for linear regression derived in Germain et al. (2016). |
| 693 | Adaptive Trust Region Policy Optimization: Global Convergence and Faster Rates for Regularized MDPs | Lior Shani, Yonathan Efroni, Shie Mannor | Nevertheless, TRPO has been considered a heuristic algorithm inspired by Conservative Policy Iteration (CPI). |
| 694 | Transfer Value Iteration Networks | Junyi Shen, Hankz Hankui Zhuo, Jin Xu, Bin Zhong, Sinno Pan | In this paper, we propose a transfer learning approach on top of VINs, termed Transfer VINs (TVINs), such that a learned policy from a source domain can be generalized to a target domain with only limited training data, even if the source domain and the target domain have domain-specific actions and features. |
| 695 | AUC Optimization with a Reject Option | Song-Qing Shen, Bin-Bin Yang, Wei Gao | In this work, we propose the framework of AUC optimization with a reject option, and the basic idea is to withhold the decision of ranking a pair of positive and negative instances with a lower cost, rather than mis-ranking. |
| 696 | Stable Learning via Sample Reweighting | Zheyan Shen, Peng Cui, Tong Zhang, Kun Kunag | In this paper we theoretically analyze this fundamental problem and propose a sample reweighting method that reduces collinearity among input variables. |
| 697 | Fractional Skipping: Towards Finer-Grained Dynamic CNN Inference | Jianghao Shen, Yue Wang, Pengfei Xu, Yonggan Fu, Zhangyang Wang, Yingyan Lin | We therefore propose a Dynamic Fractional Skipping (DFS) framework. |
| 698 | Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning | Kekai Sheng, Weiming Dong, Menglei Chai, Guohui Wang, Peng Zhou, Feiyue Huang, Bao-Gang Hu, Rongrong Ji, Chongyang Ma | In this paper, we revisit the problem of image aesthetic assessment from the self-supervised feature learning perspective. |
| 699 | Gamma-Nets: Generalizing Value Estimation over Timescale | Craig Sherstan, Shibhansh Dohare, James MacGlashan, Johannes Günther, Patrick M. Pilarski | In this paper we present Γ-nets, a method for generalizing value function estimation over timescale, allowing a given GVF to be trained and queried for arbitrary timescales so as to greatly increase the predictive ability and scalability of a GVF-based model. |
| 700 | Deep Time-Stream Framework for Click-through Rate Prediction by Tracking Interest Evolution | Shu-Ting Shi, Wenhao Zheng, Jun Tang, Qing-Guo Chen, Yao Hu, Jianke Zhu, Ming Li | In this paper, we propose a novel Deep Time-Stream framework (DTS) which introduces the time information by an ordinary differential equations (ODE). |
| 701 | Quadruply Stochastic Gradient Method for Large Scale Nonlinear Semi-Supervised Ordinal Regression AUC Optimization | Wanli Shi, Bin Gu, Xiang Li, Heng Huang | In this paper, we propose an unbiased objective function for S2OR AUC optimization based on ordinal binary decomposition approach. |
| 702 | Loss-Based Attention for Deep Multiple Instance Learning | Xiaoshuang Shi, Fuyong Xing, Yuanpu Xie, Zizhao Zhang, Lei Cui, Lin Yang | To alleviate this issue, in this paper, we propose a novel loss based attention mechanism, which simultaneously learns instance weights and predictions, and bag predictions for deep multiple instance learning. |
| 703 | Deep Message Passing on Sets | Yifeng Shi, Junier Oliva, Marc Niethammer | In this work we introduce Deep Message Passing on Sets (DMPS), a novel method that incorporates relational learning for sets. |
| 704 | Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting | Qiquan Shi, Jiaming Yin, Jiajun Cai, Andrzej Cichocki, Tatsuya Yokota, Lei Chen, Mingxuan Yuan, Jia Zeng | This work proposes a novel approach for multiple time series forecasting. |
| 705 | Morphism-Based Learning for Structured Data | Kilho Shin, Dave Shepard | In this paper, we propose a generic and theoretic framework to investigate similarity of structured data through structure-preserving one-to-one partial mappings, which we call morphisms. |
| 706 | Hierarchically Clustered Representation Learning | Su-Jin Shin, Kyungwoo Song, Il-Chul Moon | To overcome the limitations of flat clustering, we introduce hierarchically-clustered representation learning (HCRL), which simultaneously optimizes representation learning and hierarchical clustering in the embedding space. |
| 707 | HLHLp: Quantized Neural Networks Training for Reaching Flat Minima in Loss Surface | Sungho Shin, Jinhwan Park, Yoonho Boo, Wonyong Sung | We propose a novel training scheme for quantized neural networks to reach flat minima in the loss surface with the aid of quantization noise. |
| 708 | Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents | Felipe Leno Da Silva, Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor | In this work, we propose Requesting Confidence-Moderated Policy advice (RCMP), an action-advising framework where the agent asks for advice when its epistemic uncertainty is high for a certain state. |
| 709 | Efficient Facial Feature Learning with Wide Ensemble-Based Convolutional Neural Networks | Henrique Siqueira, Sven Magg, Stefan Wermter | In this paper, we present experiments on Ensembles with Shared Representations (ESRs) based on convolutional networks to demonstrate, quantitatively and qualitatively, their data processing efficiency and scalability to large-scale datasets of facial expressions. |
| 710 | Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers | Masoumeh Soflaei, Hongyu Guo, Ali Al-Bashabsheh, Yongyi Mao, Richong Zhang | We consider the problem of learning a neural network classifier. |
| 711 | Bivariate Beta-LSTM | Kyungwoo Song, JoonHo Jang, Seung jae Shin, Il-Chul Moon | This paper proposes a new gate structure with the bivariate Beta distribution. |
| 712 | Mega-Reward: Achieving Human-Level Play without Extrinsic Rewards | Yuhang Song, Jianyi Wang, Thomas Lukasiewicz, Zhenghua Xu, Shangtong Zhang, Andrzej Wojcicki, Mai Xu | In this work, we propose a novel megalomania-driven intrinsic reward (called mega-reward), which, to our knowledge, is the first approach that achieves human-level performance in intrinsically-motivated play. |
| 713 | Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip | Yuxuan Song, Minkai Xu, Lantao Yu, Hao Zhou, Shuo Shao, Yong Yu | In this paper, motivated by the inherent connections between neural joint source-channel coding and discrete representation learning, we propose a novel regularization method called Infomax Adversarial-Bit-Flip (IABF) to improve the stability and robustness of the neural joint source-channel coding scheme. |
| 714 | Benign Examples: Imperceptible Changes Can Enhance Image Translation Performance | Vignesh Srinivasan, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima | In this paper, we propose to perform Langevin dynamics, which makes a subtle change in the input space bringing them close to the data manifold, producing benign examples. |
| 715 | Scalable Probabilistic Matrix Factorization with Graph-Based Priors | Jonathan Strahl, Jaakko Peltonen, Hirsohi Mamitsuka, Samuel Kaski | We show that removing these contested edges improves prediction accuracy and scalability. |
| 716 | Learning Efficient Representations for Fake Speech Detection | Nishant Subramani, Delip Rao | In this paper, we focus on: 1) How can we build highly accurate, yet parameter and sample-efficient models for fake speech detection? |
| 717 | Lifelong Spectral Clustering | Gan Sun, Yang Cong, Qianqian Wang, Jun Li, Yun Fu | In this paper, we aim to explore the problem of spectral clustering in a lifelong machine learning framework, i.e., Lifelong Spectral Clustering (L2SC). |
| 718 | New Interpretations of Normalization Methods in Deep Learning | Jiacheng Sun, Xiangyong Cao, Hanwen Liang, Weiran Huang, Zewei Chen, Zhenguo Li | New Interpretations of Normalization Methods in Deep Learning |
| 719 | Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning | Jianwen Sun, Tianwei Zhang, Xiaofei Xie, Lei Ma, Yan Zheng, Kangjie Chen, Yang Liu | In this paper, we introduce two novel adversarial attack techniques to stealthily and efficiently attack the DRL agents. |
| 720 | Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes | Ke Sun, Zhouchen Lin, Zhanxing Zhu | In this paper, we propose a novel training algorithm for Graph Convolutional Network, called Multi-Stage Self-Supervised (M3S) Training Algorithm, combined with self-supervised learning approach, focusing on improving the generalization performance of GCNs on graphs with few labeled nodes. |
| 721 | Attentive Experience Replay | Peiquan Sun, Wengang Zhou, Houqiang Li | We introduce Attentive Experience Replay (AER), a novel experience replay algorithm that samples transitions according to the similarities between their states and the agent’s state. |
| 722 | Revisiting Probability Distribution Assumptions for Information Theoretic Feature Selection | Yuan Sun, Wei Wang, Michael Kirley, Xiaodong Li, Jeffrey Chan | In this paper, we reveal two sets of distribution assumptions underlying many MI and VI based methods: Feature Independence Distribution and Geometric Mean Distribution. |
| 723 | Adversarial Transformations for Semi-Supervised Learning | Teppei Suzuki, Ikuro Sato | We propose a Regularization framework based on Adversarial Transformations (RAT) for semi-supervised learning. |
| 724 | CGD: Multi-View Clustering via Cross-View Graph Diffusion | Chang Tang, Xinwang Liu, Xinzhong Zhu, En Zhu, Zhigang Luo, Lizhe Wang, Wen Gao | In this work, we propose a general, effective and parameter-free method with convergence guarantee to learn a unified graph for multi-view data clustering via cross-view graph diffusion (CGD), which is the first attempt to employ diffusion process for multi-view clustering. |
| 725 | Label Enhancement with Sample Correlations via Low-Rank Representation | Haoyu Tang, Jihua Zhu, Qinghai Zheng, Jun Wang, Shanmin Pang, Zhongyu Li | To handle this problem, a novel label enhancement method, Label Enhancement with Sample Correlations via low-rank representation, is proposed in this paper. |
| 726 | Discriminative Adversarial Domain Adaptation | Hui Tang, Kui Jia | To overcome it, we propose a novel adversarial learning method termed Discriminative Adversarial Domain Adaptation (DADA). |
| 727 | Parameterized Indexed Value Function for Efficient Exploration in Reinforcement Learning | Tian Tan, Zhihan Xiong, Vikranth R. Dwaracherla | In this paper, we present an alternative, computationally efficient way to induce exploration using index sampling. |
| 728 | Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values | Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Charu Aggarwal, Prasenjit Mitra, Suhang Wang | We propose a new framework øurs, which leverages memory network to explore global patterns given estimations from local perspectives. |
| 729 | Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks | Yehui Tang, Yunhe Wang, Yixing Xu, Boxin Shi, Chao Xu, Chunjing Xu, Chang Xu | Therefore, we investigate the empirical Rademacher complexity related to intermediate layers of deep neural networks and propose a feature distortion method for addressing the aforementioned problem. |
| 730 | Reborn Filters: Pruning Convolutional Neural Networks with Limited Data | Yehui Tang, Shan You, Chang Xu, Jin Han, Chen Qian, Boxin Shi, Chao Xu, Changshui Zhang | In this paper, for pruning with limited data, we propose to use all original filters to directly develop new compact filters, named reborn filters, so that all useful structure priors in the original filters can be well preserved into the pruned networks, alleviating the performance drop accordingly. |
| 731 | Discretizing Continuous Action Space for On-Policy Optimization | Yunhao Tang, Shipra Agrawal | In this work, we show that discretizing action space for continuous control is a simple yet powerful technique for on-policy optimization. |
| 732 | Bi-Objective Continual Learning: Learning ‘New’ While Consolidating ‘Known’ | Xiaoyu Tao, Xiaopeng Hong, Xinyuan Chang, Yihong Gong | In this paper, we propose a novel single-task continual learning framework named Bi-Objective Continual Learning (BOCL). |
| 733 | Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression | Tong Teng, Jie Chen, Yehong Zhang, Bryan Kian Hsiang Low | This paper presents a variational Bayesian kernel selection (VBKS) algorithm for sparse Gaussian process regression (SGPR) models. |
| 734 | Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors | Jayaraman J. Thiagarajan, Bindya Venkatesh, Prasanna Sattigeri, Peer-Timo Bremer | More specifically, we use separate models for prediction and interval estimation, and pose a bi-level optimization problem that allows the former to leverage estimates from the latter through an uncertainty matching strategy. |
| 735 | Network as Regularization for Training Deep Neural Networks: Framework, Model and Performance | Kai Tian, Yi Xu, Jihong Guan, Shuigeng Zhou | To alleviate the over-fitting problem, here we propose a new and general regularization framework that introduces an auxiliary network to dynamically incorporate guided semantic disturbance to the labels. |
| 736 | Sanity Checks for Saliency Metrics | Richard Tomsett, Dan Harborne, Supriyo Chakraborty, Prudhvi Gurram, Alun Preece | Saliency maps are a popular approach to creating post-hoc explanations of image classifier outputs. |
| 737 | Differential Equation Units: Learning Functional Forms of Activation Functions from Data | MohamadAli Torkamani, Shiv Shankar, Amirmohammad Rooshenas, Phillip Wallis | We introduce differential equation units (DEUs), an improvement to modern neural networks, which enables each neuron to learn a particular nonlinear activation function from a family of solutions to an ordinary differential equation. |
| 738 | Order-Free Learning Alleviating Exposure Bias in Multi-Label Classification | Che-Ping Tsai, Hung-Yi Lee | In this paper, we propose a new framework for MLC which does not rely on a predefined label order and thus alleviates exposure bias. |
| 739 | Learning to Crawl | Utkarsh Upadhyay, Robert Busa-Fekete, Wojciech Kotlowski, David Pal, Balazs Szorenyi | In this paper, we study the same control problem but under the assumption that the change rates are unknown a priori, and thus we need to estimate them in an online fashion using only partial observations (i.e., single-bit signals indicating whether the page has changed since the last refresh). |
| 740 | Transfer Learning for Anomaly Detection through Localized and Unsupervised Instance Selection | Vercruyssen Vincent, Meert Wannes, Davis Jesse | This paper proposes a novel transfer learning algorithm for anomaly detection that selects and transfers relevant labeled instances from a source anomaly detection task to a target one. |
| 741 | Meta-Learning for Generalized Zero-Shot Learning | Vinay Kumar Verma, Dhanajit Brahma, Piyush Rai | In this paper, we propose a meta-learning based generative model that naturally handles these limitations. |
| 742 | Deep Conservative Policy Iteration | Nino Vieillard, Olivier Pietquin, Matthieu Geist | In this paper, we show how CPI can be practically combined with deep RL with discrete actions, in an off-policy manner. |
| 743 | Justification-Based Reliability in Machine Learning | Nurali Virani, Naresh Iyer, Zhaoyuan Yang | We present an analysis of neural network classifiers linking the reliability of its prediction on a test input to characteristics of the support gathered from the input and hidden layers of the network. |
| 744 | Fast and Efficient Boolean Matrix Factorization by Geometric Segmentation | Changlin Wan, Wennan Chang, Tong Zhao, Mengya Li, Sha Cao, Chi Zhang | Inspired by binary matrix permutation theories and geometric segmentation, we developed a fast and efficient BMF approach, called MEBF (Median Expansion for Boolean Factorization). |
| 745 | Reinforcement Learning Based Meta-Path Discovery in Large-Scale Heterogeneous Information Networks | Guojia Wan, Bo Du, Shirui Pan, Gholameza Haffari | In this work, we present a novel framework, Meta-path Discovery with Reinforcement Learning (MPDRL), to identify informative meta-paths from complex and large-scale HINs. |
| 746 | Robust Tensor Decomposition via Orientation Invariant Tubal Nuclear Norms | Andong Wang, Chao Li, Zhong Jin, Qibin Zhao | To this end, we introduce two new tensor norms called OITNN-O and OITNN-L to exploit multi-orientational spectral low-rankness for an arbitrary K-way (K ≥ 3) tensors. |
| 747 | Robust Self-Weighted Multi-View Projection Clustering | Beilei Wang, Yun Xiao, Zhihui Li, Xuanhong Wang, Xiaojiang Chen, Dingyi Fang | Aiming at this problem, in this paper, we propose a Robust Self-weighted Multi-view Projection Clustering (RSwMPC) based on ℓ2,1-norm, which can simultaneously reduce dimensionality, suppress noise and learn local structure graph. |
| 748 | Learning General Latent-Variable Graphical Models with Predictive Belief Propagation | Borui Wang, Geoffrey Gordon | In order to overcome these limitations, in this paper we introduce a novel formulation of message-passing inference over junction trees named predictive belief propagation, and propose a new learning and inference algorithm for general latent-variable graphical models based on this formulation. |
| 749 | SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback | Chao Wang, Hengshu Zhu, Chen Zhu, Chuan Qin, Hui Xiong | To this end, in this paper, we propose a novel setwise Bayesian approach for collaborative ranking, namely SetRank, to inherently accommodate the characteristics of implicit feedback in recommender system. |
| 750 | Estimating Stochastic Linear Combination of Non-Linear Regressions | Di Wang, Xiangyu Guo, Chaowen Guan, Shi Li, Jinhui Xu | In this paper we study the problem of estimating stochastic linear combination of non-linear regressions, which has a close connection with many machine learning and statistical models such as non-linear regressions, the Single Index, Multi-index, Varying Coefficient Index Models and Two-layer Neural Networks. |
| 751 | Compact Autoregressive Network | Di Wang, Feiqing Huang, Jingyu Zhao, Guodong Li, Guangjian Tian | Theoretical studies show that the TAR net improves the learning efficiency, and requires much fewer samples for model training. |
| 752 | Neural Cognitive Diagnosis for Intelligent Education Systems | Fei Wang, Qi Liu, Enhong Chen, Zhenya Huang, Yuying Chen, Yu Yin, Zai Huang, Shijin Wang | In this paper, we propose a general Neural Cognitive Diagnosis (NeuralCD) framework, which incorporates neural networks to learn the complex exercising interactions, for getting both accurate and interpretable diagnosis results. |
| 753 | Adapting to Smoothness: A More Universal Algorithm for Online Convex Optimization | Guanghui Wang, Shiyin Lu, Yao Hu, Lijun Zhang | In this paper, we provide an affirmative answer by developing a novel algorithm, namely UFO, which achieves O(√L*), O(d log L*) and O(log L*) regret bounds for the three types of loss functions respectively under the assumption of smoothness, where L* is the cumulative loss of the best comparator in hindsight, and d is dimensionality. |
| 754 | Repetitive Reprediction Deep Decipher for Semi-Supervised Learning | Guo-Hua Wang, Jianxin Wu | In this paper, we propose a principled end-to-end framework named deep decipher (D2) for SSL. |
| 755 | Incorporating Label Embedding and Feature Augmentation for Multi-Dimensional Classification | Haobo Wang, Chen Chen, Weiwei Liu, Ke Chen, Tianlei Hu, Gang Chen | To fill this gap, a novel neural network based model is proposed which seamlessly integrates the Label Embedding and Feature Augmentation (LEFA) techniques to learn label correlations. |
| 756 | M-NAS: Meta Neural Architecture Search | Jiaxing Wang, Jiaxiang Wu, Haoli Bai, Jian Cheng | In this paper, we investigate a previously unexplored problem: whether a universal NAS method exists, such that task-aware architectures can be effectively generated? |
| 757 | Logo-2K+: A Large-Scale Logo Dataset for Scalable Logo Classification | Jing Wang, Weiqing Min, Sujuan Hou, Shengnan Ma, Yuanjie Zheng, Haishuai Wang, Shuqiang Jiang | Logo-2K+: A Large-Scale Logo Dataset for Scalable Logo Classification |
| 758 | Reinforcement Learning with Perturbed Rewards | Jingkang Wang, Yang Liu, Bo Li | In this paper, we consider noisy RL problems with perturbed rewards, which can be approximated with a confusion matrix. |
| 759 | Crowdfunding Dynamics Tracking: A Reinforcement Learning Approach | Jun Wang, Hefu Zhang, Qi Liu, Zhen Pan, Hanqing Tao | To address the problem, in this paper, we propose a Trajectory-based Continuous Control for Crowdfunding (TC3) algorithm to predict the funding progress in crowdfunding. |
| 760 | Differentially Private Learning with Small Public Data | Jun Wang, Zhi-Hua Zhou | In this paper, we study a common situation where a small amount of public data can be used when solving the Empirical Risk Minimization problem over a private database. |
| 761 | Dual Relation Semi-Supervised Multi-Label Learning | Lichen Wang, Yunyu Liu, Can Qin, Gan Sun, Yun Fu | To this end, we proposed a Dual Relation Semi-supervised Multi-label Learning (DRML) approach which jointly explores the feature distribution and the label relation simultaneously. |
| 762 | A Knowledge Transfer Framework for Differentially Private Sparse Learning | Lingxiao Wang, Quanquan Gu | We study the problem of estimating high dimensional models with underlying sparse structures while preserving the privacy of each training example. |
| 763 | Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling | Qian Wang, Toby Breckon | In this paper, we propose a novel selective pseudo-labeling strategy based on structured prediction. |
| 764 | Learning from Weak-Label Data: A Deep Forest Expedition | Qian-Wei Wang, Liang Yang, Yu-Feng Li | In this paper, we propose LCForest, which is the first tree ensemble based deep learning method for weak-label learning. |
| 765 | Intention Nets: Psychology-Inspired User Choice Behavior Modeling for Next-Basket Prediction | Shoujin Wang, Liang Hu, Yan Wang, Quan Z. Sheng, Mehmet Orgun, Longbing Cao | In this paper, we take user choices for shopping baskets as a typical case to study the complexity of user behaviors. |
| 766 | Multi-Component Graph Convolutional Collaborative Filtering | Xiao Wang, Ruijia Wang, Chuan Shi, Guojie Song, Qingyong Li | Therefore, in this paper we propose a novel Multi-Component graph convolutional Collaborative Filtering (MCCF) approach to distinguish the latent purchasing motivations underneath the observed explicit user-item interactions. |
| 767 | Attention-Guide Walk Model in Heterogeneous Information Network for Multi-Style Recommendation Explanation | Xin Wang, Ying Wang, Yunzhi Ling | To address these issues, we propose a framework (MSRE) of generating the multi-style recommendation explanation with the attention-guide walk model on affiliation relations and interaction relations in the heterogeneous information network. |
| 768 | Federated Latent Dirichlet Allocation: A Local Differential Privacy Based Framework | Yansheng Wang, Yongxin Tong, Dingyuan Shi | To this end, we propose FedLDA, a local differential privacy (LDP) based framework for federated learning of LDA models. |
| 769 | Transductive Ensemble Learning for Neural Machine Translation | Yiren Wang, Lijun Wu, Yingce Xia, Tao Qin, ChengXiang Zhai, Tie-Yan Liu | In this paper, we study how to effectively aggregate multiple NMT models under the transductive setting where the source sentences of the test set are known. |
| 770 | Dynamic Network Pruning with Interpretable Layerwise Channel Selection | Yulong Wang, Xiaolu Zhang, Xiaolin Hu, Bo Zhang, Hang Su | In this paper, we propose to explicitly model the discrete weight channel selections, which encourages more diverse weights utilization, and achieves more sparse runtime inference paths. |
| 771 | An Objective for Hierarchical Clustering in Euclidean Space and Its Connection to Bisecting K-means | Yuyan Wang, Benjamin Moseley | Motivated by this, the paper develops a new global objective for hierarchical clustering in Euclidean space. |
| 772 | Non-Local U-Nets for Biomedical Image Segmentation | Zhengyang Wang, Na Zou, Dinggang Shen, Shuiwang Ji | In this work, we propose the non-local U-Nets, which are equipped with flexible global aggregation blocks, for biomedical image segmentation. |
| 773 | Attention-over-Attention Field-Aware Factorization Machine | Zhibo Wang, Jinxin Ma, Yongquan Zhang, Qian Wang, Ju Ren, Peng Sun | In this paper, we present a novel algorithm called Attention-over-Attention Field-aware Factorization Machine (AoAFFM) for better capturing the characteristics of feature interactions. |
| 774 | Transparent Classification with Multilayer Logical Perceptrons and Random Binarization | Zhuo Wang, Wei Zhang, Ning LIU, Jianyong Wang | In this paper, we propose a new hierarchical rule-based model for classification tasks, named Concept Rule Sets (CRS), which has both a strong expressive ability and a transparent inner structure. |
| 775 | Less Is Better: Unweighted Data Subsampling via Influence Function | Zifeng Wang, Hong Zhu, Zhenhua Dong, Xiuqiang He, Shao-Lun Huang | In this work, we propose a novel Unweighted Influence Data Subsampling (UIDS) method, and prove that the subset-model acquired through our method can outperform the full-set-model. |
| 776 | Multi-View Multiple Clusterings Using Deep Matrix Factorization | Shaowei Wei, Jun Wang, Guoxian Yu, Carlotta Domeniconi, Xiangliang Zhang | In this paper, we introduce a deep matrix factorization based solution (DMClusts) to discover multiple clusterings. |
| 777 | Towards Certificated Model Robustness Against Weight Perturbations | Tsui-Wei Weng, Pu Zhao, Sijia Liu, Pin-Yu Chen, Xue Lin, Luca Daniel | We propose an efficient approach to compute a certified robustness bound of weight perturbations, within which neural networks will not make erroneous outputs as desired by the adversary. |
| 778 | ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems | Philippe Wenk, Gabriele Abbati, Michael A. Osborne, Bernhard Schölkopf, Andreas Krause, Stefan Bauer | In this work, we introduce a novel generative modeling approach based on constrained Gaussian processes and leverage it to build a computationally and data efficient algorithm for state and parameter inference. |
| 779 | Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics | Bingzhe Wu, Chaochao Chen, Shiwan Zhao, Cen Chen, Yuan Yao, Guangyu Sun, Li Wang, Xiaolu Zhang, Jun Zhou | In this paper, we study the properties of SGLD from a novel perspective of membership privacy protection (i.e., preventing the membership attack). |
| 780 | Vector Quantization-Based Regularization for Autoencoders | Hanwei Wu, Markus Flierl | In this paper, we introduce a quantization-based regularizer in the bottleneck stage of autoencoder models to learn meaningful latent representations. |
| 781 | Unified Graph and Low-Rank Tensor Learning for Multi-View Clustering | Jianlong Wu, Xingyu Xie, Liqiang Nie, Zhouchen Lin, Hongbin Zha | Towards these two issues, in this paper, we propose the unified graph and low-rank tensor learning (UGLTL) for multi-view clustering. |
| 782 | Estimating Early Fundraising Performance of Innovations via Graph-Based Market Environment Model | Likang Wu, Zhi Li, Hongke Zhao, Zhen Pan, Qi Liu, Enhong Chen | Specifically, we propose a Graph-based Market Environment model (GME) for estimating the early fundraising performance of the target project by exploiting the market environment. |
| 783 | Meta-Amortized Variational Inference and Learning | Mike Wu, Kristy Choi, Noah Goodman, Stefano Ermon | In this work, we present a doubly-amortized variational inference procedure as a way to address this challenge. |
| 784 | Regional Tree Regularization for Interpretability in Deep Neural Networks | Mike Wu, Sonali Parbhoo, Michael Hughes, Ryan Kindle, Leo Celi, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez | To address this issue, we propose regional tree regularization – a method that encourages a deep model to be well-approximated by several separate decision trees specific to predefined regions of the input space. |
| 785 | SK-Net: Deep Learning on Point Cloud via End-to-End Discovery of Spatial Keypoints | Weikun Wu, Yan Zhang, David Wang, Yunqi Lei | This paper presents an end-to-end framework, SK-Net, to jointly optimize the inference of spatial keypoint with the learning of feature representation of a point cloud for a specific point cloud task. |
| 786 | Multi-Label Causal Feature Selection | Xingyu Wu, Bingbing Jiang, Kui Yu, Huanhuan Chen, Chunyan Miao | To address these problems, in this paper, we theoretically study the causal relationships in multi-label data, and propose a novel Markov blanket based multi-label causal feature selection (MB-MCF) algorithm. |
| 787 | Dual Adversarial Co-Learning for Multi-Domain Text Classification | Yuan Wu, Yuhong Guo | In order to address these issues, in this paper we propose a novel dual adversarial co-learning approach for multi-domain text classification (MDTC). |
| 788 | Efficient Projection-Free Online Methods with Stochastic Recursive Gradient | Jiahao Xie, Zebang Shen, Chao Zhang, Boyu Wang, Hui Qian | To fill this gap, two efficient projection-free online methods called ORGFW and MORGFW are proposed for solving stochastic and adversarial OCO problems, respectively. |
| 789 | Partial Multi-Label Learning with Noisy Label Identification | Ming-Kun Xie, Sheng-Jun Huang | Based on this observation, we propose a partial multi-label learning approach to simultaneously recover the ground-truth information and identify the noisy labels. |
| 790 | Infinite ShapeOdds: Nonparametric Bayesian Models for Shape Representations | Wei Xing, Shireen Elhabian, Robert Kirby, Ross T. Whitaker, Shandian Zhe | To address these issues, we propose Infinite ShapeOdds (InfShapeOdds), a Bayesian nonparametric shape model, which is flexible enough to capture complex shape variations and discover hidden cluster structures, while still avoiding overfitting. |
| 791 | Learning Feature Interactions with Lorentzian Factorization Machine | Canran Xu, Ming Wu | In this paper, we propose a new model named “LorentzFM” that can learn feature interactions embedded in a hyperbolic space in which the violation of triangle inequality for Lorentz distances is available. |
| 792 | Gromov-Wasserstein Factorization Models for Graph Clustering | Hongtengl Xu | We propose a new nonlinear factorization model for graphs that are with topological structures, and optionally, node attributes. |
| 793 | Federated Patient Hashing | Jie Xu, Zhenxing Xu, Peter Walker, Fei Wang | To address these challenges, in this paper, we propose a Federated Patient Hashing (FPH) framework, which collaboratively trains a retrieval model stored in a shared memory while keeping all the patient-level information in local institutions. |
| 794 | Deep Embedded Complementary and Interactive Information for Multi-View Classification | Jinglin Xu, Wenbin Li, Xinwang Liu, Dingwen Zhang, Ji Liu, Junwei Han | In this work, we propose a novel multi-view learning framework that seamlessly embeds various view-specific information and deep interactive information and introduces a novel multi-view fusion strategy to make a joint decision during the optimization for classification. |
| 795 | Adversarial Domain Adaptation with Domain Mixup | Minghao Xu, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, Wenjun Zhang | In this paper, we present adversarial domain adaptation with domain mixup (DM-ADA), which guarantees domain-invariance in a more continuous latent space and guides the domain discriminator in judging samples’ difference relative to source and target domains. |
| 796 | Partial Multi-Label Learning with Label Distribution | Ning Xu, Yun-Peng Liu, Xin Geng | In this paper, a new partial multi-label learning strategy named Pml-ld is proposed to learn from partial multi-label examples via label enhancement. |
| 797 | Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests | Xiao Xu, Fang Dong, Yanghua Li, Shaojian He, Xin Li | An efficient learning algorithm that is adaptive to abrupt reward changes is proposed and theoretical regret analysis is provided to show that a sublinear scaling of regret in the time length T is achieved. |
| 798 | Generative-Discriminative Complementary Learning | Yanwu Xu, Mingming Gong, Junxiang Chen, Tongliang Liu, Kun Zhang, Kayhan Batmanghelich | In this paper, we study the complementary learning problem. |
| 799 | To Avoid the Pitfall of Missing Labels in Feature Selection: A Generative Model Gives the Answer | Yuanyuan Xu, Jun Wang, Jinmao Wei | To avoid the pitfall of missing labels, a novel unified framework of selecting discriminative features and modeling incomplete label matrix is proposed from a generative point of view in this paper. |
| 800 | Light Multi-Segment Activation for Model Compression | Zhenhui Xu, Guolin Ke, Jia Zhang, Jiang Bian, Tie-Yan Liu | Inspired by the nature of the expressiveness ability in NN, we propose to use multi-segment activation, which can significantly improve the expressiveness ability with very little cost, in the compact student model. |
| 801 | Not All Attention Is Needed: Gated Attention Network for Sequence Data | Lanqing Xue, Xiaopeng Li, Nevin L. Zhang | In this paper, we combine the two dynamic mechanisms for text classification tasks. |
| 802 | One-Shot Image Classification by Learning to Restore Prototypes | Wanqi Xue, Wei Wang | In this paper, we adopt metric learning for this problem, which has been applied for few- and many-shot image classification by comparing the distance between the test image and the center of each class in the feature space. |
| 803 | Effective Data Augmentation with Multi-Domain Learning GANs | Shin'ya Yamaguchi, Sekitoshi Kanai, Takeharu Eda | In this work, we propose an effective data augmentation method based on generative adversarial networks (GANs), called Domain Fusion. |
| 804 | Partial Label Learning with Batch Label Correction | Yan Yan, Yuhong Guo | In this paper, we propose a simple but effective batch-based partial label learning algorithm named PL-BLC, which tackles the partial label learning problem with batch-wise label correction (BLC). |
| 805 | Active Learning with Query Generation for Cost-Effective Text Classification | Yi-Fan Yan, Sheng-Jun Huang, Shaoyi Chen, Meng Liao, Jin Xu | In this paper, we propose an active learning approach for text classification with lower annotation cost. |
| 806 | Towards Accurate Low Bit-Width Quantization with Multiple Phase Adaptations | Zhaoyi Yan, Yemin Shi, Yaowei Wang, Mingkui Tan, Zheyang Li, Wenming Tan, Yonghong Tian | Accordingly, in this paper, we propose Multiple Phase Adaptations (MPA), a framework designed to address these two problems. |
| 807 | Variational Adversarial Kernel Learned Imitation Learning | Fan Yang, Alina Vereshchaka, Yufan Zhou, Changyou Chen, Wen Dong | To this end, we propose the variational adversarial kernel learned imitation learning (VAKLIL), which measures the distance using the maximum mean discrepancy with variational kernel learning. |
| 808 | Revisiting Online Quantum State Learning | Feidiao Yang, Jiaqing Jiang, Jialin Zhang, Xiaoming Sun | In this paper, we study the online quantum state learning problem which is recently proposed by Aaronson et al. (2018). |
| 809 | Bi-Directional Generation for Unsupervised Domain Adaptation | Guanglei Yang, Haifeng Xia, Mingli Ding, Zhengming Ding | To balance the mitigation of domain gap and the preservation of the inherent structure, we propose a Bi-Directional Generation domain adaptation model with consistent classifiers interpolating two intermediate domains to bridge source and target domains. |
| 810 | Harmonious Coexistence of Structured Weight Pruning and Ternarization for Deep Neural Networks | Li Yang, Zhezhi He, Deliang Fan | Harmonious Coexistence of Structured Weight Pruning and Ternarization for Deep Neural Networks |
| 811 | Distributed Primal-Dual Optimization for Online Multi-Task Learning | Peng Yang, Ping Li | Specifically, we propose an adaptive primal-dual algorithm, which not only captures task-specific noise in adversarial learning but also carries out a projection-free update with runtime efficiency. |
| 812 | ML-LOO: Detecting Adversarial Examples with Feature Attribution | Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael Jordan | Based on this observation, we introduce a new framework to detect adversarial examples through thresholding a scale estimate of feature attribution scores. |
| 813 | Dynamical System Inspired Adaptive Time Stepping Controller for Residual Network Families | Yibo Yang, Jianlong Wu, Hongyang Li, Xia Li, Tiancheng Shen, Zhouchen Lin | In this study, we analyze the effects of time stepping on the Euler method and ResNets. |
| 814 | Graph Few-Shot Learning via Knowledge Transfer | Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh Chawla, Zhenhui Li | To address this challenge, we innovatively propose a graph few-shot learning (GFL) algorithm that incorporates prior knowledge learned from auxiliary graphs to improve classification accuracy on the target graph. |
| 815 | Efficient Neural Architecture Search via Proximal Iterations | Quanming Yao, Ju Xu, Wei-Wei Tu, Zhanxing Zhu | In this paper, we propose an efficient NAS method based on proximal iterations (denoted as NASP). |
| 816 | Mastering Complex Control in MOBA Games with Deep Reinforcement Learning | Deheng Ye, Zhao Liu, Mingfei Sun, Bei Shi, Peilin Zhao, Hao Wu, Hongsheng Yu, Shaojie Yang, Xipeng Wu, Qingwei Guo, Qiaobo Chen, Yinyuting Yin, Hao Zhang, Tengfei Shi, Liang Wang, Qiang Fu, Wei Yang, Lanxiao Huang | In this paper, we present a deep reinforcement learning framework to tackle this problem from the perspectives of both system and algorithm. |
| 817 | A Novel Model for Imbalanced Data Classification | Jian Yin, Chunjing Gan, Kaiqi Zhao, Xuan Lin, Zhe Quan, Zhi-Jie Wang | In this work, we propose a novel imbalanced data classification model that considers all these main aspects. |
| 818 | Shared Generative Latent Representation Learning for Multi-View Clustering | Ming Yin, Weitian Huang, Junbin Gao | This paper proposes a novel multi-view clustering method by learning a shared generative latent representation that obeys a mixture of Gaussian distributions. |
| 819 | Divide-and-Conquer Learning with Nyström: Optimal Rate and Algorithm | Rong Yin, Yong Liu, Lijing Lu, Weiping Wang, Dan Meng | Therefore, we propose DC-NY, a novel algorithm that combines divide-and-conquer method, Nyström, conjugate gradient, and preconditioning to scale up KRLS, has the same accuracy of exact KRLS and the minimum time and space complexity compared to the state-of-the-art approximate KRLS estimates. |
| 820 | Fragmentation Coagulation Based Mixed Membership Stochastic Blockmodel | Zheng Yu, Xuhui Fan, Marcin Pietrasik, Marek Z. Reformat | Therefore, we propose a non-parametric fragmentation coagulation based Mixed Membership Stochastic Blockmodel (fcMMSB). |
| 821 | Trading-Off Static and Dynamic Regret in Online Least-Squares and Beyond | Jianjun Yuan, Andrew Lamperski | The first contribution of this paper rigorously characterizes the effect of forgetting factors for a class of online Newton algorithms. |
| 822 | Apprenticeship Learning via Frank-Wolfe | Tom Zahavy, Alon Cohen, Haim Kaplan, Yishay Mansour | We formulate this problem as finding the projection of the feature expectations of the expert on the feature expectations polytope – the convex hull of the feature expectations of all the deterministic policies in the MDP. |
| 823 | Fast Nonparametric Estimation of Class Proportions in the Positive-Unlabeled Classification Setting | Daniel Zeiberg, Shantanu Jain, Predrag Radivojac | Motivated by this need, we propose an intuitive and fast nonparametric algorithm to estimate class proportions. |
| 824 | Topic Modeling on Document Networks with Adjacent-Encoder | Ce Zhang, Hady W. Lauw | In this paper we propose a holistic topic model to learn meaningful and unified low-dimensional representations for networked documents that seek to preserve both textual content and network structure. |
| 825 | Aggregated Gradient Langevin Dynamics | Chao Zhang, Jiahao Xie, Zebang Shen, Peilin Zhao, Tengfei Zhou, Hui Qian | In this paper, we explore a general Aggregated Gradient Langevin Dynamics framework (AGLD) for the Markov Chain Monte Carlo (MCMC) sampling. |
| 826 | CD-UAP: Class Discriminative Universal Adversarial Perturbation | Chaoning Zhang, Philipp Benz, Tooba Imtiaz, In-So Kweon | In this work, we propose a new universal attack method to generate a single perturbation that fools a target network to misclassify only a chosen group of classes, while having limited influence on the remaining classes. |
| 827 | Learning from Positive and Unlabeled Data without Explicit Estimation of Class Prior | Chenguang Zhang, Yuexian Hou, Yan Zhang | In this paper, a new strategy based on the Bhattacharyya coefficient is put forward, which formalizes this learning problem as an optimization problem and does not need a preprocessing step. |
| 828 | Policy Search by Target Distribution Learning for Continuous Control | Chuheng Zhang, Yuanqi Li, Jian Li | To address this issue, we propose a new method, called target distribution learning (TDL), for policy improvement in reinforcement learning. |
| 829 | Universal Value Iteration Networks: When Spatially-Invariant Is Not Universal | Li Zhang, Xin Li, Sen Chen, Hongyu Zang, Jie Huang, Mingzhong Wang | To generalize VIN to spatially variant MDPs, we propose Universal Value Iteration Networks (UVIN). |
| 830 | Systematically Exploring Associations among Multivariate Data | Lifeng Zhang | In this study, we propose a statistical tool named the neighbor correlation coefficient (nCor), which is based on a new idea that measures the local continuity of the reordered data points to quantify the strength of the global association between variables. |
| 831 | High Performance Depthwise and Pointwise Convolutions on Mobile Devices | Pengfei Zhang, Eric Lo, Baotong Lu | We propose techniques to re-optimize the implementations of DWConv and PWConv based on ARM architecture. |
| 832 | Variational Inference for Sparse Gaussian Process Modulated Hawkes Process | Rui Zhang, Christian Walder, Marian-Andrei Rizoiu | In this paper, we aim to solve both problems. |
| 833 | Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset | Ruohan Zhang, Calen Walshe, Zhuode Liu, Lin Guan, Karl Muller, Jake Whritner, Luxin Zhang, Mary Hayhoe, Dana Ballard | We introduce a novel form of gameplay, in which the human plays in a semi-frame-by-frame manner. Here, we provide a large-scale, high-quality dataset of human actions with simultaneously recorded eye movements while humans play Atari video games. |
| 834 | Optimal Margin Distribution Learning in Dynamic Environments | Teng Zhang, Peng Zhao, Hai Jin | In this paper, we propose the dynamic optimal margin distribution machine and theoretically analyze its regret. |
| 835 | AutoShrink: A Topology-Aware NAS for Discovering Efficient Neural Architecture | Tunhou Zhang, Hsin-Pai Cheng, Zhenwen Li, Feng Yan, Chengyu Huang, Hai Li, Yiran Chen | To address these problems, we propose AutoShrink, a topology-aware Neural Architecture Search (NAS) for searching efficient building blocks of neural architectures. |
| 836 | Adaptive Double-Exploration Tradeoff for Outlier Detection | Xiaojin Zhang, Honglei Zhuang, Shengyu Zhang, Yuan Zhou | We study a variant of the thresholding bandit problem (TBP) in the context of outlier detection, where the objective is to identify the outliers whose rewards are above a threshold. |
| 837 | TapNet: Multivariate Time Series Classification with Attentional Prototypical Network | Xuchao Zhang, Yifeng Gao, Jessica Lin, Chang-Tien Lu | In this paper, we propose a novel MTSC model with an attentional prototype network to take the strengths of both traditional and deep learning based approaches. |
| 838 | Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data | Xuchao Zhang, Xian Wu, Fanglan Chen, Liang Zhao, Chang-Tien Lu | In this paper, we consider the problem of training a robust model by using large-scale noisy data in conjunction with a small set of clean data. |
| 839 | Local Regularizer Improves Generalization | Yikai Zhang, Hui Qu, Dimitris Metaxas, Chao Chen | In this paper, we study the generalization power of an unbiased regularizor for training algorithms in deep learning. |
| 840 | An Ordinal Data Clustering Algorithm with Automated Distance Learning | Yiqun Zhang, Yiu-ming Cheung | This paper, therefore, proposes a novel ordinal data clustering algorithm, which iteratively learns: 1) The partition of ordinal dataset, and 2) the inter-category distances. |
| 841 | Joint Adversarial Learning for Domain Adaptation in Semantic Segmentation | Yixin Zhang, Zilei Wang | For such a task, the key point is to learn domain-invariant representations and adversarial learning is usually used, in which the discriminator is to distinguish which domain the input comes from, and the segmentation model targets to deceive the domain discriminator. |
| 842 | Hypergraph Label Propagation Network | Yubo Zhang, Nan Wang, Yufeng Chen, Changqing Zou, Hai Wan, Xinbin Zhao, Yue Gao | In this paper, we propose a Hypergraph Label Propagation Network (HLPN) which combines hypergraph-based label propagation and deep neural networks in order to optimize the feature embedding for optimal hypergraph learning through an end-to-end architecture. |
| 843 | Online Second Price Auction with Semi-Bandit Feedback under the Non-Stationary Setting | Zhao Haoyu, Chen Wei | In this paper, we study the non-stationary online second price auction problem. |
| 844 | Bridging Maximum Likelihood and Adversarial Learning via α-Divergence | Miaoyun Zhao, Yulai Cong, Shuyang Dai, Lawrence Carin | We reveal that generalizations of the α-Bridge are closely related to approaches developed recently to regularize adversarial learning, providing insights into that prior work, and further understanding of why the α-Bridge performs well in practice. |
| 845 | Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent | Pu Zhao, Pin-yu Chen, Siyue Wang, Xue Lin | In this paper, we propose a zeroth-order natural gradient descent (ZO-NGD) method to design the adversarial attacks, which incorporates the zeroth-order gradient estimation technique catering to the black-box attack scenario and the second-order natural gradient descent to achieve higher query efficiency. |
| 846 | Hearing Lips: Improving Lip Reading by Distilling Speech Recognizers | Ya Zhao, Rui Xu, Xinchao Wang, Peng Hou, Haihong Tang, Mingli Song | In this paper, we propose a new method, termed as Lip by Speech (LIBS), of which the goal is to strengthen lip reading by learning from speech recognizers. |
| 847 | An Annotation Sparsification Strategy for 3D Medical Image Segmentation via Representative Selection and Self-Training | Hao Zheng, Yizhe Zhang, Lin Yang, Chaoli Wang, Danny Z. Chen | In this paper, we propose a new DL framework for reducing annotation effort and bridging the gap between full annotation and sparse annotation in 3D medical image segmentation. |
| 848 | A Near-Optimal Change-Detection Based Algorithm for Piecewise-Stationary Combinatorial Semi-Bandits | Huozhi Zhou, Lingda Wang, Lav Varshney, Ee-Peng Lim | We propose an algorithm, GLR-CUCB, which incorporates an efficient combinatorial semi-bandit algorithm, CUCB, with an almost parameter-free change-point detector, the Generalized Likelihood Ratio Test (GLRT). |
| 849 | Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization | Qi Zhou, HouQiang Li, Jie Wang | In this paper, We propose a Policy Optimization method with Model-Based Uncertainty (POMBU)—a novel model-based approach—that can effectively improve the asymptotic performance using the uncertainty in Q-values. |
| 850 | DGE: Deep Generative Network Embedding Based on Commonality and Individuality | Sheng Zhou, Xin Wang, Jiajun Bu, Martin Ester, Pinggang Yu, Jiawei Chen, Qihao Shi, Can Wang | In this paper, we propose a deep generative embedding (DGE) framework which simultaneously captures commonality and individuality between network topology and node attributes in a generative process. |
| 851 | Side Information Dependence as a Regularizer for Analyzing Human Brain Conditions across Cognitive Experiments | Shuo Zhou, Wenwen Li, Christopher Cox, Haiping Lu | Inspired by this approach and the multi-source TL theory, we propose a Side Information Dependence Regularization (SIDeR) learning framework for TL in brain condition decoding. |
| 852 | Multi-View Spectral Clustering with Optimal Neighborhood Laplacian Matrix | Sihang Zhou, Xinwang Liu, Jiyuan Liu, Xifeng Guo, Yawei Zhao, En Zhu, Yongping Zhai, Jianping Yin, Wen Gao | In this paper, we propose a novel optimal neighborhood multi-view spectral clustering (ONMSC) algorithm to address these issues. |
| 853 | Posterior-Guided Neural Architecture Search | Yizhou Zhou, Xiaoyan Sun, Chong Luo, Zheng-Jun Zha, Wenjun Zeng | In this paper, we formulate the NAS problem from a Bayesian perspective. |
| 854 | Safe Sample Screening for Robust Support Vector Machine | Zhou Zhai, Bin Gu, Xiang Li, Heng Huang | To address this challenge, in this paper, we propose two safe sample screening rules for RSVM based on the framework of concave-convex procedure (CCCP). |
| 855 | Object-Oriented Dynamics Learning through Multi-Level Abstraction | Guangxiang Zhu, Jianhao Wang, Zhizhou Ren, Zichuan Lin, Chongjie Zhang | In this paper, we present a novel self-supervised learning framework, called Multi-level Abstraction Object-oriented Predictor (MAOP), which employs a three-level learning architecture that enables efficient object-based dynamics learning from raw visual observations. |
| 856 | A Knowledge-Aware Attentional Reasoning Network for Recommendation | Qiannan Zhu, Xiaofei Zhou, Jia Wu, Jianlong Tan, Li Guo | In this paper, we propose a knowledge-aware attentional reasoning network KARN that incorporates the users’ clicked history sequences and path connectivity between users and items for recommendation. |
| 857 | GSSNN: Graph Smoothing Splines Neural Networks | Shichao Zhu, Lewei Zhou, Shirui Pan, Chuan Zhou, Guiying Yan, Bin Wang | Aiming to overcome these limitations simultaneously, in this paper, we propose a novel, flexible, and end-to-end framework, Graph Smoothing Splines Neural Networks (GSSNN), for graph classification. |
| 858 | Semi-Supervised Streaming Learning with Emerging New Labels | Yong-Nan Zhu, Yu-Feng Li | In this paper, we tackle these issues by a new approach called SEEN which consists of three major components: an effective novel class detector based on clustering random trees, a robust classifier for predictions on the known classes, and an efficient updating process that ensures the whole framework adapts to the changing environment automatically. |
| 859 | Observe Before Play: Multi-Armed Bandit with Pre-Observations | Jinhang Zuo, Xiaoxi Zhang, Carlee Joe-Wong | We consider the stochastic multi-armed bandit (MAB) problem in a setting where a player can pay to pre-observe arm rewards before playing an arm in each round. |
| 860 | Subsidy Allocations in the Presence of Income Shocks | Rediet Abebe, Jon Kleinberg, S. Matthew Weinberg | We introduce a model of welfare that incorporates income, wealth, and income shocks and analyze this model to show that it can vary, at times substantially, from measures of welfare that only use income or wealth. |
| 861 | Parameterised Resource-Bounded ATL | Natasha Alechina, Stéphane Demri, Brian Logan | We give a parameter extraction algorithm and prove that the model-checking problem is 2EXPTIME-complete. |
| 862 | Partner Selection for the Emergence of Cooperation in Multi-Agent Systems Using Reinforcement Learning | Nicolas Anastassacos, Stephen Hailes, Mirco Musolesi | In this paper, we investigate how partner selection can promote cooperative behavior between agents who are trained to maximize a purely selfish objective function. |
| 863 | Incentive-Compatible Classification | Yakov Babichenko, Oren Dean, Moshe Tennenholtz | We investigate the possibility of an incentive-compatible (IC, a.k.a. strategy-proof) mechanism for the classification of agents in a network according to their reviews of each other. |
| 864 | Learning the Value of Teamwork to Form Efficient Teams | Ryan Beal, Narayan Changder, Timothy Norman, Sarvapali Ramchurn | In this paper we describe a novel approach to team formation based on the value of inter-agent interactions. |
| 865 | Model Checking Temporal Epistemic Logic under Bounded Recall | Francesco Belardinelli, Alessio Lomuscio, Emily Yu | We introduce the logic CTLKBR, a bounded-recall variant of the temporal-epistemic logic CTLK. |
| 866 | ODSS: Efficient Hybridization for Optimal Coalition Structure Generation | Narayan Changder, Samir Aknine, Sarvapali Ramchurn, Animesh Dutta | In this paper, we propose an efficient hybrid algorithm for optimal coalition structure generation called ODSS. |
| 867 | HS-CAI: A Hybrid DCOP Algorithm via Combining Search with Context-Based Inference | Dingding Chen, Yanchen Deng, Ziyu Chen, Wenxing Zhang, Zhongshi He | In this paper, (i) hybridizing search with context-based inference, we propose a complete algorithm for DCOPs, named HS-CAI where the inference utilizes the contexts derived from the search process to establish tight lower bounds while the search uses such bounds for efficient pruning and thereby reduces contexts for the inference. |
| 868 | AATEAM: Achieving the Ad Hoc Teamwork by Employing the Attention Mechanism | Shuo Chen, Ewa Andrejczuk, Zhiguang Cao, Jie Zhang | In this paper, we propose AATEAM – a method that uses the attention-based neural networks to cope with new teammates’ behaviour in real-time. |
| 869 | Convergence of Opinion Diffusion is PSPACE-Complete | Dmitry Chistikov, Grzegorz Lisowski, Mike Paterson, Paolo Turrini | We analyse opinion diffusion in social networks, where a finite set of individuals is connected in a directed graph and each simultaneously changes their opinion to that of the majority of their influencers. |
| 870 | A Particle Swarm Based Algorithm for Functional Distributed Constraint Optimization Problems | Moumita Choudhury, Saaduddin Mahmud, Md. Mosaddek Khan | To address this issue, we propose a new F-DCOP algorithm, namely Particle Swarm based F-DCOP (PFD), which is inspired by a meta-heuristic, Particle Swarm Optimization (PSO). |
| 871 | An Operational Semantics for True Concurrency in BDI Agent Systems | Lavindra de Silva | This paper provides a true concurrency operational semantics for a BDI agent programming language, allowing actions to overlap in execution. |
| 872 | Scalable Decision-Theoretic Planning in Open and Typed Multiagent Systems | Adam Eck, Maulik Shah, Prashant Doshi, Leen-Kiat Soh | We present a novel, principled, and scalable method in this context that enables an agent to reason about others’ presence in its shared environment and their actions. |
| 873 | Parameterized Complexity of Envy-Free Resource Allocation in Social Networks | Eduard Eiben, Robert Ganian, Thekla Hamm, Sebastian Ordyniak | We consider the classical problem of allocating resources among agents in an envy-free (and, where applicable, proportional) way. |
| 874 | On the Convergence of Model Free Learning in Mean Field Games | Romuald Elie, Julien Pérolat, Mathieu Laurière, Matthieu Geist, Olivier Pietquin | We adopt a high perspective on this problem and analyze in full generality the convergence of a fictitious iterative scheme using any single agent learning algorithm at each step. |
| 875 | Implicit Coordination Using FOND Planning | Thorsten Engesser, Tim Miller | In this paper, we show how implicit coordination can be achieved in a simpler, propositional setting by using nondeterminism as a means to allow the agents to take the other agents’ perspectives. |
| 876 | Communication Learning via Backpropagation in Discrete Channels with Unknown Noise | Benjamin Freed, Guillaume Sartoretti, Jiaheng Hu, Howie Choset | In this paper, we propose a stochastic message encoding/decoding procedure that makes a discrete communication channel mathematically equivalent to an analog channel with additive noise, through which gradients can be backpropagated. |
| 877 | Distributed Stochastic Gradient Descent with Event-Triggered Communication | Jemin George, Prudhvi Gurram | We propose a novel communication triggering mechanism that would allow the networked agents to update their model parameters aperiodically and provide sufficient conditions on the algorithm step-sizes that guarantee the asymptotic mean-square convergence. |
| 878 | Distributed Machine Learning through Heterogeneous Edge Systems | Hanpeng Hu, Dan Wang, Chuan Wu | This paper proposes ADSP, a parameter synchronization model for distributed machine learning (ML) with heterogeneous edge systems. |
| 879 | Improving Policies via Search in Cooperative Partially Observable Games | Adam Lerer, Hengyuan Hu, Jakob Foerster, Noam Brown | In this paper we propose two different search techniques that can be applied to improve an arbitrary agreed-upon policy in a cooperative partially observable game. |
| 880 | Generative Attention Networks for Multi-Agent Behavioral Modeling | Guangyu Li, Bo Jiang, Hao Zhu, Zhengping Che, Yan Liu | Here we present a deep generative model which captures behavior generating process of multi-agent systems, supports accurate predictions and inference, infers how agents interact in a complex system, as well as identifies agent groups and interaction types. |
| 881 | A Variational Perturbative Approach to Planning in Graph-Based Markov Decision Processes | Dominik Linzner, Heinz Koeppl | We present a novel approximate solution method for multi-agent Markov decision problems on graphs, based on variational perturbation theory. |
| 882 | Multi-Agent Game Abstraction via Graph Attention Neural Network | Yong Liu, Weixun Wang, Yujing Hu, Jianye Hao, Xingguo Chen, Yang Gao | In this paper, we model the relationship between agents by a complete graph and propose a novel game abstraction mechanism based on two-stage attention network (G2ANet), which can indicate whether there is an interaction between two agents and the importance of the interaction. |
| 883 | Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning | Hangyu Mao, Wulong Liu, Jianye Hao, Jun Luo, Dong Li, Zhengchao Zhang, Jun Wang, Zhen Xiao | As examples, we propose neighborhood cognition consistent deep Q-learning and Actor-Critic to facilitate large-scale multi-agent cooperations. |
| 884 | Multi-Objective Multi-Agent Planning for Jointly Discovering and Tracking Mobile Objects | Hoa Van Nguyen, Hamid Rezatofighi, Ba-Ngu Vo, Damith C. Ranasinghe | We consider the challenging problem of online planning for a team of agents to autonomously search and track a time-varying number of mobile objects under the practical constraint of detection range limited onboard sensors. |
| 885 | Multi-Agent Actor-Critic with Hierarchical Graph Attention Network | Heechang Ryu, Hayong Shin, Jinkyoo Park | To resolve these limitations, we propose a model that conducts both representation learning for multiple agents using hierarchical graph attention network and policy learning using multi-agent actor-critic. |
| 886 | Clouseau: Generating Communication Protocols from Commitments | Munindar Singh, Amit Chopra | We contribute Clouseau, an approach that takes a commitment-based specification of an interaction and generates a communication protocol amenable to decentralized enactment. |
| 887 | Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence | Yuhang Song, Andrzej Wojcicki, Thomas Lukasiewicz, Jianyi Wang, Abi Aryan, Zhenghua Xu, Mai Xu, Zihan Ding, Lianlong Wu | To this end, we introduce Arena, a general evaluation platform for multi-agent intelligence with 35 games of diverse logics and representations. Along with the baseline implementations, we release a set of 100 best agents/teams that we can train with different training schemes for each game, as the base for evaluating agents with population performance. |
| 888 | Learning to Communicate Implicitly by Actions | Zheng Tian, Shihao Zou, Ian Davies, Tim Warr, Lisheng Wu, Haitham Bou Ammar, Jun Wang | To mimic both components mentioned above, thereby completing the learning process, we introduce a novel algorithm: Policy Belief Learning (PBL). |
| 889 | Fair Procedures for Fair Stable Marriage Outcomes | Nikolaos Tziavelis, Ioannis Giannakopoulos, Rune Quist Johansen, Katerina Doka, Nectarios Koziris, Panagiotis Karras | We provide the first procedurally fair algorithms that output equitable stable marriages and are guaranteed to terminate in at most cubic time; the key to this breakthrough is the monitoring of a monotonic state function and the use of a selective criterion for accepting proposals. |
| 890 | Generalized and Sub-Optimal Bipartite Constraints for Conflict-Based Search | Thayne T. Walker, Nathan R. Sturtevant, Ariel Felner | This paper introduces a new automatic constraint generation technique called bipartite reduction (BR). |
| 891 | Shapley Q-Value: A Local Reward Approach to Solve Global Reward Games | Jianhong Wang, Yuan Zhang, Tae-Kyun Kim, Yunjie Gu | To deal with this problem, we i) introduce a cooperative-game theoretical framework called extended convex game (ECG) that is a superset of global reward game, and ii) propose a local reward approach called Shapley Q-value. |
| 892 | From Few to More: Large-Scale Dynamic Multiagent Curriculum Learning | Weixun Wang, Tianpei Yang, Yong Liu, Jianye Hao, Xiaotian Hao, Yujing Hu, Yingfeng Chen, Changjie Fan, Yang Gao | In this paper, we design a novel Dynamic Multiagent Curriculum Learning (DyMA-CL) to solve large-scale problems by starting from learning on a multiagent scenario with a small size and progressively increasing the number of agents. |
| 893 | SMIX(λ): Enhancing Centralized Value Functions for Cooperative Multi-Agent Reinforcement Learning | Chao Wen, Xinghu Yao, Yuhui Wang, Xiaoyang Tan | This work presents a sample efficient and effective value-based method, named SMIX(λ), for reinforcement learning in multi-agent environments (MARL) within the paradigm of centralized training with decentralized execution (CTDE), in which learning a stable and generalizable centralized value function (CVF) is crucial. |
| 894 | Optimal Common Contract with Heterogeneous Agents | Shenke Xiao, Zihe Wang, Mengjing Chen, Pingzhong Tang, Xiwang Yang | We consider the principal-agent problem with heterogeneous agents. |
| 895 | COBRA: Context-Aware Bernoulli Neural Networks for Reputation Assessment | Leonit Zeynalvand, Tie Luo, Jie Zhang | COBRA is also robust to security attacks by agents who inject fake machine learning models; notably, it is resistant to the 51-percent attack. |
| 896 | Bi-Level Actor-Critic for Multi-Agent Coordination | Haifeng Zhang, Weizhe Chen, Zeren Huang, Minne Li, Yaodong Yang, Weinan Zhang, Jun Wang | In this paper, we treat agents unequally and consider Stackelberg equilibrium as a potentially better convergence point than Nash equilibrium in terms of Pareto superiority, especially in cooperative environments. |
| 897 | Beyond Trees: Analysis and Convergence of Belief Propagation in Graphs with Multiple Cycles | Roie Zivan, Omer Lev, Rotem Galiki | Focusing on Max-sum, the version of belief propagation for solving distributed constraint optimization problems (DCOPs), we extend the theory on the behavior of belief propagation in general – and Max-sum specifically – when solving problems represented by graphs with multiple cycles. |
| 898 | LeDeepChef Deep Reinforcement Learning Agent for Families of Text-Based Games | Leonard Adolphs, Thomas Hofmann | In this work, we present our deep RL agent—LeDeepChef—that shows generalization capabilities to never-before-seen games of the same family with different environments and task descriptions. |
| 899 | Knowledge Distillation from Internal Representations | Gustavo Aguilar, Yuan Ling, Yu Zhang, Benjamin Yao, Xing Fan, Chenlei Guo | In this paper, we propose to distill the internal representations of a large model such as BERT into a simplified version of it. |
| 900 | Modelling Sentence Pairs via Reinforcement Learning: An Actor-Critic Approach to Learn the Irrelevant Words | Mahtab Ahmed, Robert E. Mercer | In this study, we propose a reinforcement learning (RL) method to learn a sentence pair representation when performing tasks like semantic similarity, paraphrase identification, and question-answer pair modelling. |
| 901 | End-to-End Argumentation Knowledge Graph Construction | Khalid Al-Khatib, Yufang Hou, Henning Wachsmuth, Charles Jochim, Francesca Bonin, Benno Stein | Original in our work is that we propose a model of the knowledge encapsulated in arguments. Based on this model, we build a new corpus that comprises about 16k manual annotations of 4740 claims with instances of the model’s elements, and we develop an end-to-end framework that automatically identifies all modeled types of instances. |
| 902 | Story Realization: Expanding Plot Events into Sentences | Prithviraj Ammanabrolu, Ethan Tien, Wesley Cheung, Zhaochen Luo, William Ma, Lara J. Martin, Mark O. Riedl | We present an ensemble-based model that generates natural language guided by events. |
| 903 | Do Not Have Enough Data? Deep Learning to the Rescue! | Ateret Anaby-Tavor, Boaz Carmeli, Esther Goldbraich, Amir Kantor, George Kour, Segev Shlomov, Naama Tepper, Naama Zwerdling | Based on recent advances in natural language modeling and those in text generation capabilities, we propose a novel data augmentation method for text classification tasks. |
| 904 | Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined Representations | Muhammad Asif Ali, Yifang Sun, Bing Li, Wei Wang | For this, we propose an edge-weighted attentive graph convolution network that refines the noisy mention representations by attending over corpus-level contextual clues prior to the end classification. |
| 905 | Understanding the Semantic Content of Sparse Word Embeddings Using a Commonsense Knowledge Base | Vanda Balogh, Gábor Berend, Dimitrios I. Diochnos, György Turán | We propose a novel methodology to evaluate the semantic content of word embeddings using a commonsense knowledge base, applied here to the sparse case. |
| 906 | Simultaneously Linking Entities and Extracting Relations from Biomedical Text without Mention-Level Supervision | Trapit Bansal, Pat Verga, Neha Choudhary, Andrew McCallum | Instead, we propose a model which is trained to simultaneously produce entity linking and relation decisions while requiring no mention-level annotations. |
| 907 | Zero-Resource Cross-Lingual Named Entity Recognition | M Saiful Bari, Shafiq Joty, Prathyusha Jwalapuram | In this paper, we propose an unsupervised cross-lingual NER model that can transfer NER knowledge from one language to another in a completely unsupervised way without relying on any bilingual dictionary or parallel data. |
| 908 | Generating Well-Formed Answers by Machine Reading with Stochastic Selector Networks | Bin Bi, Chen Wu, Ming Yan, Wei Wang, Jiangnan Xia, Chenliang Li | For the generative QA task, we introduce a new neural architecture, LatentQA, in which a novel stochastic selector network composes a well-formed answer with words selected from the question, the paragraph and the global vocabulary, based on a sequence of discrete latent variables. |
| 909 | PIQA: Reasoning about Physical Commonsense in Natural Language | Yonatan Bisk, Rowan Zellers, Ronan Le bras, Jianfeng Gao, Yejin Choi | In this paper, we introduce the task of physical commonsense reasoning and a corresponding benchmark dataset Physical Interaction: Question Answering or PIQA. |
| 910 | Back to the Future – Temporal Adaptation of Text Representations | Johannes Bjerva, Wouter Kouw, Isabelle Augenstein | We argue that, due to its low computational expense, sequential alignment is a practical solution to dealing with language evolution. |
| 911 | Modelling Semantic Categories Using Conceptual Neighborhood | Zied Bouraoui, Jose Camacho-Collados, Luis Espinosa-Anke, Steven Schockaert | Modelling Semantic Categories Using Conceptual Neighborhood |
| 912 | Inducing Relational Knowledge from BERT | Zied Bouraoui, Jose Camacho-Collados, Steven Schockaert | To explore this question, we propose a methodology for distilling relational knowledge from a pre-trained language model. |
| 913 | Graph Transformer for Graph-to-Sequence Learning | Deng Cai, Wai Lam | Unlike graph neural networks that restrict the information exchange between immediate neighborhood, we propose a new model, known as Graph Transformer, that uses explicit relation encoding and allows direct communication between two distant nodes. |
| 914 | Learning from Easy to Complex: Adaptive Multi-Curricula Learning for Neural Dialogue Generation | Hengyi Cai, Hongshen Chen, Cheng Zhang, Yonghao Song, Xiaofang Zhao, Yangxi Li, Dongsheng Duan, Dawei Yin | The framework is established upon the reinforcement learning paradigm, which automatically chooses different curricula at the evolving learning process according to the learning status of the neural dialogue generation model. |
| 915 | Unsupervised Domain Adaptation on Reading Comprehension | Yu Cao, Meng Fang, Baosheng Yu, Joey Tianyi Zhou | To solve this, we provide a novel conditional adversarial self-training method (CASe). |
| 916 | Zero-Shot Text-to-SQL Learning with Auxiliary Task | Shuaichen Chang, Pengfei Liu, Yun Tang, Jing Huang, Xiaodong He, Bowen Zhou | In this paper, we first diagnose the bottleneck of the text-to-SQL task by providing a new testbed, in which we observe that existing models present poor generalization ability on rarely-seen data. |
| 917 | Hyperbolic Interaction Model for Hierarchical Multi-Label Classification | Boli Chen, Xin Huang, Lin Xiao, Zixin Cai, Liping Jing | We propose to model the word and label hierarchies by embedding them jointly in the hyperbolic space. |
| 918 | DMRM: A Dual-Channel Multi-Hop Reasoning Model for Visual Dialog | Feilong Chen, Fandong Meng, Jiaming Xu, Peng Li, Bo Xu, Jie Zhou | In this paper, we thus propose a novel and more powerful Dual-channel Multi-hop Reasoning Model for Visual Dialog, named DMRM. |
| 919 | Sequence Generation with Optimal-Transport-Enhanced Reinforcement Learning | Liqun Chen, Ke Bai, Chenyang Tao, Yizhe Zhang, Guoyin Wang, Wenlin Wang, Ricardo Henao, Lawrence Carin | We propose a principled approach to address the difficulties associated with RL-based solutions, namely, high-variance gradients, uninformative rewards and brittle training. |
| 920 | Schema-Guided Multi-Domain Dialogue State Tracking with Graph Attention Neural Networks | Lu Chen, Boer Lv, Chi Wang, Su Zhu, Bowen Tan, Kai Yu | In this paper, we propose a Schema-guided multi-domain dialogue State Tracker with graph attention networks (SST) that predicts dialogue states from dialogue utterances and schema graphs which contain slot relations in edges. |
| 921 | Improving Entity Linking by Modeling Latent Entity Type Information | Shuang Chen, Jinpeng Wang, Feng Jiang, Chin-Yew Lin | To tackle this problem, we propose to inject latent entity type information into the entity embeddings based on pre-trained BERT. |
| 922 | TemPEST: Soft Template-Based Personalized EDM Subject Generation through Collaborative Summarization | Yu-Hsiu Chen, Pin-Yu Chen, Hong-Han Shuai, Wen-Chih Peng | We propose a novel personalized EDM subject generation model named Soft Template-based Personalized EDM Subject Generator (TemPEST) to consider the aforementioned users’ characteristics when generating subjects, which contains a soft template-based selective encoder network, a user rating encoder network, a summary decoder network and a rating decoder. |
| 923 | Learning to Map Frequent Phrases to Sub-Structures of Meaning Representation for Neural Semantic Parsing | Bo Chen, Xianpei Han, Ben He, Le Sun | In this paper, we propose that the vocabulary-mismatch problem can be effectively resolved by leveraging appropriate logical tokens. |
| 924 | Attending to Entities for Better Text Understanding | Pengxiang Cheng, Katrin Erk | Recent progress in NLP witnessed the development of large-scale pre-trained language models (GPT, BERT, XLNet, etc.) based on Transformer (Vaswani et al. 2017), and in a range of end tasks, such models have achieved state-of-the-art results, approaching human performance. |
| 925 | Dynamic Embedding on Textual Networks via a Gaussian Process | Pengyu Cheng, Yitong Li, Xinyuan Zhang, Liqun Chen, David Carlson, Lawrence Carin | We address this challenge with a novel end-to-end node-embedding model, called Dynamic Embedding for Textual Networks with a Gaussian Process (DetGP). |
| 926 | Cross-Lingual Natural Language Generation via Pre-Training | Zewen Chi, Li Dong, Furu Wei, Wenhui Wang, Xian-Ling Mao, Heyan Huang | In this work we focus on transferring supervision signals of natural language generation (NLG) tasks between multiple languages. |
| 927 | An Empirical Study of Content Understanding in Conversational Question Answering | Ting-Rui Chiang, Hao-Tong Ye, Yun-Nung Chen | 2) Do the models well utilize the conversation content when answering questions? |
| 928 | How to Ask Better Questions? A Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions | Zewei Chu, Mingda Chen, Jing Chen, Miaosen Wang, Kevin Gimpel, Manaal Faruqui, Xiance Si | We present a large-scale dataset for the task of rewriting an ill-formed natural language question to a well-formed one. We release the MQR dataset to encourage research on the problem of question rewriting.1 |
| 929 | Guiding Attention in Sequence-to-Sequence Models for Dialogue Act Prediction | Pierre Colombo, Emile Chapuis, Matteo Manica, Emmanuel Vignon, Giovanna Varni, Chloe Clavel | In this work, we introduce a seq2seq model tailored for DA classification using: a hierarchical encoder, a novel guided attention mechanism and beam search applied to both training and inference. |
| 930 | Discriminative Sentence Modeling for Story Ending Prediction | Yiming Cui, Wanxiang Che, Wei-Nan Zhang, Ting Liu, Shijin Wang, Guoping Hu | To tackle this task, we propose a new neural network called Diff-Net for better modeling the differences of each ending in this task. |
| 931 | Multiple Positional Self-Attention Network for Text Classification | Biyun Dai, Jinlong Li, Ruoyi Xu | We propose Faraway Mask focusing on the (2m + 1)-gram words and Scaled-Distance Mask putting the logarithmic distance punishment to avoid and weaken the self-attention of distant words respectively. |
| 932 | Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis | Yong Dai, Jian Liu, Xiancong Ren, Zenglin Xu | To avoid these problems, we propose two transfer learning frameworks based on the multi-source domain adaptation methodology for SA by combining the source hypotheses to derive a good target hypothesis. |
| 933 | Hypernym Detection Using Strict Partial Order Networks | Sarthak Dash, Md Faisal Mahbub Chowdhury, Alfio Gliozzo, Nandana Mihindukulasooriya, Nicolas Rodolfo Fauceglia | This paper introduces Strict Partial Order Networks (SPON), a novel neural network architecture designed to enforce asymmetry and transitive properties as soft constraints. |
| 934 | Just Add Functions: A Neural-Symbolic Language Model | David Demeter, Doug Downey | In this paper, we propose a general methodology to enhance the inductive bias of NNLMs by incorporating simple functions into a neural architecture to form a hierarchical neural-symbolic language model (NSLM). |
| 935 | An Iterative Polishing Framework Based on Quality Aware Masked Language Model for Chinese Poetry Generation | Liming Deng, Jie Wang, Hangming Liang, Hui Chen, Zhiqiang Xie, Bojin Zhuang, Shaojun Wang, Jing Xiao | In this paper, we propose a novel iterative polishing framework for highly qualified Chinese poetry generation. |
| 936 | Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering | Yang Deng, Wai Lam, Yuexiang Xie, Daoyuan Chen, Yaliang Li, Min Yang, Ying Shen | To solve these problems, we tackle the tasks of answer selection and answer summary generation in CQA with a novel joint learning model. In addition, we construct a new large-scale CQA corpus, WikiHowQA, which contains long answers for answer selection as well as reference summaries for answer summarization. |
| 937 | On Measuring and Mitigating Biased Inferences of Word Embeddings | Sunipa Dev, Tao Li, Jeff M. Phillips, Vivek Srikumar | We use this observation to design a mechanism for measuring stereotypes using the task of natural language inference. |
| 938 | Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendation | Xin Dong, Jingchao Ni, Wei Cheng, Zhengzhang Chen, Bo Zong, Dongjin Song, Yanchi Liu, Haifeng Chen, Gerard de Melo | In this work, we develop a novel neural network model that properly accounts for this important difference by means of asymmetric attentive modules. |
| 939 | Detecting Asks in Social Engineering Attacks: Impact of Linguistic and Structural Knowledge | Bonnie Dorr, Archna Bhatia, Adam Dalton, Brodie Mather, Bryanna Hebenstreit, Sashank Santhanam, Zhuo Cheng, Samira Shaikh, Alan Zemel, Tomek Strzalkowski | Our approach is implemented in a system that informs users about social engineering risk situations. |
| 940 | Corpus Wide Argument Mining—A Working Solution | Liat Ein-Dor, Eyal Shnarch, Lena Dankin, Alon Halfon, Benjamin Sznajder, Ariel Gera, Carlos Alzate, Martin Gleize, Leshem Choshen, Yufang Hou, Yonatan Bilu, Ranit Aharonov, Noam Slonim | Here we present a first end-to-end high-precision, corpus-wide argument mining system. |
| 941 | Latent Emotion Memory for Multi-Label Emotion Classification | Hao Fei, Yue Zhang, Yafeng Ren, Donghong Ji | In this paper, we propose a Latent Emotion Memory network (LEM) for multi-label emotion classification. |
| 942 | Translucent Answer Predictions in Multi-Hop Reading Comprehension | G P Shrivatsa Bhargav, Michael Glass, Dinesh Garg, Shirish Shevade, Saswati Dana, Dinesh Khandelwal, L Venkata Subramaniam, Alfio Gliozzo | In this paper, we propose a novel deep neural architecture, called TAP (Translucent Answer Prediction), to identify answers and evidence (in the form of supporting facts) in an RCQA task requiring multi-hop reasoning. |
| 943 | Posterior-GAN: Towards Informative and Coherent Response Generation with Posterior Generative Adversarial Network | Shaoxiong Feng, Hongshen Chen, Kan Li, Dawei Yin | Intuitively, a high-quality response not only responds to the given query but also links up to the future conversations, in this paper, we leverage the query-response-future turn triples to induce the generated responses that consider both the given context and the future conversations. |
| 944 | Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation | Xiaocheng Feng, Yawei Sun, Bing Qin, Heng Gong, Yibo Sun, Wei Bi, XiaoJiang Liu, Ting Liu | In this paper, we focus on a new practical task, document-scale text content manipulation, which is the opposite of text style transfer and aims to preserve text styles while altering the content. To tackle those problems, we first build a dataset based on a basketball game report corpus as our testbed, and present an unsupervised neural model with interactive attention mechanism, which is used for learning the semantic relationship between records and reference texts to achieve better content transfer and better style preservation. |
| 945 | Discontinuous Constituent Parsing with Pointer Networks | Daniel Fernández-González, Carlos Gómez-Rodríguez | We propose a novel neural network architecture that, by means of Pointer Networks, is able to generate the most accurate discontinuous constituent representations to date, even without the need of Part-of-Speech tagging information. |
| 946 | Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study | Jinlan Fu, Pengfei Liu, Qi Zhang | In this paper, we take the NER task as a testbed to analyze the generalization behavior of existing models from different perspectives and characterize the differences of their generalization abilities through the lens of our proposed measures, which guides us to better design models and training methods. We have released the datasets: (ReCoNLL, PLONER) for the future research at our project page: http://pfliu.com/InterpretNER/. |
| 947 | Document Summarization with VHTM: Variational Hierarchical Topic-Aware Mechanism | Xiyan Fu, Jun Wang, Jinghan Zhang, Jinmao Wei, Zhenglu Yang | In this study, we propose a variational hierarchical model to holistically address both issues, dubbed VHTM. |
| 948 | Open Domain Event Text Generation | Zihao Fu, Lidong Bing, Wai Lam | In this paper, we extend the task to an open domain event text generation scenario with an entity chain as its skeleton. We build a new dataset called WikiEvent1 that provides 34K pairs of entity chain and its corresponding description sentences. |
| 949 | ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs | Zuohui Fu, Yikun Xian, Shijie Geng, Yingqiang Ge, Yuting Wang, Xin Dong, Guang Wang, Gerard de Melo | To this end, we propose an Adversarial Bi-directional Sentence Embedding Mapping (ABSent) framework, which learns mappings of cross-lingual sentence representations from limited quantities of parallel data. |
| 950 | Likelihood Ratios and Generative Classifiers for Unsupervised Out-of-Domain Detection in Task Oriented Dialog | Varun Gangal, Abhinav Arora, Arash Einolghozati, Sonal Gupta | In this work, we focus on OOD detection for natural language sentence inputs to task-based dialog systems. |
| 951 | Neural Snowball for Few-Shot Relation Learning | Tianyu Gao, Xu Han, Ruobing Xie, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun | To address new relations with few-shot instances, we propose a novel bootstrapping approach, Neural Snowball, to learn new relations by transferring semantic knowledge about existing relations. |
| 952 | TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection | Siddhant Garg, Thuy Vu, Alessandro Moschitti | We propose TandA, an effective technique for fine-tuning pre-trained Transformer models for natural language tasks. We built a large scale dataset to enable the transfer step, exploiting the Natural Questions dataset. |
| 953 | Predictive Engagement: An Efficient Metric for Automatic Evaluation of Open-Domain Dialogue Systems | Sarik Ghazarian, Ralph Weischedel, Aram Galstyan, Nanyun Peng | In this paper, we investigate the possibility and efficacy of estimating utterance-level engagement and define a novel metric, predictive engagement, for automatic evaluation of open-domain dialogue systems. |
| 954 | Two-Level Transformer and Auxiliary Coherence Modeling for Improved Text Segmentation | Goran Glavaš, Swapna Somasundaran | Starting from an apparent link between text coherence and segmentation, we introduce a novel supervised model for text segmentation with simple but explicit coherence modeling. |
| 955 | A Large-Scale Dataset for Argument Quality Ranking: Construction and Analysis | Shai Gretz, Roni Friedman, Edo Cohen-Karlik, Assaf Toledo, Dan Lahav, Ranit Aharonov, Noam Slonim | In this work, we explore the challenging task of argument quality ranking. To this end, we created a corpus of 30,497 arguments carefully annotated for point-wise quality, released as part of this work. |
| 956 | Two Birds with One Stone: Investigating Invertible Neural Networks for Inverse Problems in Morphology | Gözde Gül Şahin, Iryna Gurevych | In this study, we investigate INNs on morphological problems casted as inverse problems. |
| 957 | Working Memory-Driven Neural Networks with a Novel Knowledge Enhancement Paradigm for Implicit Discourse Relation Recognition | Fengyu Guo, Ruifang He, Jianwu Dang, Jian Wang | Inspired by this, we propose a Knowledge-Enhanced Attentive Neural Network (KANN) framework to address these issues. |
| 958 | Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits | Han Guo, Ramakanth Pasunuru, Mohit Bansal | We present a study of various distance-based measures in the context of NLP tasks, that characterize the dissimilarity between domains based on sample estimates. |
| 959 | Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation | Junliang Guo, Xu Tan, Linli Xu, Tao Qin, Enhong Chen, Tie-Yan Liu | In this work, we introduce curriculum learning into fine-tuning for NAT. |
| 960 | Multi-Scale Self-Attention for Text Classification | Qipeng Guo, Xipeng Qiu, Pengfei Liu, Xiangyang Xue, Zheng Zhang | In this paper, we introduce the prior knowledge, multi-scale structure, into self-attention modules. |
| 961 | Fact-Aware Sentence Split and Rephrase with Permutation Invariant Training | Yinuo Guo, Tao Ge, Furu Wei | Previous studies tend to address the issue by seq2seq learning from parallel sentence pairs, which takes a complex sentence as input and sequentially generates a series of simple sentences. |
| 962 | P-SIF: Document Embeddings Using Partition Averaging | Vivek Gupta, Ankit Saw, Pegah Nokhiz, Praneeth Netrapalli, Piyush Rai, Partha Talukdar | To alleviate this problem, we present P-SIF, a partitioned word averaging model to represent long documents. |
| 963 | CASE: Context-Aware Semantic Expansion | Jialong Han, Aixin Sun, Haisong Zhang, Chenliang Li, Shuming Shi | In this paper, we define and study a new task called Context-Aware Semantic Expansion (CASE). |
| 964 | ManyModalQA: Modality Disambiguation and QA over Diverse Inputs | Darryl Hannan, Akshay Jain, Mohit Bansal | We present a new multimodal question answering challenge, ManyModalQA, in which an agent must answer a question by considering three distinct modalities: text, images, and tables. We collect our data by scraping Wikipedia and then utilize crowdsourcing to collect question-answer pairs. |
| 965 | What Do You Mean ‘Why?’: Resolving Sluices in Conversations | Victor Petrén Bach Hansen, Anders Søgaard | This paper introduces the novel ellipsis resolution task of resolving such one-word questions, referred to as sluices in linguistics. We present a crowd-sourced dataset containing annotations of sluices from over 4,000 dialogues collected from conversational QA datasets, as well as a series of strong baseline architectures. |
| 966 | One Homonym per Translation | Bradley Hauer, Grzegorz Kondrak | In this paper, we propose four hypotheses that characterize the unique behavior of homonyms in the context of translations, discourses, collocations, and sense clusters. |
| 967 | Interactive Fiction Games: A Colossal Adventure | Matthew Hausknecht, Prithviraj Ammanabrolu, Marc-Alexandre Côté, Xingdi Yuan | To facilitate rapid development of language-based agents, we introduce Jericho, a learning environment for man-made IF games and conduct a comprehensive study of text-agents across a rich set of games, highlighting directions in which agents can improve. |
| 968 | Latent Relation Language Models | Hiroaki Hayashi, Zecong Hu, Chenyan Xiong, Graham Neubig | In this paper, we propose Latent Relation Language Models (LRLMs), a class of language models that parameterizes the joint distribution over the words in a document and the entities that occur therein via knowledge graph relations. |
| 969 | Knowledge-Graph Augmented Word Representations for Named Entity Recognition | Qizhen He, Liang Wu, Yida Yin, Heming Cai | In this paper, in addition to such context modeling, we propose to encode the prior knowledge of entities from an external knowledge base into the representation, and introduce a Knowledge-Graph Augmented Word Representation or KAWR for named entity recognition. |
| 970 | Improving Neural Relation Extraction with Positive and Unlabeled Learning | Zhengqiu He, Wenliang Chen, Yuyi Wang, Wei Zhang, Guanchun Wang, Min Zhang | We present a novel approach to improve the performance of distant supervision relation extraction with Positive and Unlabeled (PU) Learning. |
| 971 | Emu: Enhancing Multilingual Sentence Embeddings with Semantic Specialization | Wataru Hirota, Yoshihiko Suhara, Behzad Golshan, Wang-Chiew Tan | We present Emu, a system that semantically enhances multilingual sentence embeddings. |
| 972 | Unsupervised Interlingual Semantic Representations from Sentence Embeddings for Zero-Shot Cross-Lingual Transfer | Channy Hong, Jaeyeon Lee, Jungkwon Lee | In this work, we present a novel architecture for training interlingual semantic representations on top of sentence embeddings in a completely unsupervised manner, and demonstrate its effectiveness in zero-shot cross-lingual transfer in natural language inference task. |
| 973 | Knowledge-Enriched Visual Storytelling | Chao-Chun Hsu, Zi-Yuan Chen, Chi-Yang Hsu, Chih-Chia Li, Tzu-Yuan Lin, Ting-Hao Huang, Lun-Wei Ku | This paper introduces KG-Story, a three-stage framework that allows the story generation model to take advantage of external Knowledge Graphs to produce interesting stories. |
| 974 | Leveraging Multi-Token Entities in Document-Level Named Entity Recognition | Anwen Hu, Zhicheng Dou, Jian-Yun Nie, Ji-Rong Wen | In this paper, we divide entities to multi-token entities that contain multiple tokens and single-token entities that are composed of a single token. |
| 975 | What Makes A Good Story? Designing Composite Rewards for Visual Storytelling | Junjie Hu, Yu Cheng, Zhe Gan, Jingjing Liu, Jianfeng Gao, Graham Neubig | In this paper, we re-examine this problem from a different angle, by looking deep into what defines a natural and topically-coherent story. |
| 976 | MALA: Cross-Domain Dialogue Generation with Action Learning | Xinting Huang, Jianzhong Qi, Yu Sun, Rui Zhang | To address this issue, we propose multi-stage adaptive latent action learning (MALA) that learns semantic latent actions by distinguishing the effects of utterances on dialogue progress. |
| 977 | Privacy Enhanced Multimodal Neural Representations for Emotion Recognition | Mimansa Jaiswal, Emily Mower Provost | In this work, we show how multimodal representations trained for a primary task, here emotion recognition, can unintentionally leak demographic information, which could override a selected opt-out option by the user. |
| 978 | Bayes-Adaptive Monte-Carlo Planning and Learning for Goal-Oriented Dialogues | Youngsoo Jang, Jongmin Lee, Kee-Eung Kim | In this paper, we introduce an efficient Bayes-adaptive planning algorithm for goal-oriented dialogues, which combines RNN-based dialogue generation and MCTS-based Bayesian planning in a novel way, leading to robust decision-making under the uncertainty of the other agent’s goal. |
| 979 | Real-Time Emotion Recognition via Attention Gated Hierarchical Memory Network | Wenxiang Jiao, Michael Lyu, Irwin King | Particularly, we propose a Hierarchical Memory Network (HMN) with a bidirectional GRU (BiGRU) as the utterance reader and a BiGRU fusion layer for the interaction between historical utterances. |
| 980 | MMM: Multi-Stage Multi-Task Learning for Multi-Choice Reading Comprehension | Di Jin, Shuyang Gao, Jiun-Yu Kao, Tagyoung Chung, Dilek Hakkani-tur | We introduce MMM, a Multi-stage Multi-task learning framework for Multi-choice reading comprehension. |
| 981 | Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment | Di Jin, Zhijing Jin, Joey Tianyi Zhou, Peter Szolovits | In this paper, we present TextFooler, a simple but strong baseline to generate adversarial text. |
| 982 | SemSUM: Semantic Dependency Guided Neural Abstractive Summarization | Hanqi Jin, Tianming Wang, Xiaojun Wan | In this work, we incorporate semantic dependency graphs about predicate-argument structure of input sentences into neural abstractive summarization for the problem. |
| 983 | Relation Extraction Exploiting Full Dependency Forests | Lifeng Jin, Linfeng Song, Yue Zhang, Kun Xu, Wei-Yun Ma, Dong Yu | We propose to leverage full dependency forests for this task, where a full dependency forest encodes all possible trees. |
| 984 | Monolingual Transfer Learning via Bilingual Translators for Style-Sensitive Paraphrase Generation | Tomoyuki Kajiwara, Biwa Miura, Yuki Arase | We tackle the low-resource problem in style transfer by employing transfer learning that utilizes abundantly available raw corpora. |
| 985 | Syntactically Look-Ahead Attention Network for Sentence Compression | Hidetaka Kamigaito, Manabu Okumura | To solve this problem, we propose a novel Seq2Seq model, syntactically look-ahead attention network (SLAHAN), that can generate informative summaries by explicitly tracking both dependency parent and child words during decoding and capturing important words that will be decoded in the future. |
| 986 | Learning to Learn Morphological Inflection for Resource-Poor Languages | Katharina Kann, Samuel R. Bowman, Kyunghyun Cho | We propose to cast the task of morphological inflection—mapping a lemma to an indicated inflected form—for resource-poor languages as a meta-learning problem. |
| 987 | Weakly Supervised POS Taggers Perform Poorly on <em>Truly</em> Low-Resource Languages | Katharina Kann, Ophélie Lacroix, Anders Søgaard | We train and evaluate state-of-the-art weakly supervised POS taggers for a typologically diverse set of 15 truly low-resource languages. |
| 988 | Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks | Pavan Kapanipathi, Veronika Thost, Siva Sankalp Patel, Spencer Whitehead, Ibrahim Abdelaziz, Avinash Balakrishnan, Maria Chang, Kshitij Fadnis, Chulaka Gunasekara, Bassem Makni, Nicholas Mattei, Kartik Talamadupula, Achille Fokoue | We present an approach that complements text-based entailment models with information from KGs by (1) using Personalized PageRank to generate contextual subgraphs with reduced noise and (2) encoding these subgraphs using graph convolutional networks to capture the structural and semantic information in KGs. |
| 989 | QASC: A Dataset for Question Answering via Sentence Composition | Tushar Khot, Peter Clark, Michal Guerquin, Peter Jansen, Ashish Sabharwal | Guided by these annotations, we present a two-step approach to mitigate the retrieval challenges. We present a multi-hop reasoning dataset, Question Answering via Sentence Composition (QASC), that requires retrieving facts from a large corpus and composing them to answer a multiple-choice question. |
| 990 | Modality-Balanced Models for Visual Dialogue | Hyounghun Kim, Hao Tan, Mohit Bansal | We present multiple methods for this integration of the two models, via ensemble and consensus dropout fusion with shared parameters. |
| 991 | Top-Down RST Parsing Utilizing Granularity Levels in Documents | Naoki Kobayashi, Tsutomu Hirao, Hidetaka Kamigaito, Manabu Okumura, Masaaki Nagata | Thus, we propose a novel neural top-down RST parsing method. |
| 992 | MA-DST: Multi-Attention-Based Scalable Dialog State Tracking | Adarsh Kumar, Peter Ku, Anuj Goyal, Angeliki Metallinou, Dilek Hakkani-Tur | We introduce a novel architecture for this task to encode the conversation history and slot semantics more robustly by using attention mechanisms at multiple granularities. |
| 993 | Deep Attentive Ranking Networks for Learning to Order Sentences | Pawan Kumar, Dhanajit Brahma, Harish Karnick, Piyush Rai | We present an attention-based ranking framework for learning to order sentences given a paragraph. |
| 994 | CSI: A Coarse Sense Inventory for 85% Word Sense Disambiguation | Caterina Lacerra, Michele Bevilacqua, Tommaso Pasini, Roberto Navigli | In this paper we cope with this long-standing problem by introducing Coarse Sense Inventory (CSI), obtained by linking WordNet concepts to a new set of 45 labels. |
| 995 | A General Framework for Implicit and Explicit Debiasing of Distributional Word Vector Spaces | Anne Lauscher, Goran Glavaš, Simone Paolo Ponzetto, Ivan Vulić | In this work, we introduce a general framework for debiasing word embeddings. |
| 996 | Multi-Task Learning for Metaphor Detection with Graph Convolutional Neural Networks and Word Sense Disambiguation | Duong Le, My Thai, Thien Nguyen | In this work, we introduce two novel mechanisms to improve the performance of the deep learning models for metaphor detection. |
| 997 | Segment-Then-Rank: Non-Factoid Question Answering on Instructional Videos | Kyungjae Lee, Nan Duan, Lei Ji, Jason Li, Seung-won Hwang | Motivated by this, we propose a two-stage model: (a) multimodal segmentation of video into span candidates and (b) length-adaptive ranking of the candidates to the question. |
| 998 | ALOHA: Artificial Learning of Human Attributes for Dialogue Agents | Aaron W. Li, Veronica Jiang, Steven Y. Feng, Julia Sprague, Wei Zhou, Jesse Hoey | We propose Human Level Attributes (HLAs) based on tropes as the basis of a method for learning dialogue agents that can imitate the personalities of fictional characters. |
| 999 | Recursively Binary Modification Model for Nested Named Entity Recognition | Bing Li, Shifeng Liu, Yifang Sun, Wei Wang, Xiang Zhao | In this paper, we present a novel Recursively Binary Modification model for nested named entity recognition. |
| 1000 | GraphER: Token-Centric Entity Resolution with Graph Convolutional Neural Networks | Bing Li, Wei Wang, Yifang Sun, Linhan Zhang, Muhammad Asif Ali, Yi Wang | In this paper, we propose a novel graph-based ER model GraphER. |
| 1001 | ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network | Fei Li, Hong Yu | In this paper, we proposed a Multi-Filter Residual Convolutional Neural Network (MultiResCNN) for ICD coding. |
| 1002 | Aspect-Aware Multimodal Summarization for Chinese E-Commerce Products | Haoran Li, Peng Yuan, Song Xu, Youzheng Wu, Xiaodong He, Bowen Zhou | We present an abstractive summarization system that produces summary for Chinese e-commerce products. We construct a large-scale Chinese e-commerce product summarization dataset that contains approximately 1.4 million manually created product summaries that are paired with detailed product information, including an image, a title, and other textual descriptions for each product. |
| 1003 | Keywords-Guided Abstractive Sentence Summarization | Haoran Li, Junnan Zhu, Jiajun Zhang, Chengqing Zong, Xiaodong He | In this paper, we propose an abstractive sentence summarization method by applying guidance signals of keywords to both the encoder and the decoder in the sequence-to-sequence model. |
| 1004 | Neuron Interaction Based Representation Composition for Neural Machine Translation | Jian Li, Xing Wang, Baosong Yang, Shuming Shi, Michael R. Lyu, Zhaopeng Tu | Starting from this intuition, we propose a novel approach to compose representations learned by different components in neural machine translation (e.g., multi-layer networks or multi-head attention), based on modeling strong interactions among neurons in the representation vectors. |
| 1005 | Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce | Juntao Li, Chang Liu, Jian Wang, Lidong Bing, Hongsong Li, Xiaozhong Liu, Dongyan Zhao, Rui Yan | In this paper, we explore a new task of cross-lingual information retrieval, i.e., cross-lingual set-to-description retrieval in cross-border e-commerce, which involves matching product attribute sets in the source language with persuasive product descriptions in the target language. We manually collect a new and high-quality paired dataset, where each pair contains an unordered product attribute set in the source language and an informative product description in the target language. |
| 1006 | Simultaneous Learning of Pivots and Representations for Cross-Domain Sentiment Classification | Liang Li, Weirui Ye, Mingsheng Long, Yateng Tang, Jin Xu, Jianmin Wang | Towards learning the pivots and representations simultaneously, we propose a new Transferable Pivot Transformer (TPT). |
| 1007 | RobuTrans: A Robust Transformer-Based Text-to-Speech Model | Naihan Li, Yanqing Liu, Yu Wu, Shujie Liu, Sheng Zhao, Ming Liu | To build a neural model which can synthesize both natural and stable audios, in this paper, we make a deep analysis of why the previous neural TTS models are not robust, based on which we propose RobuTrans (Robust Transformer), a robust neural TTS model based on Transformer. |
| 1008 | Why Attention? Analyze BiLSTM Deficiency and Its Remedies in the Case of NER | Peng-Hsuan Li, Tsu-Jui Fu, Wei-Yun Ma | We give in-depth analyses of the improvements across several aspects of NER, especially the identification of multi-token mentions. |
| 1009 | MetaMT, a Meta Learning Method Leveraging Multiple Domain Data for Low Resource Machine Translation | Rumeng Li, Xun Wang, Hong Yu | In this paper, we present a novel NMT model with a new word embedding transition technique for fast domain adaption. |
| 1010 | Relevance-Promoting Language Model for Short-Text Conversation | Xin Li, Piji Li, Wei Bi, Xiaojiang Liu, Wai Lam | In this paper, we propose to formulate the STC task as a language modeling problem and tailor-make a training strategy to adapt a language model for response generation. |
| 1011 | Towards Zero-Shot Learning for Automatic Phonemic Transcription | Xinjian Li, Siddharth Dalmia, David Mortensen, Juncheng Li, Alan Black, Florian Metze | In this work, we address this problem by adopting the idea of zero-shot learning. |
| 1012 | Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction | Yang Li, Guodong Long, Tao Shen, Tianyi Zhou, Lina Yao, Huan Huo, Jing Jiang | In this paper, we propose a brand-new light-weight neural framework to address the distantly supervised relation extraction problem and alleviate the defects in previous selective attention framework. |
| 1013 | Span-Based Neural Buffer: Towards Efficient and Effective Utilization of Long-Distance Context for Neural Sequence Models | Yangming Li, Kaisheng Yao, Libo Qin, Shuang Peng, Yijia Liu, Xiaolong Li | To alleviate this problem, we propose a novel training algorithm that combines an annealed maximum likelihood estimation with an intrinsic reward-driven reinforcement learning. |
| 1014 | Neural Machine Translation with Joint Representation | Yanyang Li, Qiang Wang, Tong Xiao, Tongran Liu, Jingbo Zhu | In this paper, we employ Joint Representation that fully accounts for each possible interaction. |
| 1015 | End-to-End Trainable Non-Collaborative Dialog System | Yu Li, Kun Qian, Weiyan Shi, Zhou Yu | Building upon TransferTransfo (Wolf et al. 2019), we propose an end-to-end neural network model to generate diverse coherent responses. |
| 1016 | Complementary Auxiliary Classifiers for Label-Conditional Text Generation | Yuan Li, Chunyuan Li, Yizhe Zhang, Xiujun Li, Guoqing Zheng, Lawrence Carin, Jianfeng Gao | In this paper, we present CARA to alleviate the issue, where two auxiliary classifiers work simultaneously to ensure that (1) the encoder learns disentangled features and (2) the generator produces label-related sentences. |
| 1017 | Explicit Sentence Compression for Neural Machine Translation | Zuchao Li, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Zhuosheng Zhang, Hai Zhao | In this paper, we propose an explicit sentence compression method to enhance the source sentence representation for NMT. |
| 1018 | Global Greedy Dependency Parsing | Zuchao Li, Hai Zhao, Kevin Parnow | In this paper, we propose a novel parsing order objective, resulting in a novel dependency parsing model capable of both global (in sentence scope) feature extraction as in graph models and linear time inference as in transitional models. |
| 1019 | MOSS: End-to-End Dialog System Framework with Modular Supervision | Weixin Liang, Youzhi Tian, Chengcai Chen, Zhou Yu | To utilize limited training data more efficiently, we propose Modular Supervision Network (MOSS), an encoder-decoder training framework that could incorporate supervision from various intermediate dialog system modules including natural language understanding, dialog state tracking, dialog policy learning and natural language generation. |
| 1020 | Embedding Compression with Isotropic Iterative Quantization | Siyu Liao, Jie Chen, Yanzhi Wang, Qinru Qiu, Bo Yuan | Therefore, in this paper we propose an isotropic iterative quantization (IIQ) approach for compressing embedding vectors into binary ones, leveraging the iterative quantization technique well established for image retrieval, while satisfying the desired isotropic property of PMI based models. |
| 1021 | Semi-Supervised Learning on Meta Structure: Multi-Task Tagging and Parsing in Low-Resource Scenarios | KyungTae Lim, Jay Yoon Lee, Jaime Carbonell, Thierry Poibeau | More specifically, taking inspiration from co-training methods, we propose a semi-supervised learning approach based on multi-view models through consensus promotion, and investigate whether this improves overall performance. |
| 1022 | Hierarchical Attention Network with Pairwise Loss for Chinese Zero Pronoun Resolution | Peiqin Lin, Meng Yang | To solve these problems, we propose a Hierarchical Attention Network with Pairwise Loss (HAN-PL), for Chinese zero pronoun resolution. |
| 1023 | Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement | Ting-En Lin, Hua Xu, Hanlei Zhang | In this paper, we propose constrained deep adaptive clustering with cluster refinement (CDAC+), an end-to-end clustering method that can naturally incorporate pairwise constraints as prior knowledge to guide the clustering process. |
| 1024 | Integrating Linguistic Knowledge to Sentence Paraphrase Generation | Zibo Lin, Ziran Li, Ning Ding, Hai-Tao Zheng, Ying Shen, Wei Wang, Cong-Zhi Zhao | To fill this gap, we propose Knowledge-Enhanced Paraphrase Network (KEPN), a transformer-based framework that can leverage external linguistic knowledge to facilitate paraphrase generation. |
| 1025 | Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning | Dayiheng Liu, Jie Fu, Yidan Zhang, Chris Pal, Jiancheng Lv | In this paper, we show that neither of these components is indispensable. |
| 1026 | Joint Character-Level Word Embedding and Adversarial Stability Training to Defend Adversarial Text | Hui Liu, Yongzheng Zhang, Yipeng Wang, Zheng Lin, Yige Chen | In this paper, we propose a framework which jointly uses the character embedding and the adversarial stability training to overcome these two challenges. |
| 1027 | A Robust Adversarial Training Approach to Machine Reading Comprehension | Kai Liu, Xin Liu, An Yang, Jing Liu, Jinsong Su, Sujian Li, Qiaoqiao She | In this paper, we propose a novel robust adversarial training approach to improve the robustness of MRC models in a more generic way. |
| 1028 | HAMNER: Headword Amplified Multi-Span Distantly Supervised Method for Domain Specific Named Entity Recognition | Shifeng Liu, Yifang Sun, Bing Li, Wei Wang, Xiang Zhao | In this paper, we aim at the limitations of the dictionary usage and mention boundary detection. |
| 1029 | Tensor Graph Convolutional Networks for Text Classification | Xien Liu, Xinxin You, Xiao Zhang, Ji Wu, Ping Lv | In this paper, we investigate graph-based neural networks for text classification problem. |
| 1030 | Synchronous Speech Recognition and Speech-to-Text Translation with Interactive Decoding | Yuchen Liu, Jiajun Zhang, Hao Xiong, Long Zhou, Zhongjun He, Hua Wu, Haifeng Wang, Chengqing Zong | In this paper, we propose a novel interactive attention mechanism which enables ASR and ST to perform synchronously and interactively in a single model. |
| 1031 | CatGAN: Category-Aware Generative Adversarial Networks with Hierarchical Evolutionary Learning for Category Text Generation | Zhiyue Liu, Jiahai Wang, Zhiwei Liang | This paper proposes a category-aware GAN (CatGAN) which consists of an efficient category-aware model for category text generation and a hierarchical evolutionary learning algorithm for training our model. |
| 1032 | Attention-Informed Mixed-Language Training for Zero-Shot Cross-Lingual Task-Oriented Dialogue Systems | Zihan Liu, Genta Indra Winata, Zhaojiang Lin, Peng Xu, Pascale Fung | In order to circumvent the expensive and time-consuming data collection, we introduce Attention-Informed Mixed-Language Training (MLT), a novel zero-shot adaptation method for cross-lingual task-oriented dialogue systems. |
| 1033 | Hierarchical Contextualized Representation for Named Entity Recognition | Ying Luo, Fengshun Xiao, Hai Zhao | In this paper, we address these two deficiencies and propose a model augmented with hierarchical contextualized representation: sentence-level representation and document-level representation. |
| 1034 | Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering | Shangwen Lv, Daya Guo, Jingjing Xu, Duyu Tang, Nan Duan, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, Songlin Hu | In this work, we propose to automatically extract evidence from heterogeneous knowledge sources, and answer questions based on the extracted evidence. |
| 1035 | FPETS: Fully Parallel End-to-End Text-to-Speech System | Dabiao Ma, Zhiba Su, Wenxuan Wang, Yuhao Lu | In this paper, we propose a novel non-autoregressive, fully parallel end-to-end TTS system (FPETS). |
| 1036 | Improving Question Generation with Sentence-Level Semantic Matching and Answer Position Inferring | Xiyao Ma, Qile Zhu, Yanlin Zhou, Xiaolin Li | In this paper, we propose a neural question generation model with two general modules: sentence-level semantic matching and answer position inferring. |
| 1037 | CAWA: An Attention-Network for Credit Attribution | Saurav Manchanda, George Karypis | In this paper, we present Credit Attribution With Attention (CAWA), a neural-network-based approach, that instead of using sentence-level labeled data, uses the set of class labels that are associated with an entire document as a source of distant-supervision. |
| 1038 | Robust Named Entity Recognition with Truecasing Pretraining | Stephen Mayhew, Gupta Nitish, Dan Roth | In this work, we address the problem of robustness of NER systems in data with noisy or uncertain casing, using a pretraining objective that predicts casing in text, or a truecaser, leveraging unlabeled data. |
| 1039 | Simplify-Then-Translate: Automatic Preprocessing for Black-Box Translation | Sneha Mehta, Bahareh Azarnoush, Boris Chen, Avneesh Saluja, Vinith Misra, Ballav Bihani, Ritwik Kumar | In this work, we introduce a method to improve such systems via automatic pre-processing (APP) using sentence simplification. |
| 1040 | RefNet: A Reference-Aware Network for Background Based Conversation | Chuan Meng, Pengjie Ren, Zhumin Chen, Christof Monz, Jun Ma, Maarten de Rijke | In this paper, we propose a Reference-aware Network (RefNet) to address both issues. |
| 1041 | Enhancing Natural Language Inference Using New and Expanded Training Data Sets and New Learning Models | Arindam Mitra, Ishan Shrivastava, Chitta Baral | As part of this work, we have developed two datasets that help mitigate such issues and make the systems better at understanding the notion of “entities” and “roles”. |
| 1042 | TRENDNERT: A Benchmark for Trend and Downtrend Detection in a Scientific Domain | Alena Moiseeva, Hinrich Schütze | We propose Mean Average Precision (MAP) as an evaluation measure for trend detection and apply this measure in an investigation of several baselines. |
| 1043 | Conclusion-Supplement Answer Generation for Non-Factoid Questions | Makoto Nakatsuji, Sohei Okui | This paper tackles the goal of conclusion-supplement answer generation for non-factoid questions, which is a critical issue in the field of Natural Language Processing (NLP) and Artificial Intelligence (AI), as users often require supplementary information before accepting a conclusion. |
| 1044 | Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction | Tapas Nayak, Hwee Tou Ng | In this paper, we propose two approaches to use encoder-decoder architecture for jointly extracting entities and relations. |
| 1045 | Merging Weak and Active Supervision for Semantic Parsing | Ansong Ni, Pengcheng Yin, Graham Neubig | We study different active learning heuristics for selecting examples to query, and various forms of extra supervision for such queries. |
| 1046 | Message Passing Attention Networks for Document Understanding | Giannis Nikolentzos, Antoine Tixier, Michalis Vazirgiannis | In this paper, we represent documents as word co-occurrence networks and propose an application of the message passing framework to NLP, the Message Passing Attention network for Document understanding (MPAD). |
| 1047 | Deep Residual-Dense Lattice Network for Speech Enhancement | Mohammad Nikzad, Aaron Nicolson, Yongsheng Gao, Jun Zhou, Kuldip K. Paliwal, Fanhua Shang | Motivated by this, we propose the residual-dense lattice network (RDL-Net), which is a new CNN for speech enhancement that employs both residual and dense aggregations without over-allocating parameters for feature re-usage. |
| 1048 | AvgOut: A Simple Output-Probability Measure to Eliminate Dull Responses | Tong Niu, Mohit Bansal | In our work, we build dialogue models that are dynamically aware of what utterances or tokens are dull without any feature-engineering. |
| 1049 | Controlling Neural Machine Translation Formality with Synthetic Supervision | Xing Niu, Marine Carpuat | This work aims to produce translations that convey source language content at a formality level that is appropriate for a particular audience. |
| 1050 | Fine-Grained Entity Typing for Domain Independent Entity Linking | Yasumasa Onoe, Greg Durrett | We tackle the problem of building robust entity linking models that generalize effectively and do not rely on labeled entity linking data with a specific entity distribution. |
| 1051 | Mask & Focus: Conversation Modelling by Learning Concepts | Gaurav Pandey, Dinesh Raghu, Sachindra Joshi | In this paper, we attempt to mimic this response generating mechanism by learning the essential concepts in the context and response in an unsupervised manner. |
| 1052 | Associating Natural Language Comment and Source Code Entities | Sheena Panthaplackel, Milos Gligoric, Raymond J. Mooney, Junyi Jessy Li | We propose an approach for automatically extracting supervised data using revision histories of open source projects and present a manually annotated evaluation dataset for this task. |
| 1053 | Knowing What, How and Why: A Near Complete Solution for Aspect-Based Sentiment Analysis | Haiyun Peng, Lu Xu, Lidong Bing, Fei Huang, Wei Lu, Luo Si | In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE). |
| 1054 | MTSS: Learn from Multiple Domain Teachers and Become a Multi-Domain Dialogue Expert | Shuke Peng, Feng Ji, Zehao Lin, Shaobo Cui, Haiqing Chen, Yin Zhang | In this paper, we propose a novel method to acquire a satisfying policy and subtly circumvent the knotty dialogue state representation problem in the multi-domain setting. |
| 1055 | Verb Class Induction with Partial Supervision | Daniel Peterson, Susan Brown, Martha Palmer | Prior work on constructing Levin-style semantic verb clusters achieves state-of-the-art results using D-M mixtures for verb sense induction and clustering. |
| 1056 | Towards Building a Multilingual Sememe Knowledge Base: Predicting Sememes for BabelNet Synsets | Fanchao Qi, Liang Chang, Maosong Sun, Sicong Ouyang, Zhiyuan Liu | To address the issue, we propose to build a unified sememe KB for multiple languages based on BabelNet, a multilingual encyclopedic dictionary. We first build a dataset serving as the seed of the multilingual sememe KB. |
| 1057 | Translation-Based Matching Adversarial Network for Cross-Lingual Natural Language Inference | Kunxun Qi, Jianfeng Du | To resolve these limitations in existing methods, this paper proposes an adversarial training framework to enhance both pre-trained models and classical neural models for cross-lingual natural language inference. |
| 1058 | Solving Sequential Text Classification as Board-Game Playing | Chen Qian, Fuli Feng, Lijie Wen, Zhenpeng Chen, Li Lin, Yanan Zheng, Tat-Seng Chua | In this paper, we propose a novel model that labels a sequence of fragments in jumping order. |
| 1059 | Lexical Simplification with Pretrained Encoders | Jipeng Qiang, Yun Li, Yi Zhu, Yunhao Yuan, Xindong Wu | We present a simple LS approach that makes use of the Bidirectional Encoder Representations from Transformers (BERT) which can consider both the given sentence and the complex word during generating candidate substitutions for the complex word. |
| 1060 | Dynamic Knowledge Routing Network for Target-Guided Open-Domain Conversation | Jinghui Qin, Zheng Ye, Jianheng Tang, Xiaodan Liang | In this work, we adopt a structured approach that controls the intended content of system responses by introducing coarse-grained keywords, attains smooth conversation transition through turn-level supervised learning and knowledge relations between candidate keywords, and drives an conversation towards an specified target with discourse-level guiding strategy. Furthermore, to push the research boundary of target-guided open-domain conversation to match real-world scenarios better, we introduce a new large-scale Chinese target-guided open-domain conversation dataset (more than 900K conversations) crawled from Sina Weibo. |
| 1061 | DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification | Libo Qin, Wanxiang Che, Yangming Li, Mingheng Ni, Ting Liu | To address this problem, we propose a Deep Co-Interactive Relation Network (DCR-Net) to explicitly consider the cross-impact and model the interaction between the two tasks by introducing a co-interactive relation layer. |
| 1062 | Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs | Pengda Qin, Xin Wang, Wenhu Chen, Chunyun Zhang, Weiran Xu, William Yang Wang | In this paper, we consider a novel formulation, zero-shot learning, to free this cumbersome curation. |
| 1063 | Entrainment2Vec: Embedding Entrainment for Multi-Party Dialogues | Zahra Rahimi, Diane Litman | In this paper, utilizing an existing pairwise asymmetric entrainment measure, we propose a novel graph-based vector representation of multi-party entrainment that incorporates both strength and dynamics of pairwise entrainment relations. |
| 1064 | Towards Scalable Multi-Domain Conversational Agents: The Schema-Guided Dialogue Dataset | Abhinav Rastogi, Xiaoxue Zang, Srinivas Sunkara, Raghav Gupta, Pranav Khaitan | In this work, we introduce the the Schema-Guided Dialogue (SGD) dataset, containing over 16k multi-domain conversations spanning 16 domains. |
| 1065 | Thinking Globally, Acting Locally: Distantly Supervised Global-to-Local Knowledge Selection for Background Based Conversation | Pengjie Ren, Zhumin Chen, Christof Monz, Jun Ma, Maarten de Rijke | In order to effectively learn the topic transition vector, we propose a distantly supervised learning schema. |
| 1066 | Multi-Task Learning with Generative Adversarial Training for Multi-Passage Machine Reading Comprehension | Qiyu Ren, Xiang Cheng, Sen Su | In this paper, we present MG-MRC, a novel approach for multi-passage MRC via multi-task learning with generative adversarial training. |
| 1067 | Probing Natural Language Inference Models through Semantic Fragments | Kyle Richardson, Hai Hu, Lawrence Moss, Ashish Sabharwal | To investigate this, we propose the use of semantic fragments—systematically generated datasets that each target a different semantic phenomenon—for probing, and efficiently improving, such capabilities of linguistic models. |
| 1068 | Getting Closer to AI Complete Question Answering: A Set of Prerequisite Real Tasks | Anna Rogers, Olga Kovaleva, Matthew Downey, Anna Rumshisky | We present QuAIL, the first RC dataset to combine text-based, world knowledge and unanswerable questions, and to provide question type annotation that would enable diagnostics of the reasoning strategies by a given QA system. |
| 1069 | WinoGrande: An Adversarial Winograd Schema Challenge at Scale | Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi | To investigate this question, we introduce WinoGrande, a large-scale dataset of 44k problems, inspired by the original WSC design, but adjusted to improve both the scale and the hardness of the dataset. |
| 1070 | Hierarchical Reinforcement Learning for Open-Domain Dialog | Abdelrhman Saleh, Natasha Jaques, Asma Ghandeharioun, Judy Shen, Rosalind Picard | In this paper, we propose a novel approach to hierarchical reinforcement learning (HRL), VHRL, which uses policy gradients to tune the utterance-level embedding of a variational sequence model. |
| 1071 | CASIE: Extracting Cybersecurity Event Information from Text | Taneeya Satyapanich, Francis Ferraro, Tim Finin | We present CASIE, a system that extracts information about cybersecurity events from text and populates a semantic model, with the ultimate goal of integration into a knowledge graph of cybersecurity data. |
| 1072 | SensEmBERT: Context-Enhanced Sense Embeddings for Multilingual Word Sense Disambiguation | Bianca Scarlini, Tommaso Pasini, Roberto Navigli | In this paper, we propose SensEmBERT, a knowledge-based approach that brings together the expressive power of language modelling and the vast amount of knowledge contained in a semantic network to produce high-quality latent semantic representations of word meanings in multiple languages. |
| 1073 | Rare Words: A Major Problem for Contextualized Embeddings and How to Fix it by Attentive Mimicking | Timo Schick, Hinrich Schütze | In order to make this possible, we introduce one-token approximation, a procedure that enables us to use Attentive Mimicking even when the underlying language model uses subword-based tokenization, i.e., it does not assign embeddings to all words. To evaluate our method, we create a novel dataset that tests the ability of language models to capture semantic properties of words without any task-specific fine-tuning. |
| 1074 | Can Embeddings Adequately Represent Medical Terminology? New Large-Scale Medical Term Similarity Datasets Have the Answer! | Claudia Schulz, Damir Juric | We present multiple automatically created large-scale medical term similarity datasets and confirm their high quality in an annotation study with doctors. |
| 1075 | Interpretable Rumor Detection in Microblogs by Attending to User Interactions | Ling Min Serena Khoo, Hai Leong Chieu, Zhong Qian, Jing Jiang | We propose a post-level attention model (PLAN) to model long distance interactions between tweets with the multi-head attention mechanism in a transformer network. |
| 1076 | Automatic Fact-Guided Sentence Modification | Darsh Shah, Tal Schuster, Regina Barzilay | In this paper, we focus on rewriting such dynamically changing articles. |
| 1077 | Are Noisy Sentences Useless for Distant Supervised Relation Extraction? | Yuming Shang, He-Yan Huang, Xian-Ling Mao, Xin Sun, Wei Wei | Thus, in this paper, we propose a novel method for distant supervised relation extraction, which employs unsupervised deep clustering to generate reliable labels for noisy sentences. |
| 1078 | Graph-Based Transformer with Cross-Candidate Verification for Semantic Parsing | Bo Shao, Yeyun Gong, Weizhen Qi, Guihong Cao, Jianshu Ji, Xiaola Lin | In this paper, we present a graph-based Transformer for semantic parsing. |
| 1079 | Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT | Sheng Shen, Zhen Dong, Jiayu Ye, Linjian Ma, Zhewei Yao, Amir Gholami, Michael W. Mahoney, Kurt Keutzer | In this work, we perform an extensive analysis of fine-tuned BERT models using second order Hessian information, and we use our results to propose a novel method for quantizing BERT models to ultra low precision. |
| 1080 | On the Generation of Medical Question-Answer Pairs | Sheng Shen, Yaliang Li, Nan Du, Xian Wu, Yusheng Xie, Shen Ge, Tao Yang, Kai Wang, Xingzheng Liang, Wei Fan | With the insight that each medical question can be considered as a sample from the latent distribution of questions given answers, we propose an automated medical QA pair generation framework, consisting of an unsupervised key phrase detector that explores unstructured material for validity, and a generator that involves a multi-pass decoder to integrate structural knowledge for diversity. |
| 1081 | IntroVNMT: An Introspective Model for Variational Neural Machine Translation | Xin Sheng, Linli Xu, Junliang Guo, Jingchang Liu, Ruoyu Zhao, Yinlong Xu | We propose a novel introspective model for variational neural machine translation (IntroVNMT) in this paper, inspired by the recent successful application of introspective variational autoencoder (IntroVAE) in high quality image synthesis. |
| 1082 | Understanding Medical Conversations with Scattered Keyword Attention and Weak Supervision from Responses | Xiaoming Shi, Haifeng Hu, Wanxiang Che, Zhongqian Sun, Ting Liu, Junzhou Huang | In this work, we consider the medical slot filling problem, i.e., the problem of converting medical queries into structured representations which is a challenging task. |
| 1083 | Latent-Variable Non-Autoregressive Neural Machine Translation with Deterministic Inference Using a Delta Posterior | Raphael Shu, Jason Lee, Hideki Nakayama, Kyunghyun Cho | Inspired by recent refinement-based approaches, we propose LaNMT, a latent-variable non-autoregressive model with continuous latent variables and deterministic inference procedure. |
| 1084 | Evaluating the Cross-Lingual Effectiveness of Massively Multilingual Neural Machine Translation | Aditya Siddhant, Melvin Johnson, Henry Tsai, Naveen Ari, Jason Riesa, Ankur Bapna, Orhan Firat, Karthik Raman | In this paper, we evaluate the cross-lingual effectiveness of representations from the encoder of a massively multilingual NMT model on 5 downstream classification and sequence labeling tasks covering a diverse set of over 50 languages. |
| 1085 | Low Resource Sequence Tagging with Weak Labels | Edwin Simpson, Jonas Pfeiffer, Iryna Gurevych | In this paper, we propose a domain adaptation method using Bayesian sequence combination to exploit pre-trained models and unreliable crowdsourced data that does not require high resource data in a different language. |
| 1086 | Modelling Form-Meaning Systematicity with Linguistic and Visual Features | Arie Soeteman, Dario Gutierrez, Elia Bruni, Ekaterina Shutova | In this paper, we investigate to what extent our visual experience explains some of the form-meaning systematicity found in language. |
| 1087 | Generating Persona Consistent Dialogues by Exploiting Natural Language Inference | Haoyu Song, Wei-Nan Zhang, Jingwen Hu, Ting Liu | In this paper, we exploit the advantages of natural language inference (NLI) technique to address the issue of generating persona consistent dialogues. |
| 1088 | Alignment-Enhanced Transformer for Constraining NMT with Pre-Specified Translations | Kai Song, Kun Wang, Heng Yu, Yue Zhang, Zhongqiang Huang, Weihua Luo, Xiangyu Duan, Min Zhang | We address this problem by introducing a dedicated head in the multi-head Transformer architecture to capture external supervision signals. |
| 1089 | Joint Parsing and Generation for Abstractive Summarization | Kaiqiang Song, Logan Lebanoff, Qipeng Guo, Xipeng Qiu, Xiangyang Xue, Chen Li, Dong Yu, Fei Liu | In this paper we propose to remedy this problem by jointly generating a sentence and its syntactic dependency parse while performing abstraction. |
| 1090 | Controlling the Amount of Verbatim Copying in Abstractive Summarization | Kaiqiang Song, Bingqing Wang, Zhe Feng, Ren Liu, Fei Liu | In this paper, we present a neural summarization model that, by learning from single human abstracts, can produce a broad spectrum of summaries ranging from purely extractive to highly generative ones. |
| 1091 | Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline Generation | Yun-Zhu Song, Hong-Han Shuai, Sung-Lin Yeh, Yi-Lun Wu, Lun-Wei Ku, Wen-Chih Peng | In this paper, we generate inspired headlines that preserve the nature of news articles and catch the eye of the reader simultaneously. |
| 1092 | Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets | Saku Sugawara, Pontus Stenetorp, Kentaro Inui, Akiko Aizawa | We propose a semi-automated, ablation-based methodology for this challenge; By checking whether questions can be solved even after removing features associated with a skill requisite for language understanding, we evaluate to what degree the questions do not require the skill. |
| 1093 | Relation Extraction with Convolutional Network over Learnable Syntax-Transport Graph | Kai Sun, Richong Zhang, Yongyi Mao, Samuel Mensah, Xudong Liu | In this work, we learn to transform the dependency tree into a weighted graph by considering the syntax dependencies of the connected nodes and persisting the structure of the original dependency tree. |
| 1094 | Learning Sparse Sharing Architectures for Multiple Tasks | Tianxiang Sun, Yunfan Shao, Xiaonan Li, Pengfei Liu, Hang Yan, Xipeng Qiu, Xuanjing Huang | In this paper, we propose a novel parameter sharing mechanism, named Sparse Sharing. |
| 1095 | History-Adaption Knowledge Incorporation Mechanism for Multi-Turn Dialogue System | Yajing Sun, Yue Hu, Luxi Xing, Jing Yu, Yuqiang Xie | So we design a history-adaption knowledge incorporation mechanism to build an effective multi-turn dialogue model. |
| 1096 | SPARQA: Skeleton-Based Semantic Parsing for Complex Questions over Knowledge Bases | Yawei Sun, Lingling Zhang, Gong Cheng, Yuzhong Qu | In this paper, we propose a novel skeleton grammar to represent the high-level structure of a complex question. |
| 1097 | Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning | Yibo Sun, Duyu Tang, Nan Duan, Yeyun Gong, Xiaocheng Feng, Bing Qin, Daxin Jiang | Our goal is to learn a neural semantic parser when only prior knowledge about a limited number of simple rules is available, without access to either annotated programs or execution results. |
| 1098 | ERNIE 2.0: A Continual Pre-Training Framework for Language Understanding | Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Hao Tian, Hua Wu, Haifeng Wang | Based on this framework, we construct several tasks and train the ERNIE 2.0 model to capture lexical, syntactic and semantic aspects of information in the training data. |
| 1099 | Generating Diverse Translation by Manipulating Multi-Head Attention | Zewei Sun, Shujian Huang, Hao-Ran Wei, Xin-yu Dai, Jiajun Chen | In this paper, we report an interesting phenomenon in its encoder-decoder multi-head attention: different attention heads of the final decoder layer align to different word translation candidates. |
| 1100 | TreeGen: A Tree-Based Transformer Architecture for Code Generation | Zeyu Sun, Qihao Zhu, Yingfei Xiong, Yican Sun, Lili Mou, Lu Zhang | In this paper, we propose a novel tree-based neural architecture, TreeGen, for code generation. |
| 1101 | Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis | Zhongkai Sun, Prathusha Sarma, William Sethares, Yingyu Liang | This paper proposes a novel model, the Interaction Canonical Correlation Network (ICCN), to learn such multimodal embeddings. |
| 1102 | Distributed Representations for Arithmetic Word Problems | Sowmya S Sundaram, Deepak P, Savitha Sam Abraham | We consider the task of learning distributed representations for arithmetic word problems. |
| 1103 | Adapting Language Models for Non-Parallel Author-Stylized Rewriting | Bakhtiyar Syed, Gaurav Verma, Balaji Vasan Srinivasan, Anandhavelu Natarajan, Vasudeva Varma | Given the recent progress in language modeling using Transformer-based neural models and an active interest in generating stylized text, we present an approach to leverage the generalization capabilities of a language model to rewrite an input text in a target author’s style. |
| 1104 | Boundary Enhanced Neural Span Classification for Nested Named Entity Recognition | Chuanqi Tan, Wei Qiu, Mosha Chen, Rui Wang, Fei Huang | To tackle the above two issues, we propose a boundary enhanced neural span classification model. |
| 1105 | Multi-Label Patent Categorization with Non-Local Attention-Based Graph Convolutional Network | Pingjie Tang, Meng Jiang, Bryan (Ning) Xia, Jed W. Pitera, Jeffrey Welser, Nitesh V. Chawla | In this work, we propose a label attention model based on graph convolutional network. |
| 1106 | Capturing Sentence Relations for Answer Sentence Selection with Multi-Perspective Graph Encoding | Zhixing Tian, Yuanzhe Zhang, Xinwei Feng, Wenbin Jiang, Yajuan Lyu, Kang Liu, Jun Zhao | Unlike previous work, which only models the relation between the question and each candidate sentence, we propose Multi-Perspective Graph Encoder (MPGE) to take the relations among the candidate sentences into account and capture the relations from multiple perspectives. |
| 1107 | Image Enhanced Event Detection in News Articles | Meihan Tong, Shuai Wang, Yixin Cao, Bin Xu, Juanzi Li, Lei Hou, Tat-Seng Chua | Existing works mainly focus on using textual context information, while there naturally exist many images accompanied by news articles that are yet to be explored. |
| 1108 | Fine-Grained Argument Unit Recognition and Classification | Dietrich Trautmann, Johannes Daxenberger, Christian Stab, Hinrich Schütze, Iryna Gurevych | In this work, we argue that the task should be performed on a more fine-grained level of sequence labeling. We present a dataset of arguments from heterogeneous sources annotated as spans of tokens within a sentence, as well as with a corresponding stance. |
| 1109 | Sentence Generation for Entity Description with Content-Plan Attention | Bayu Trisedya, Jianzhong Qi, Rui Zhang | To address these problems, we propose a novel attention model that exploits content-plan to highlight salient attributes in a proper order. |
| 1110 | Capturing Greater Context for Question Generation | Luu Anh Tuan, Darsh Shah, Regina Barzilay | Our goal is to incorporate interactions across multiple sentences to generate realistic questions for long documents. |
| 1111 | Select, Answer and Explain: Interpretable Multi-Hop Reading Comprehension over Multiple Documents | Ming Tu, Kevin Huang, Guangtao Wang, Jing Huang, Xiaodong He, Bowen Zhou | In this paper, we propose an effective and interpretable Select, Answer and Explain (SAE) system to solve the multi-document RC problem. |
| 1112 | An Annotated Corpus of Reference Resolution for Interpreting Common Grounding | Takuma Udagawa, Akiko Aizawa | To address this problem, we consider reference resolution as the central subtask of common grounding and propose a new resource to study its intermediate process. |
| 1113 | A Comparison of Architectures and Pretraining Methods for Contextualized Multilingual Word Embeddings | Niels van der Heijden, Samira Abnar, Ekaterina Shutova | In this work we (i) perform a comprehensive comparison of state-of-the-art multilingual word and sentence encoders on the tasks of named entity recognition (NER) and part of speech (POS) tagging; and (ii) propose a new method for creating multilingual contextualized word embeddings, compare it to multiple baselines and show that it performs at or above state-of-the-art level in zero-shot transfer settings. |
| 1114 | A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistency | Amir Veyseh, Franck Dernoncourt, Dejing Dou, Thien Nguyen | In this work, we propose a novel model for DE that simultaneously performs the two tasks in a single framework to benefit from their inter-dependencies. |
| 1115 | Multi-View Consistency for Relation Extraction via Mutual Information and Structure Prediction | Amir Veyseh, Franck Dernoncourt, My Thai, Dejing Dou, Thien Nguyen | In order to overcome this issue, we introduce a novel method for RE that simultaneously induces the structures and predicts the relations for the input sentences, thus avoiding the external parsers and potentially leading to better sentence structures for RE. |
| 1116 | Parsing as Pretraining | David Vilares, Michalina Strzyz, Anders Søgaard, Carlos Gómez-Rodríguez | Recent analyses suggest that encoders pretrained for language modeling capture certain morpho-syntactic structure. |
| 1117 | Target-Aspect-Sentiment Joint Detection for Aspect-Based Sentiment Analysis | Hai Wan, Yufei Yang, Jianfeng Du, Yanan Liu, Kunxun Qi, Jeff Z. Pan | To tackle these limitations in ABSA, this paper proposes a novel method for target-aspect-sentiment joint detection. |
| 1118 | Unsupervised Neural Dialect Translation with Commonality and Diversity Modeling | Yu Wan, Baosong Yang, Derek F. Wong, Lidia S. Chao, Haihua Du, Ben C.H. Ao | In this paper, we investigate how to exploit the commonality and diversity between dialects thus to build unsupervised translation models merely accessing to monolingual data. In order to examine the effectiveness of the proposed models, we collect 20 million monolingual corpus for each of Mandarin and Cantonese, which are official language and the most widely used dialect in China. |
| 1119 | Neural Question Generation with Answer Pivot | Bingning Wang, Xiaochuan Wang, Ting Tao, Qi Zhang, Jingfang Xu | In this paper, we treat the answers as the hidden pivot for question generation and combine the question generation and answer selection process in a joint model. |
| 1120 | ReCO: A Large Scale Chinese Reading Comprehension Dataset on Opinion | Bingning Wang, Ting Yao, Qi Zhang, Jingfang Xu, Xiaochuan Wang | This paper presents the ReCO, a human-curated Chinese Reading Comprehension dataset on Opinion. |
| 1121 | Neural Machine Translation with Byte-Level Subwords | Changhan Wang, Kyunghyun Cho, Jiatao Gu | In this paper, we investigate byte-level subwords, specifically byte-level BPE (BBPE), which is compacter than character vocabulary and has no out-of-vocabulary tokens, but is more efficient than using pure bytes only is. |
| 1122 | Bridging the Gap between Pre-Training and Fine-Tuning for End-to-End Speech Translation | Chengyi Wang, Yu Wu, Shujie Liu, Zhenglu Yang, Ming Zhou | To address these issues, we propose a Tandem Connectionist Encoding Network (TCEN) which bridges the gap by reusing all subnets in fine-tuning, keeping the roles of subnets consistent, and pre-training the attention module. |
| 1123 | Improving Knowledge-Aware Dialogue Generation via Knowledge Base Question Answering | Jian Wang, Junhao Liu, Wei Bi, Xiaojiang Liu, Kejing He, Ruifeng Xu, Min Yang | In this paper, we propose a novel knowledge-aware dialogue generation model (called TransDG), which transfers question representation and knowledge matching abilities from knowledge base question answering (KBQA) task to facilitate the utterance understanding and factual knowledge selection for dialogue generation. |
| 1124 | Sentiment Classification in Customer Service Dialogue with Topic-Aware Multi-Task Learning | Jiancheng Wang, Jingjing Wang, Changlong Sun, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou | In this study, we focus on the sentiment classification task in an important type of dialogue, namely customer service dialogue, and propose a novel approach which captures overall information to enhance the classification performance. |
| 1125 | Storytelling from an Image Stream Using Scene Graphs | Ruize Wang, Zhongyu Wei, Piji Li, Qi Zhang, Xuanjing Huang | To this end, we propose a novel graph-based architecture for visual storytelling by modeling the two-level relationships on scene graphs. |
| 1126 | Multi-Task Self-Supervised Learning for Disfluency Detection | Shaolei Wang, Wangxiang Che, Qi Liu, Pengda Qin, Ting Liu, William Yang Wang | To tackle the training data bottleneck, we investigate methods for combining multiple self-supervised tasks-i.e., supervised tasks where data can be collected without manual labeling. First, we construct large-scale pseudo training data by randomly adding or deleting words from unlabeled news data, and propose two self-supervised pre-training tasks: (i) tagging task to detect the added noisy words. |
| 1127 | Probing Brain Activation Patterns by Dissociating Semantics and Syntax in Sentences | Shaonan Wang, Jiajun Zhang, Nan Lin, Chengqing Zong | This paper proposes an alternative framework to study the brain representation of semantics and syntax. |
| 1128 | Multi-Level Head-Wise Match and Aggregation in Transformer for Textual Sequence Matching | Shuohang Wang, Yunshi Lan, Yi Tay, Jing Jiang, Jingjing Liu | In this paper, we propose a new approach to sequence pair matching with Transformer, by learning head-wise matching representations on multiple levels. |
| 1129 | Masking Orchestration: Multi-Task Pretraining for Multi-Role Dialogue Representation Learning | Tianyi Wang, Yating Zhang, Xiaozhong Liu, Changlong Sun, Qiong Zhang | In this work, we investigate dialogue context representation learning with various types unsupervised pretraining tasks where the training objectives are given naturally according to the nature of the utterance and the structure of the multi-role conversation. |
| 1130 | Integrating Deep Learning with Logic Fusion for Information Extraction | Wenya Wang, Sinno Jialin Pan | To combine such logic reasoning capabilities with learning capabilities of deep neural networks, we propose to integrate logical knowledge in the form of first-order logic into a deep learning system, which can be trained jointly in an end-to-end manner. |
| 1131 | Go From the General to the Particular: Multi-Domain Translation with Domain Transformation Networks | Yong Wang, Longyue Wang, Shuming Shi, Victor O.K. Li, Zhaopeng Tu | Previous work shows that the standard neural machine translation (NMT) model, trained on mixed-domain data, generally captures the general knowledge, but misses the domain-specific knowledge. |
| 1132 | TextNAS: A Neural Architecture Search Space Tailored for Text Representation | Yujing Wang, Yaming Yang, Yiren Chen, Jing Bai, Ce Zhang, Guinan Su, Xiaoyu Kou, Yunhai Tong, Mao Yang, Lidong Zhou | In this paper, we argue that the search space is also an important human prior to the success of NAS in different applications. |
| 1133 | Learning Multi-Level Dependencies for Robust Word Recognition | Zhiwei Wang, Hui Liu, Jiliang Tang, Songfan Yang, Gale Yan Huang, Zitao Liu | In this paper, we introduce a robust word recognition framework that captures multi-level sequential dependencies in noised sentences. |
| 1134 | GRET: Global Representation Enhanced Transformer | Rongxiang Weng, Haoran Wei, Shujian Huang, Heng Yu, Lidong Bing, Weihua Luo, Jiajun Chen | In this paper, we propose a novel global representation enhanced Transformer (GRET) to explicitly model global representation in the Transformer network. |
| 1135 | Acquiring Knowledge from Pre-Trained Model to Neural Machine Translation | Rongxiang Weng, Heng Yu, Shujian Huang, Shanbo Cheng, Weihua Luo | In this paper, we propose an Apt framework for acquiring knowledge from pre-trained model to NMT. |
| 1136 | Enhanced Meta-Learning for Cross-Lingual Named Entity Recognition with Minimal Resources | Qianhui Wu, Zijia Lin, Guoxin Wang, Hui Chen, Börje F. Karlsson, Biqing Huang, Chin-Yew Lin | To this end, we present a meta-learning algorithm to find a good model parameter initialization that could fast adapt to the given test case and propose to construct multiple pseudo-NER tasks for meta-training by computing sentence similarities. |
| 1137 | Importance-Aware Learning for Neural Headline Editing | Qingyang Wu, Lei Li, Hao Zhou, Ying Zeng, Zhou Yu | We propose to automate this headline editing process through neural network models to provide more immediate writing support for these social media news writers. To train such a neural headline editing model, we collected a dataset which contains articles with original headlines and professionally edited headlines. |
| 1138 | A Dataset for Low-Resource Stylized Sequence-to-Sequence Generation | Yu Wu, Yunli Wang, Shujie Liu | We provide three baselines, the pivot-based method, the teacher-student method, and the back-translation method. We construct two large-scale, multiple-reference datasets for low-resource stylized S2S, the Machine Translation Formality Corpus (MTFC) that is easy to evaluate and the Twitter Conversation Formality Corpus (TCFC) that tackles an important problem in chatbots. |
| 1139 | Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction | Zhen Wu, Fei Zhao, Xin-Yu Dai, Shujian Huang, Jiajun Chen | In this paper, we propose a novel model to transfer these opinions knowledge from resource-rich review sentiment classification datasets to low-resource task TOWE. |
| 1140 | Copy or Rewrite: Hybrid Summarization with Hierarchical Reinforcement Learning | Liqiang Xiao, Lu Wang, Hao He, Yaohui Jin | To address this problem, we propose HySum, a hybrid framework for summarization that can flexibly switch between copying sentence and rewriting sentence according to the degree of redundancy. |
| 1141 | Joint Entity and Relation Extraction with a Hybrid Transformer and Reinforcement Learning Based Model | Ya Xiao, Chengxiang Tan, Zhijie Fan, Qian Xu, Wenye Zhu | We propose a hybrid deep neural network model to jointly extract the entities and relations, and the model is also capable of filtering noisy data. |
| 1142 | Attentive User-Engaged Adversarial Neural Network for Community Question Answering | Yuexiang Xie, Ying Shen, Yaliang Li, Min Yang, Kai Lei | We present an Attentive User-engaged Adversarial Neural Network (AUANN), which interactively learns the context information of questions and answers, and enhances user engagement with the CQA task. |
| 1143 | Hashing Based Answer Selection | Dong Xu, Wu-Jun Li | In this paper, we propose a novel method, called hashing based answer selection (HAS), to tackle this problem. |
| 1144 | Knowledge Graph Grounded Goal Planning for Open-Domain Conversation Generation | Jun Xu, Haifeng Wang, Zhengyu Niu, Hua Wu, Wanxiang Che | To this end, we propose a three-layer Knowledge aware Hierarchical Reinforcement Learning based Model (KnowHRL). |
| 1145 | The Value of Paraphrase for Knowledge Base Predicates | Bingcong Xue, Sen Hu, Lei Zou, Jiashu Cheng | This paper shows a full process of collecting large-scale and high-quality paraphrase dictionaries for predicates in knowledge bases, which takes advantage of existing datasets and combines the technologies of machine mining and crowdsourcing. |
| 1146 | Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment | Kun Xu, Linfeng Song, Yansong Feng, Yan Song, Dong Yu | In this paper, we introduce two coordinated reasoning methods, i.e., the Easy-to-Hard decoding strategy and joint entity alignment algorithm. |
| 1147 | Improving Domain-Adapted Sentiment Classification by Deep Adversarial Mutual Learning | Qianming Xue, Wei Zhang, Hongyuan Zha | To improve domain-adapted sentiment classification by learning sentiment from the target domain as well, we devise a novel deep adversarial mutual learning approach involving two groups of feature extractors, domain discriminators, sentiment classifiers, and label probers. |
| 1148 | Knowledge and Cross-Pair Pattern Guided Semantic Matching for Question Answering | Zihan Xu, Hai-Tao Zheng, Shaopeng Zhai, Dong Wang | In this work, a novel knowledge and cross-pair pattern guided semantic matching system (KCG) is proposed, which considers both knowledge and pattern conditions for QA. |
| 1149 | Towards Making the Most of BERT in Neural Machine Translation | Jiacheng Yang, Mingxuan Wang, Hao Zhou, Chengqi Zhao, Weinan Zhang, Yong Yu, Lei Li | In this work, we introduce a concerted training framework (CTnmt) that is the key to integrate the pre-trained LMs to neural machine translation (NMT). |
| 1150 | Alternating Language Modeling for Cross-Lingual Pre-Training | Jian Yang, Shuming Ma, Dongdong Zhang, ShuangZhi Wu, Zhoujun Li, Ming Zhou | In this work, we introduce a novel cross-lingual pre-training method, called Alternating Language Modeling (ALM). |
| 1151 | Generalize Sentence Representation with Self-Inference | Kai-Chou Yang, Hung-Yu Kao | In this paper, we propose Self Inference Neural Network (SINN), a simple yet efficient sentence encoder which leverages knowledge from recurrent and convolutional neural networks. |
| 1152 | End-to-End Bootstrapping Neural Network for Entity Set Expansion | Lingyong Yan, Xianpei Han, Ben He, Le Sun | In this paper, we propose an end-to-end bootstrapping neural network for entity set expansion, named BootstrapNet, which models the bootstrapping in an encoder-decoder architecture. |
| 1153 | Be Relevant, Non-Redundant, and Timely: Deep Reinforcement Learning for Real-Time Event Summarization | Min Yang, Chengming Li, Fei Sun, Zhou Zhao, Ying Shen, Chenglin Wu | In this paper, we propose a Deep Reinforcement learning framework for real-time Event Summarization (DRES), which shows promising performance for resolving all three challenges (i.e., relevance, non-redundancy, timeliness) in a unified framework. |
| 1154 | Visual Agreement Regularized Training for Multi-Modal Machine Translation | Pengcheng Yang, Boxing Chen, Pei Zhang, Xu Sun | To make better use of visual information, this work presents visual agreement regularized training. |
| 1155 | Causally Denoise Word Embeddings Using Half-Sibling Regression | Zekun Yang, Tianlin Liu | In line with these investigations, we introduce a novel word vector postprocessing scheme under a causal inference framework. |
| 1156 | A Causal Inference Method for Reducing Gender Bias in Word Embedding Relations | Zekun Yang, Juan Feng | In this paper, we design a causal and simple approach for mitigating gender bias in word vector relation by utilizing the statistical dependency between gender-definition word embeddings and gender-biased word embeddings. |
| 1157 | Integrating Relation Constraints with Neural Relation Extractors | Yuan Ye, Yansong Feng, Bingfeng Luo, Yuxuan Lai, Dongyan Zhao | In this paper, we propose a unified framework to integrate relation constraints with NNs by introducing a new loss term, Constraint Loss. |
| 1158 | MixPoet: Diverse Poetry Generation via Learning Controllable Mixed Latent Space | Xiaoyuan Yi, Ruoyu Li, Cheng Yang, Wenhao Li, Maosong Sun | Inspired by this, we propose MixPoet, a novel model that absorbs multiple factors to create various styles and promote diversity. |
| 1159 | PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network | Dacheng Yin, Chong Luo, Zhiwei Xiong, Wenjun Zeng | In this paper, we propose a phase-and-harmonics-aware deep neural network (DNN), named PHASEN, for this task. |
| 1160 | Meta-CoTGAN: A Meta Cooperative Training Paradigm for Improving Adversarial Text Generation | Haiyan Yin, Dingcheng Li, Xu Li, Ping Li | In this paper, we propose a novel approach which aims to improve the performance of adversarial text generation via efficiently decelerating mode collapse of the adversarial training. |
| 1161 | Dialog State Tracking with Reinforced Data Augmentation | Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu | In this paper, we address this difficulty by proposing a reinforcement learning (RL) based framework for data augmentation that can generate high-quality data to improve the neural state tracker. |
| 1162 | Enhancing Pointer Network for Sentence Ordering with Pairwise Ordering Predictions | Yongjing Yin, Fandong Meng, Jinsong Su, Yubin Ge, Lingeng Song, Jie Zhou, Jiebo Luo | To address this deficiency, we propose to enhance the pointer network decoder by using two pairwise ordering prediction modules: The FUTURE module predicts the relative orientations of other unordered sentences with respect to the candidate sentence, and the HISTORY module measures the local coherence between several (e.g., 2) previously ordered sentences and the candidate sentence, without the influence of noisy left-side context. |
| 1163 | Automatic Generation of Headlines for Online Math Questions | Ke Yuan, Dafang He, Zhuoren Jiang, Liangcai Gao, Zhi Tang, C. Lee Giles | To address these issues, we propose MathSum, a novel summarization model which utilizes a pointer mechanism combined with a multi-head attention mechanism for mathematical representation augmentation. |
| 1164 | Improving Context-Aware Neural Machine Translation Using Self-Attentive Sentence Embedding | Hyeongu Yun, Yongkeun Hwang, Kyomin Jung | In this paper, we propose Hierarchical Context Encoder (HCE) that is able to exploit multiple context sentences separately using the hierarchical FAN structure. |
| 1165 | CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning | Daojian Zeng, Haoran Zhang, Qianying Liu | Among existing methods, CopyRE is effective and novel, which uses a sequence-to-sequence framework and copy mechanism to directly generate the relation triplets. |
| 1166 | Neural Simile Recognition with Cyclic Multitask Learning and Local Attention | Jiali Zeng, Linfeng Song, Jinsong Su, Jun Xie, Wei Song, Jiebo Luo | We propose a novel cyclic multitask learning framework for neural simile recognition, which stacks the subtasks and makes them into a loop by connecting the last to the first. |
| 1167 | Span Model for Open Information Extraction on Accurate Corpus | Junlang Zhan, Hai Zhao | In this work, we first alleviate this difficulty from both sides of training and test sets. For the latter, we present our accurately re-annotated benchmark test set (Re-OIE2016) according to a series of linguistic observation and analysis. |
| 1168 | Multi-Point Semantic Representation for Intent Classification | Jinghan Zhang, Yuxiao Ye, Yue Zhang, Likun Qiu, Bin Fu, Yang Li, Zhenglu Yang, Jian Sun | Besides, we propose a compositional intent bi-attention model under multi-task learning with three kinds of attention mechanisms among queries, labels and factors, which jointly combines coarse-grained intent and fine-grained factor information. |
| 1169 | Graph LSTM with Context-Gated Mechanism for Spoken Language Understanding | Linhao Zhang, Dehong Ma, Xiaodong Zhang, Xiaohui Yan, Houfeng Wang | In this paper, we propose to tackle this task with Graph LSTM, which first converts text into a graph and then utilizes the message passing mechanism to learn the node representation. |
| 1170 | Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification | Mozhi Zhang, Yoshinari Fujinuma, Jordan Boyd-Graber | We present a cross-lingual document classification framework (caco) that exploits cross-lingual subword similarity by jointly training a character-based embedder and a word-based classifier. |
| 1171 | Structure Learning for Headline Generation | Ruqing Zhang, Jiafeng Guo, Yixing Fan, Yanyan Lan, Xueqi Cheng | In this paper, therefore, we propose to incorporate structure learning into the graph-based neural models for headline generation. |
| 1172 | DCMN+: Dual Co-Matching Network for Multi-Choice Reading Comprehension | Shuailiang Zhang, Hai Zhao, Yuwei Wu, Zhuosheng Zhang, Xi Zhou, Xiang Zhou | In this work, we propose dual co-matching network (DCMN) which models the relationship among passage, question and answer options bidirectionally. |
| 1173 | Learning Long- and Short-Term User Literal-Preference with Multimodal Hierarchical Transformer Network for Personalized Image Caption | Wei Zhang, Yue Ying, Pan Lu, Hongyuan Zha | To bridge this gap, we develop a novel multimodal hierarchical transformer network (MHTN) for personalized image caption in this paper. |
| 1174 | Learning Conceptual-Contextual Embeddings for Medical Text | Xiao Zhang, Dejing Dou, Ji Wu | We introduce a contextual text representation model called Conceptual-Contextual (CC) embeddings, which incorporates structured knowledge into text representations. |
| 1175 | Filling Conversation Ellipsis for Better Social Dialog Understanding | Xiyuan Zhang, Chengxi Li, Dian Yu, Samuel Davidson, Zhou Yu | To address this issue, we propose a method which considers both the original utterance that has ellipsis and the automatically completed utterance in dialog act and semantic role labeling tasks. We also present an open-domain human-machine conversation dataset with manually completed user utterances and annotated semantic role labeling after manual completion. |
| 1176 | CFGNN: Cross Flow Graph Neural Networks for Question Answering on Complex Tables | Xuanyu Zhang | To leverage more context information flow comprehensively, we propose novel cross flow graph neural networks in this paper. |
| 1177 | Task-Oriented Dialog Systems That Consider Multiple Appropriate Responses under the Same Context | Yichi Zhang, Zhijian Ou, Zhou Yu | We propose a Multi-Action Data Augmentation (MADA) framework to utilize the one-to-many property to generate diverse appropriate dialog responses. |
| 1178 | Relational Graph Neural Network with Hierarchical Attention for Knowledge Graph Completion | Zhao Zhang, Fuzhen Zhuang, Hengshu Zhu, Zhiping Shi, Hui Xiong, Qing He | To this end, we propose a Relational Graph neural network with Hierarchical ATtention (RGHAT) for the KGC task. |
| 1179 | Distilling Knowledge from Well-Informed Soft Labels for Neural Relation Extraction | Zhenyu Zhang, Xiaobo Shu, Bowen Yu, Tingwen Liu, Jiapeng Zhao, Quangang Li, Li Guo | In this paper, we aim to explore the supervision with soft labels in relation extraction, which makes it possible to integrate prior knowledge. |
| 1180 | Semantics-Aware BERT for Language Understanding | Zhuosheng Zhang, Yuwei Wu, Hai Zhao, Zuchao Li, Shuailiang Zhang, Xi Zhou, Xiang Zhou | To promote natural language understanding, we propose to incorporate explicit contextual semantics from pre-trained semantic role labeling, and introduce an improved language representation model, Semantics-aware BERT (SemBERT), which is capable of explicitly absorbing contextual semantics over a BERT backbone. |
| 1181 | SG-Net: Syntax-Guided Machine Reading Comprehension | Zhuosheng Zhang, Yuwei Wu, Junru Zhou, Sufeng Duan, Hai Zhao, Rui Wang | In this work, we propose using syntax to guide the text modeling by incorporating explicit syntactic constraints into attention mechanism for better linguistically motivated word representations. |
| 1182 | Weakly-Supervised Opinion Summarization by Leveraging External Information | Chao Zhao, Snigdha Chaturvedi | This work proposes AspMem, a generative method that contains an array of memory cells to store aspect-related knowledge. |
| 1183 | Reinforced Curriculum Learning on Pre-Trained Neural Machine Translation Models | Mingjun Zhao, Haijiang Wu, Di Niu, Xiaoli Wang | In this paper, we aim to learn a curriculum for improving a pre-trained NMT model by re-selecting influential data samples from the original training set and formulate this task as a reinforcement learning problem. |
| 1184 | Balancing Quality and Human Involvement: An Effective Approach to Interactive Neural Machine Translation | Tianxiang Zhao, Lemao Liu, Guoping Huang, Huayang Li, Yingling Liu, Liu GuiQuan, Shuming Shi | In response to these pitfalls, we propose a novel interactive NMT model, which explicitly accounts the history of human involvements and particularly is optimized towards two objectives corresponding to the translation quality and the cost of human involvement, respectively. |
| 1185 | Semi-Supervised Text Simplification with Back-Translation and Asymmetric Denoising Autoencoders | Yanbin Zhao, Lu Chen, Zhi Chen, Kai Yu | To tackle this problem, we propose asymmetric denoising methods for sentences with separate complexity. |
| 1186 | Dynamic Reward-Based Dueling Deep Dyna-Q: Robust Policy Learning in Noisy Environments | Yangyang Zhao, Zhenyu Wang, Kai Yin, Rui Zhang, Zhenhua Huang, Pei Wang | In this paper, we propose a new approach, called Dynamic Reward-based Dueling Deep Dyna-Q (DR-D3Q). |
| 1187 | Replicate, Walk, and Stop on Syntax: An Effective Neural Network Model for Aspect-Level Sentiment Classification | Yaowei Zheng, Richong Zhang, Samuel Mensah, Yongyi Mao | For this purpose, we present a neural network model named RepWalk which performs a replicated random walk on a syntax graph, to effectively focus on the informative contextual words. |
| 1188 | A Pre-Training Based Personalized Dialogue Generation Model with Persona-Sparse Data | Yinhe Zheng, Rongsheng Zhang, Minlie Huang, Xiaoxi Mao | This paper proposes a pre-training based personalized dialogue model that can generate coherent responses using persona-sparse dialogue data. |
| 1189 | JEC-QA: A Legal-Domain Question Answering Dataset | Haoxi Zhong, Chaojun Xiao, Cunchao Tu, Tianyang Zhang, Zhiyuan Liu, Maosong Sun | We present JEC-QA, the largest question answering dataset in the legal domain, collected from the National Judicial Examination of China. |
| 1190 | Discourse Level Factors for Sentence Deletion in Text Simplification | Yang Zhong, Chao Jiang, Wei Xu, Junyi Jessy Li | This paper presents a data-driven study focusing on analyzing and predicting sentence deletion — a prevalent but understudied phenomenon in document simplification — on a large English text simplification corpus. |
| 1191 | Learning to Compare for Better Training and Evaluation of Open Domain Natural Language Generation Models | Wangchunshu Zhou, Ke Xu | In our paper, we propose to evaluate natural language generation models by learning to compare a pair of generated sentences by fine-tuning BERT, which has been shown to have good natural language understanding ability. |
| 1192 | Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension | Xiaorui Zhou, Senlin Luo, Yunfang Wu | To solve the first problem, we propose a co-attention enhanced hierarchical architecture to better capture the interactions between the article and question, thus guide the decoder to generate more coherent distractors. |
| 1193 | Evaluating Commonsense in Pre-Trained Language Models | Xuhui Zhou, Yue Zhang, Leyang Cui, Dandan Huang | We study the commonsense ability of GPT, BERT, XLNet, and RoBERTa by testing them on seven challenging benchmarks, finding that language modeling and its variants are effective objectives for promoting models’ commonsense ability while bi-directional context and larger training set are bonuses. We release a test set, named CATs publicly, for future research. |
| 1194 | Who Did They Respond to? Conversation Structure Modeling Using Masked Hierarchical Transformer | Henghui Zhu, Feng Nan, Zhiguo Wang, Ramesh Nallapati, Bing Xiang | In this work, we define the problem of conversation structure modeling as identifying the parent utterance(s) to which each utterance in the conversation responds to. |
| 1195 | Multimodal Summarization with Guidance of Multimodal Reference | Junnan Zhu, Yu Zhou, Jiajun Zhang, Haoran Li, Chengqing Zong, Changliang Li | To alleviate this problem, we propose a multimodal objective function with the guidance of multimodal reference to use the loss from the summary generation and the image selection. |
| 1196 | LATTE: Latent Type Modeling for Biomedical Entity Linking | Ming Zhu, Busra Celikkaya, Parminder Bhatia, Chandan K. Reddy | Thus, we propose LATTE, a LATent Type Entity Linking model, that improves entity linking by modeling the latent fine-grained type information about mentions and entities. |
| 1197 | Hybrid Compositional Reasoning for Reactive Synthesis from Finite-Horizon Specifications | Suguman Bansal, Yong Li, Lucas Tabajara, Moshe Vardi | This work proposes a hybrid representation approach for the conversion. |
| 1198 | On Succinct Groundings of HTN Planning Problems | Gregor Behnke, Daniel Höller, Alexander Schmid, Pascal Bercher, Susanne Biundo | In this paper we present a new approach for grounding HTN planning problems that produces smaller groundings in a shorter timespan than the previously published method. |
| 1199 | POP ≡ POCL, Right? Complexity Results for Partial Order (Causal Link) Makespan Minimization | Pascal Bercher, Conny Olz | As a first contribution, we study the similarities and differences of PO and POCL plans, thereby clarifying a common misconception about their relationship: There are PO plans for which there does not exist a POCL plan with the same orderings. |
| 1200 | Reinforcement Learning of Risk-Constrained Policies in Markov Decision Processes | Tomáš Brázdil, Krishnendu Chatterjee, Petr Novotný, Jiří Vahala | Our main contribution is an efficient risk-constrained planning algorithm that combines UCT-like search with a predictor learned through interaction with the MDP (in the style of AlphaZero) and with a risk-constrained action selection via linear programming. |
| 1201 | Planning and Acting with Non-Deterministic Events: Navigating between Safe States | Lukas Chrpa, Jakub Gemrot, Martin Pilat | We introduce a technique for generating plans that constrains the number of consecutive “unsafe” actions in a plan and a technique for generating “robust” plans that effectively evade event effects. |
| 1202 | Optimizing Reachability Sets in Temporal Graphs by Delaying | Argyrios Deligkas, Igor Potapov | In this paper, we study how changes of the time labels, corresponding to delays on the availability of the edges, affect the reachability sets from given sources. |
| 1203 | A New Approach to Plan-Space Explanation: Analyzing Plan-Property Dependencies in Oversubscription Planning | Rebecca Eifler, Michael Cashmore, Jörg Hoffmann, Daniele Magazzeni, Marcel Steinmetz | We propose to answer this kind of question with the goal conjunctions B excluded by A, i. e., that could not be achieved if A were to be enforced. |
| 1204 | Beliefs We Can Believe in: Replacing Assumptions with Data in Real-Time Search | Maximilian Fickert, Tianyi Gu, Leonhard Staut, Wheeler Ruml, Joerg Hoffmann, Marek Petrik | In this paper, we explore how to replace these assumptions with actual data. |
| 1205 | Lifted Fact-Alternating Mutex Groups and Pruned Grounding of Classical Planning Problems | Daniel Fišer | In this paper, we focus on the inference of mutex groups in the lifted (PDDL) representation. |
| 1206 | Time-Inconsistent Planning: Simple Motivation Is Hard to Find | Fedor V. Fomin, Torstein J. F. Strømme | In this paper, we study the complexity of finding a choice reduction for the agent; that is, how to remove edges and vertices from the task graph such that a present-biased agent will remain motivated to reach his target even for a limited reward. |
| 1207 | Dynamic Control of Probabilistic Simple Temporal Networks | Michael Gao, Lindsay Popowski, Jim Boerkoel | In this paper, we propose two new dynamic controllability algorithms that attempt to maximize the likelihood of successfully executing a schedule within a PSTN. |
| 1208 | Decidability and Complexity of Action-Based Temporal Planning over Dense Time | Nicola Gigante, Andrea Micheli, Angelo Montanari, Enrico Scala | We prove the problem to be PSPACE-complete when self-overlap is forbidden, whereas, when allowed, it becomes EXPSPACE-complete with ϵ-separation and undecidable with non-zero separation. |
| 1209 | Solving Sum-of-Costs Multi-Agent Pathfinding with Answer-Set Programming | Rodrigo N. Gómez, Carlos Hernández, Jorge A. Baier | In this paper, we propose the first family of compilations to ASP that solve sum-of-costs MAPF over 4-connected grids. |
| 1210 | Novel Is Not Always Better: On the Relation between Novelty and Dominance Pruning | Joschka Gross, Alvaro Torralba, Maximilian Fickert | We relate novelty to dominance pruning, which compares states to previously seen states to eliminate those that are provably worse in terms of goal distance. |
| 1211 | HDDL: An Extension to PDDL for Expressing Hierarchical Planning Problems | Daniel Höller, Gregor Behnke, Pascal Bercher, Susanne Biundo, Humbert Fiorino, Damien Pellier, Ron Alford | In this paper, we propose an extension to PDDL, the description language used in non-hierarchical planning, to the needs of hierarchical planning systems. |
| 1212 | Reshaping Diverse Planning | Michael Katz, Shirin Sohrabi | We propose a novel approach to diverse planning, exploiting existing classical planners via planning task reformulation and choosing a subset of plans of required size in post-processing. |
| 1213 | Top-Quality Planning: Finding Practically Useful Sets of Best Plans | Michael Katz, Shirin Sohrabi, Octavian Udrea | Recent work in diverse planning introduced additionally restrictions on solution quality. |
| 1214 | Information Shaping for Enhanced Goal Recognition of Partially-Informed Agents | Sarah Keren, Haifeng Xu, Kofi Kwapong, David Parkes, Barbara Grosz | We formally define this problem, and suggest a pruning approach for efficiently searching the search space. |
| 1215 | Monte Carlo Tree Search in Continuous Spaces Using Voronoi Optimistic Optimization with Regret Bounds | Beomjoon Kim, Kyungjae Lee, Sungbin Lim, Leslie Kaelbling, Tomas Lozano-Perez | We provide a novel MCTS algorithm (voot) for deterministic environments with continuous action spaces, which, in turn, is based on a novel black-box function-optimization algorithm (voo) to efficiently sample actions. |
| 1216 | Idle Time Optimization for Target Assignment and Path Finding in Sortation Centers | Ngai Meng Kou, Cheng Peng, Hang Ma, T. K. Satish Kumar, Sven Koenig | In this paper, we study the one-shot and lifelong versions of the Target Assignment and Path Finding problem in automated sortation centers, where each agent needs to constantly assign itself a sorting station, move to its assigned station without colliding with obstacles or other agents, wait in the queue of that station to obtain a parcel for delivery, and then deliver the parcel to a sorting bin. |
| 1217 | Semantic Attachments for HTN Planning | Maurício Cecílio Magnaguagno, Felipe Meneguzzi | We formalize Semantic Attachments for HTN planning using semi coroutines, allowing such procedurally defined predicates to link the planning process to custom unifications outside of the planner, such as numerical results from a robotics simulator. |
| 1218 | Automated Synthesis of Social Laws in STRIPS | Ronen Nir, Alexander Shleyfman, Erez Karpas | In this paper, we address the problem of automatically synthesizing a robust social law for a given multi-agent environment. |
| 1219 | Generalized Planning with Positive and Negative Examples | Javier Segovia-Aguas, Sergio Jiménez, Anders Jonsson | In this paper we define negative examples for generalized planning as planning instances that must not be solved by a generalized plan. |
| 1220 | Active Goal Recognition | Maayan Shvo, Sheila A. McIlraith | To this end, we present an algorithm for active goal recognition and a landmark-based approach to the elimination of hypothesized goals which leverages automated planning. |
| 1221 | Symbolic Top-k Planning | David Speck, Robert Mattmüller, Bernhard Nebel | In this paper we show that, in general, the decision problem version of top-k planning is PSPACE-complete, as is the decision problem version of ordinary classical planning. |
| 1222 | Temporal Planning with Intermediate Conditions and Effects | Alessandro Valentini, Andrea Micheli, Alessandro Cimatti | In this paper, we address this limitation by providing an effective heuristic-search technique for temporal planning, allowing the definition of actions with conditions and effects at any arbitrary time within the action duration. |
| 1223 | Neural Architecture Search Using Deep Neural Networks and Monte Carlo Tree Search | Linnan Wang, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, Rodrigo Fonseca | In this paper, we present a novel scalable Monte Carlo Tree Search (MCTS) based NAS agent, named AlphaX, to tackle these two aspects. |
| 1224 | Planning with Abstract Learned Models While Learning Transferable Subtasks | John Winder, Stephanie Milani, Matthew Landen, Erebus Oh, Shane Parr, Shawn Squire, Marie desJardins, Cynthia Matuszek | We introduce an algorithm for model-based hierarchical reinforcement learning to acquire self-contained transition and reward models suitable for probabilistic planning at multiple levels of abstraction. |
| 1225 | NeoNav: Improving the Generalization of Visual Navigation via Generating Next Expected Observations | Qiaoyun Wu, Dinesh Manocha, Jun Wang, Kai Xu | We propose improving the cross-target and cross-scene generalization of visual navigation through learning an agent that is guided by conceiving the next observations it expects to see. |
| 1226 | Refining HTN Methods via Task Insertion with Preferences | Zhanhao Xiao, Hai Wan, Hankui Hankz Zhuo, Andreas Herzig, Laurent Perrussel, Peilin Chen | In this paper, we propose a novel learning framework to refine HTN methods via task insertion with completely preserving the original methods. |
| 1227 | Computing Superior Counter-Examples for Conformant Planning | Xiaodi Zhang, Alban Grastien, Enrico Scala | In a counter-example based approach to conformant planning, choosing the right counter-example can improve performance. |
| 1228 | Deep Bayesian Nonparametric Learning of Rules and Plans from Demonstrations with a Learned Automaton Prior | Brandon Araki, Kiran Vodrahalli, Thomas Leech, Cristian-Ioan Vasile, Mark Donahue, Daniela Rus | We introduce a method to learn imitative policies from expert demonstrations that are interpretable and manipulable. |
| 1229 | Multi-Fidelity Multi-Objective Bayesian Optimization: An Output Space Entropy Search Approach | Syrine Belakaria, Aryan Deshwal, Janardhan Rao Doppa | In this paper, we propose a novel approach referred as Multi-Fidelity Output Space Entropy Search for Multi-objective Optimization (MF-OSEMO) to solve this problem. |
| 1230 | Uncertainty-Aware Search Framework for Multi-Objective Bayesian Optimization | Syrine Belakaria, Aryan Deshwal, Nitthilan Kannappan Jayakodi, Janardhan Rao Doppa | We propose a novel uncertainty-aware search framework referred to as USeMO to efficiently select the sequence of inputs for evaluation to solve this problem. |
| 1231 | Exchangeable Generative Models with Flow Scans | Christopher Bender, Kevin O'Connor, Yang Li, Juan Garcia, Junier Oliva, Manzil Zaheer | In this work, we develop a new approach to generative density estimation for exchangeable, non-i.i.d. data. |
| 1232 | Point-Based Methods for Model Checking in Partially Observable Markov Decision Processes | Maxime Bouton, Jana Tumova, Mykel J. Kochenderfer | We propose a methodology to synthesize policies that satisfy a linear temporal logic formula in a partially observable Markov decision process (POMDP). |
| 1233 | Scalable Methods for Computing State Similarity in Deterministic Markov Decision Processes | Pablo Samuel Castro | We present new algorithms for computing and approximating bisimulation metrics in Markov Decision Processes (MDPs). |
| 1234 | Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns | YooJung Choi, Golnoosh Farnadi, Behrouz Babaki, Guy Van den Broeck | In this paper, we study fairness of naive Bayes classifiers, which allow partial observations. |
| 1235 | Regret Minimisation in Multi-Armed Bandits Using Bounded Arm Memory | Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan | In this paper, we consider the design of algorithms that are constrained to store statistics from only a bounded number of arms. |
| 1236 | A Calculus for Stochastic Interventions:Causal Effect Identification and Surrogate Experiments | Juan Correa, Elias Bareinboim | In this paper, we investigate a general class of interventions that covers some non-trivial types of policies (conditional and stochastic), which goes beyond the atomic class. Specifically, in this paper, we introduce a new set of inference rules (akin to do-calculus) that can be used to derive claims about general interventions, which we call σ-calculus. |
| 1237 | Reliable and Efficient Anytime Skeleton Learning | Rui Ding, Yanzhi Liu, Jingjing Tian, Zhouyu Fu, Shi Han, Dongmei Zhang | Specifically, we point out that the commonly existing Functional Dependency (FD) among variables could make the learned skeleton violate faithfulness assumption, thus we propose a theory to resolve such incompatibility. |
| 1238 | Deception through Half-Truths | Andrew Estornell, Sanmay Das, Yevgeniy Vorobeychik | We consider the problem of how much an adversary can affect a principal’s decision by “half-truths”, that is, by masking or hiding bits of information, when the principal is oblivious to the presence of the adversary. |
| 1239 | Causal Transfer for Imitation Learning and Decision Making under Sensor-Shift | Jalal Etesami, Philipp Geiger | In this paper, we propose a causal model-based framework for transfer learning under such “sensor-shifts”, for two common LfD tasks: (1) inferring the effect of the demonstrator’s actions and (2) imitation learning. |
| 1240 | Low-Variance Black-Box Gradient Estimates for the Plackett-Luce Distribution | Artyom Gadetsky, Kirill Struminsky, Christopher Robinson, Novi Quadrianto, Dmitry Vetrov | In this work, we consider models with latent permutations and propose control variates for the Plackett-Luce distribution. |
| 1241 | Title | Robert Ganian, Thekla Hamm, Topi Talvitie | We consider the problem of counting the number of DAGs which are Markov-equivalent, i.e., which encode the same conditional independencies between random variables. |
| 1242 | A MaxSAT-Based Framework for Group Testing | Lorenzo Ciampiconi, Bishwamittra Ghosh, Jonathan Scarlett, Kuldeep S Meel | In this paper, we propose a MaxSAT-based framework, called MGT, that solves group testing, in particular, the decoding phase of non-adaptive group testing. |
| 1243 | Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets | Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour | In this paper, we propose a principled method to uniquely identify causal relationships over the integrated set of variables from multiple data sets, in linear, non-Gaussian cases. |
| 1244 | Introducing Probabilistic Bézier Curves for N-Step Sequence Prediction | Ronny Hug, Wolfgang Hübner, Michael Arens | This paper proposes probabilistic B'{e}zier curves (𝒩-Curves) as a basis for effectively modeling continuous-time stochastic processes. |
| 1245 | Probabilistic Reasoning Across the Causal Hierarchy | Duligur Ibeling, Thomas Icard | We propose a formalization of the three-tier causal hierarchy of association, intervention, and counterfactuals as a series of probabilistic logical languages. |
| 1246 | The Choice Function Framework for Online Policy Improvement | Murugeswari Issakkimuthu, Alan Fern, Prasad Tadepalli | To address this issue, we introduce the choice function framework for analyzing online search procedures for policy improvement. |
| 1247 | Estimating Causal Effects Using Weighting-Based Estimators | Yonghan Jung, Jin Tian, Elias Bareinboim | In this paper, we extend weighting-based methods developed for the back-door case to more general settings, and develop novel machinery for estimating causal effects using the weighting-based method as a building block. |
| 1248 | Error-Correcting and Verifiable Parallel Inference in Graphical Models | Negin Karimi, Petteri Kaski, Mikko Koivisto | We present a novel framework for parallel exact inference in graphical models. |
| 1249 | Safe Linear Stochastic Bandits | Kia Khezeli, Eilyan Bitar | We introduce the safe linear stochastic bandit framework—a generalization of linear stochastic bandits—where, in each stage, the learner is required to select an arm with an expected reward that is no less than a predetermined (safe) threshold with high probability. |
| 1250 | General Transportability – Synthesizing Observations and Experiments from Heterogeneous Domains | Sanghack Lee, Juan Correa, Elias Bareinboim | In this paper, we investigate a general version of this challenge where the goal is to learn conditional causal effects from an arbitrary combination of datasets collected under different conditions, observational or experimental, and from heterogeneous populations. |
| 1251 | Temporal Logics Over Finite Traces with Uncertainty | Fabrizio M Maggi, Marco Montali, Rafael Peñaloza | We thus propose a new probabilistic temporal logic over finite traces using superposition semantics, where all possible evolutions are possible, until observed. |
| 1252 | Parallel AND/OR Search for Marginal MAP | Radu Marinescu, Akihiro Kishimoto, Adi Botea | In this paper, we explore and evaluate for the first time the power of parallel search for exact Marginal MAP inference. |
| 1253 | Experimental Design for Optimization of Orthogonal Projection Pursuit Models | Mojmir Mutny, Johannes Kirschner, Andreas Krause | In this work, we go beyond the additivity assumption and use an orthogonal projection pursuit regression model, which strictly generalizes additive models. |
| 1254 | Adversarial Disentanglement with Grouped Observations | Jozsef Nemeth | We consider the disentanglement of the representations of the relevant attributes of the data (content) from all other factors of variations (style) using Variational Autoencoders. |
| 1255 | Few-Shot Bayesian Imitation Learning with Logical Program Policies | Tom Silver, Kelsey R. Allen, Alex K. Lew, Leslie Pack Kaelbling, Josh Tenenbaum | We propose an expressive class of policies, a strong but general prior, and a learning algorithm that, together, can learn interesting policies from very few examples. |
| 1256 | Tandem Inference: An Out-of-Core Streaming Algorithm for Very Large-Scale Relational Inference | Sriram Srinivasan, Eriq Augustine, Lise Getoor | In this work we address this issue by introducing a novel technique called tandem inference (ti). |
| 1257 | BOWL: Bayesian Optimization for Weight Learning in Probabilistic Soft Logic | Sriram Srinivasan, Golnoosh Farnadi, Lise Getoor | In this paper, we introduce a new weight learning approach called Bayesian optimization for weight learning (BOWL) based on Gaussian process regression that directly optimizes weights on a chosen domain performance metric. |
| 1258 | Off-Policy Evaluation in Partially Observable Environments | Guy Tennenholtz, Uri Shalit, Shie Mannor | We define the problem of off-policy evaluation for Partially Observable Markov Decision Processes (POMDPs) and establish what we believe is the first off-policy evaluation result for POMDPs. |
| 1259 | Beyond the Grounding Bottleneck: Datalog Techniques for Inference in Probabilistic Logic Programs | Efthymia Tsamoura, Victor Gutierrez-Basulto, Angelika Kimmig | We propose an alternative approach that uses efficient Datalog techniques to integrate knowledge compilation with forward reasoning with a non-ground program. |
| 1260 | Gradient-Based Optimization for Bayesian Preference Elicitation | Ivan Vendrov, Tyler Lu, Qingqing Huang, Craig Boutilier | We tackle this issue by introducing a continuous formulation of EVOI as a differentiable network that can be optimized using gradient methods available in modern machine learning computational frameworks (e.g., TensorFlow, PyTorch). |
| 1261 | Recovering Causal Structures from Low-Order Conditional Independencies | Marcel Wienöbst, Maciej Liskiewicz | In this paper, we propose an algorithm which, for a given set of conditional independencies of order less or equal to k, where k is a small fixed number, computes a faithful graphical representation of the given set. |
| 1262 | A New Framework for Online Testing of Heterogeneous Treatment Effect | Miao Yu, Wenbin Lu, Rui Song | We propose a new framework for online testing of heterogeneous treatment effects. |
| 1263 | A Simultaneous Discover-Identify Approach to Causal Inference in Linear Models | Chi Zhang, Bryant Chen, Judea Pearl | Rather than performing the two tasks in tandem, as is usually done in the literature, we propose a symbiotic approach in which the two are performed simultaneously for mutual benefit; information gained through identification helps causal discovery and vice versa. |
| 1264 | Modeling Probabilistic Commitments for Maintenance Is Inherently Harder than for Achievement | Qi Zhang, Edmund Durfee, Satinder Singh | Most research on probabilistic commitments focuses on commitments to achieve enabling preconditions for other agents. |
| 1265 | Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series | Tan Zhi-Xuan, Harold Soh, Desmond Ong | In this work, we introduce a factorized inference method for Multimodal Deep Markov Models (MDMMs), allowing us to filter and smooth in the presence of missing data, while also performing uncertainty-aware multimodal fusion. |
| 1266 | That and There: Judging the Intent of Pointing Actions with Robotic Arms | Malihe Alikhani, Baber Khalid, Rahul Shome, Chaitanya Mitash, Kostas Bekris, Matthew Stone | This work proposes a set of interpretive principles for how a robotic arm can use pointing actions to communicate task information to people by extending existing models from the related literature. |
| 1267 | Learning from Interventions Using Hierarchical Policies for Safe Learning | Jing Bi, Vikas Dhiman, Tianyou Xiao, Chenliang Xu | We address these limitations by 1) interpolating the expert’s interventions back in time, and 2) by splitting the policy into two hierarchical levels, one that generates sub-goals for the future and another that generates actions to reach those desired sub-goals. |
| 1268 | On the Problem of Covering a 3-D Terrain | Eduard Eiben, Isuru Godage, Iyad Kanj, Ge Xia | We study the problem of covering a 3-dimensional terrain by a sweeping robot that is equipped with a camera. |
| 1269 | Long-Term Loop Closure Detection through Visual-Spatial Information Preserving Multi-Order Graph Matching | Peng Gao, Hao Zhang | In this paper, we introduce a novel visual-spatial information preserving multi-order graph matching approach for long-term loop closure detection. |
| 1270 | Adversarial Fence Patrolling: Non-Uniform Policies for Asymmetric Environments | Yaniv Oshrat, Noa Agmon, Sarit Kraus | In this work we examine the Fence Patrol problem, in which the robots must travel back and forth along an open polyline and the adversary is aware of the robots’ patrol strategy. |
| 1271 | Task and Motion Planning Is PSPACE-Complete | William Vega-Brown, Nicholas Roy | We present a new representation for task and motion planning that uses constraints to capture both continuous and discrete phenomena in a unified framework. |
| 1272 | AtLoc: Attention Guided Camera Localization | Bing Wang, Changhao Chen, Chris Xiaoxuan Lu, Peijun Zhao, Niki Trigoni, Andrew Markham | In this work, we show that attention can be used to force the network to focus on more geometrically robust objects and features, achieving state-of-the-art performance in common benchmark, even if using only a single image as input. |
| 1273 | RoboCoDraw: Robotic Avatar Drawing with GAN-Based Style Transfer and Time-Efficient Path Optimization | Tianying Wang, Wei Qi Toh, Hao Zhang, Xiuchao Sui, Shaohua Li, Yong Liu, Wei Jing | In this paper we present RoboCoDraw, a real-time collaborative robot-based drawing system that draws stylized human face sketches interactively in front of human users, by using the Generative Adversarial Network (GAN)-based style transfer and a Random-Key Genetic Algorithm (RKGA)-based path optimization. |
| 1274 | Dempster-Shafer Theoretic Learning of Indirect Speech Act Comprehension Norms | Ruchen Wen, Mohammed Aun Siddiqui, Tom Williams | This work builds off of previous research on understanding and generation of ISAs using Dempster-Shafer Theoretic Uncertain Logic, by showing how other recent work in Dempster-Shafer Theoretic rule learning can be used to learn appropriate uncertainty intervals for robots’ representations of sociocultural politeness norms. |
| 1275 | Modular Robot Design Synthesis with Deep Reinforcement Learning | Julian Whitman, Raunaq Bhirangi, Matthew Travers, Howie Choset | This work uses deep reinforcement learning to create a search heuristic that allows us to efficiently search the space of modular serial manipulator designs. |
| 1276 | Visual Tactile Fusion Object Clustering | Tao Zhang, Yang Cong, Gan Sun, Qianqian Wang, Zhenming Ding | To effectively benefit both visual and tactile modalities for object clustering, in this paper, we propose a deep Auto-Encoder-like Non-negative Matrix Factorization framework for visual-tactile fusion clustering. |
| 1277 | Learning End-to-End Scene Flow by Distilling Single Tasks Knowledge | Filippo Aleotti, Matteo Poggi, Fabio Tosi, Stefano Mattoccia | Conversely, we propose DWARF, a novel and lightweight architecture able to infer full scene flow jointly reasoning about depth and optical flow easily and elegantly trainable end-to-end from scratch. |
| 1278 | A Variational Autoencoder with Deep Embedding Model for Generalized Zero-Shot Learning | Peirong Ma, Xiao Hu | In order to tackle such a problem, this paper integrates a deep embedding network (DE) and a modified variational autoencoder (VAE) into a novel model (DE-VAE) to learn a latent space shared by both image features and class embeddings. |
| 1279 | Ultrafast Photorealistic Style Transfer via Neural Architecture Search | Jie An, Haoyi Xiong, Jun Huan, Jiebo Luo | In this work, we propose an effective solution to these issues. |
| 1280 | PsyNet: Self-Supervised Approach to Object Localization Using Point Symmetric Transformation | Kyungjune Baek, Minhyun Lee, Hyunjung Shim | In this paper, we overcome common drawbacks of co-localization techniques by utilizing self-supervised learning approach. |
| 1281 | Detecting Human-Object Interactions via Functional Generalization | Ankan Bansal, Sai Saketh Rambhatla, Abhinav Shrivastava, Rama Chellappa | We present an approach for detecting human-object interactions (HOIs) in images, based on the idea that humans interact with functionally similar objects in a similar manner. |
| 1282 | Incremental Multi-Domain Learning with Network Latent Tensor Factorization | Adrian Bulat, Jean Kossaifi, Georgios Tzimiropoulos, Maja Pantic | In this paper, we present a method to learn new-domains and tasks incrementally, building on prior knowledge from already learned tasks and without catastrophic forgetting. |
| 1283 | Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation | Yingjie Cai, Buyu Li, Zeyu Jiao, Hongsheng Li, Xingyu Zeng, Xiaogang Wang | Since the location recovery in 3D space is quite difficult on account of absence of depth information, this paper proposes a novel unified framework which decomposes the detection problem into a structured polygon prediction task and a depth recovery task. |
| 1284 | Auto-GAN: Self-Supervised Collaborative Learning for Medical Image Synthesis | Bing Cao, Han Zhang, Nannan Wang, Xinbo Gao, Dinggang Shen | To impute missing data with adequate clinical accuracy, here we propose a framework called self-supervised collaborative learning to synthesize missing modality for medical images. |
| 1285 | Feature Deformation Meta-Networks in Image Captioning of Novel Objects | Tingjia Cao, Ke Han, Xiaomei Wang, Lin Ma, Yanwei Fu, Yu-Gang Jiang, Xiangyang Xue | To this end, we introduce the feature deformation meta-networks (FDM-net), which is trained on source data, and learn to adapt to the novel object features detected by the auxiliary detection model. |
| 1286 | General Partial Label Learning via Dual Bipartite Graph Autoencoder | Brian Chen, Bo Wu, Alireza Zareian, Hanwang Zhang, Shih-Fu Chang | In this paper, we propose a novel graph convolutional network (GCN) called Dual Bipartite Graph Autoencoder (DB-GAE) to tackle the label ambiguity challenge of GPLL. |
| 1287 | Learning Deep Relations to Promote Saliency Detection | Changrui Chen, Xin Sun, Yang Hua, Junyu Dong, Hongwei Xv | We introduce a threshold-constrained training pair construction strategy to ensure that we can accurately estimate the relations between different image parts in a self-supervised way. |
| 1288 | Hierarchical Online Instance Matching for Person Search | Di Chen, Shanshan Zhang, Wanli Ouyang, Jian Yang, Bernt Schiele | We argue that simply concatenating detection and re-ID is a sub-optimal solution, and we propose a Hierarchical Online Instance Matching (HOIM) loss which exploits the hierarchical relationship between detection and re-ID to guide the learning of our network. |
| 1289 | Binarized Neural Architecture Search | Hanlin Chen, Li'an Zhuo, Baochang Zhang, Xiawu Zheng, Jianzhuang Liu, David Doermann, Rongrong Ji | To address these issues, we introduce channel sampling and operation space reduction into a differentiable NAS to significantly reduce the cost of searching. |
| 1290 | End-to-End Learning of Object Motion Estimation from Retinal Events for Event-Based Object Tracking | Haosheng Chen, David Suter, Qiangqiang Wu, Hanzi Wang | The main idea behind this work is to propose a novel deep neural network to learn and regress a parametric object-level motion/transform model for event-based object tracking. |
| 1291 | Zero-Shot Ingredient Recognition by Multi-Relational Graph Convolutional Network | Jingjing Chen, Liangming Pan, Zhipeng Wei, Xiang Wang, Chong-Wah Ngo, Tat-Seng Chua | Therefore, in this paper, we target the problem of ingredient recognition with zero training samples. |
| 1292 | Rethinking the Bottom-Up Framework for Query-Based Video Localization | Long Chen, Chujie Lu, Siliang Tang, Jun Xiao, Dong Zhang, Chilie Tan, Xiaolin Li | In this paper, we focus on the task query-based video localization, i.e., localizing a query in a long and untrimmed video. |
| 1293 | Diversity Transfer Network for Few-Shot Learning | Mengting Chen, Yuxin Fang, Xinggang Wang, Heng Luo, Yifeng Geng, Xinyu Zhang, Chang Huang, Wenyu Liu, Bo Wang | To alleviate this problem, we propose a novel generative framework, Diversity Transfer Network (DTN), that learns to transfer latent diversities from known categories and composite them with support features to generate diverse samples for novel categories in feature space. |
| 1294 | Structure-Aware Feature Fusion for Unsupervised Domain Adaptation | Qingchao Chen, Yang Liu | As the MI is hard to measure directly in high-dimension spaces, we adopt a new objective function that implicitly maximizes the MI via an effective sampling strategy and a discriminator design. |
| 1295 | Knowledge Graph Transfer Network for Few-Shot Recognition | Riquan Chen, Tianshui Chen, Xiaolu Hui, Hefeng Wu, Guanbin Li, Liang Lin | In this work, we represent the semantic correlations in the form of structured knowledge graph and integrate this graph into deep neural networks to promote few-shot learning by a novel Knowledge Graph Transfer Network (KGTN). Furthermore, we construct an ImageNet-6K dataset that covers larger scale categories, i.e, 6,000 categories, and experiments on this dataset further demonstrate the effectiveness of our proposed model. |
| 1296 | Expressing Objects Just Like Words: Recurrent Visual Embedding for Image-Text Matching | Tianlang Chen, Jiebo Luo | To address this problem, we propose a Dual Path Recurrent Neural Network (DP-RNN) which processes images and sentences symmetrically by recurrent neural networks (RNN). |
| 1297 | Frame-Guided Region-Aligned Representation for Video Person Re-Identification | Zengqun Chen, Zhiheng Zhou, Junchu Huang, Pengyu Zhang, Bo Li | To address the above issue, in this paper, we propose a Frame-Guided Region-Aligned model (FGRA) for discriminative representation learning in two steps in an end-to-end manner. |
| 1298 | Global Context-Aware Progressive Aggregation Network for Salient Object Detection | Zuyao Chen, Qianqian Xu, Runmin Cong, Qingming Huang | To remedy these issues, we propose a novel network named GCPANet to effectively integrate low-level appearance features, high-level semantic features, and global context features through some progressive context-aware Feature Interweaved Aggregation (FIA) modules and generate the saliency map in a supervised way. |
| 1299 | Video Frame Interpolation via Deformable Separable Convolution | Xianhang Cheng, Zhenzhong Chen | To solve this problem in this paper, we propose to use deformable separable convolution (DSepConv) to adaptively estimate kernels, offsets and masks to allow the network to obtain information with much fewer but more relevant pixels. |
| 1300 | CSPN++: Learning Context and Resource Aware Convolutional Spatial Propagation Networks for Depth Completion | Xinjing Cheng, Peng Wang, Chenye Guan, Ruigang Yang | In this paper, we propose CSPN++, which further improves its effectiveness and efficiency by learning adaptive convolutional kernel sizes and the number of iterations for the propagation, thus the context and computational resource needed at each pixel could be dynamically assigned upon requests. |
| 1301 | A Coarse-to-Fine Adaptive Network for Appearance-Based Gaze Estimation | Yihua Cheng, Shiyao Huang, Fei Wang, Chen Qian, Feng Lu | In this paper we make the following contributions: 1) We propose a coarse-to-fine strategy which estimates a basic gaze direction from face image and refines it with corresponding residual predicted from eye images. |
| 1302 | 3D Human Pose Estimation Using Spatio-Temporal Networks with Explicit Occlusion Training | Yu Cheng, Bo Yang, Bo Wang, Robby T. Tan | Addressing these problems, we introduce a spatio-temporal network for robust 3D human pose estimation. |
| 1303 | PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes | Cheng Chi, Shifeng Zhang, Junliang Xing, Zhen Lei, Stan Z. Li, Xudong Zou | In this paper, we propose an effective and efficient detection network to hunt pedestrians in crowd scenes. To facilitate further studies on the occluded pedestrian detection in surveillance scenes, we release a new pedestrian dataset, called SUR-PED, with a total of over 162k high-quality manually labeled instances in 10k images. |
| 1304 | Relational Learning for Joint Head and Human Detection | Cheng Chi, Shifeng Zhang, Junliang Xing, Zhen Lei, Stan Z. Li, Xudong Zou | To handle these two issues, we present a novel joint head and human detection network, namely JointDet, which effectively detects head and human body simultaneously. |
| 1305 | Visual Domain Adaptation by Consensus-Based Transfer to Intermediate Domain | Jongwon Choi, Youngjoon Choi, Jihoon Kim, Jinyeop Chang, Ilhwan Kwon, Youngjune Gwon, Seungjai Min | We describe an unsupervised domain adaptation framework for images by a transform to an abstract intermediate domain and ensemble classifiers seeking a consensus. |
| 1306 | Channel Attention Is All You Need for Video Frame Interpolation | Myungsub Choi, Heewon Kim, Bohyung Han, Ning Xu, Kyoung Mu Lee | To alleviate the limitation, we propose a simple but effective deep neural network for video frame interpolation, which is end-to-end trainable and is free from a motion estimation network component. We construct a comprehensive evaluation benchmark and demonstrate that the proposed approach achieves outstanding performance compared to the existing models with a component for optical flow computation. |
| 1307 | DASOT: A Unified Framework Integrating Data Association and Single Object Tracking for Online Multi-Object Tracking | Qi Chu, Wanli Ouyang, Bin Liu, Feng Zhu, Nenghai Yu | In this paper, we propose an online multi-object tracking (MOT) approach that integrates data association and single object tracking (SOT) with a unified convolutional network (ConvNet), named DASOTNet. |
| 1308 | Towards Ghost-Free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN | Xiaodong Cun, Chi-Man Pun, Cheng Shi | In this paper, we tackle these issues in two ways. |
| 1309 | The Missing Data Encoder: Cross-Channel Image Completion with Hide-and-Seek Adversarial Network | Arnaud Dapogny, Matthieu Cord, Patrick Perez | In this paper, we employ a deep network to perform image completion, with adversarial training as well as perceptual and completion losses, and call it the “missing data encoder” (MDE). |
| 1310 | Spatio-Temporal Deformable Convolution for Compressed Video Quality Enhancement | Jianing Deng, Li Wang, Shiliang Pu, Cheng Zhuo | In this paper, we propose a fast yet effective method for compressed video quality enhancement by incorporating a novel Spatio-Temporal Deformable Fusion (STDF) scheme to aggregate temporal information. |
| 1311 | Zero Shot Learning with the Isoperimetric Loss | Shay Deutsch, Andrea Bertozzi, Stefano Soatto | We introduce the isoperimetric loss as a regularization criterion for learning the map from a visual representation to a semantic embedding, to be used to transfer knowledge to unknown classes in a zero-shot learning setting. |
| 1312 | Every Frame Counts: Joint Learning of Video Segmentation and Optical Flow | Mingyu Ding, Zhe Wang, Bolei Zhou, Jianping Shi, Zhiwu Lu, Ping Luo | In this paper, we propose a novel framework for joint video semantic segmentation and optical flow estimation. |
| 1313 | Cycle-CNN for Colorization towards Real Monochrome-Color Camera Systems | Xuan Dong, Weixin Li, Xiaojie Wang, Yunhong Wang | We present a new CNN model, named cycle CNN, which can directly use the real data from monochrome-color camera systems for training. |
| 1314 | FD-GAN: Generative Adversarial Networks with Fusion-Discriminator for Single Image Dehazing | Yu Dong, Yihao Liu, He Zhang, Shifeng Chen, Yu Qiao | To address this, we propose a fully end-to-end Generative Adversarial Networks with Fusion-discriminator (FD-GAN) for image dehazing. |
| 1315 | Visual Relationship Detection with Low Rank Non-Negative Tensor Decomposition | Mohammed Haroon Dupty, Zhen Zhang, Wee Sun Lee | To make learning the triplet joint distribution feasible, we introduce a novel technique of learning conditional triplet distributions in the form of their normalized low rank non-negative tensor decompositions. |
| 1316 | SubSpace Capsule Network | Marzieh Edraki, Nazanin Rahnavard, Mubarak Shah | In this paper, we propose the SubSpace Capsule Network (SCN) that exploits the idea of capsule networks to model possible variations in the appearance or implicitly-defined properties of an entity through a group of capsule subspaces instead of simply grouping neurons to create capsules. |
| 1317 | Person Tube Retrieval via Language Description | Hehe Fan, Yi Yang | To transform tubes and descriptions into a shared latent space where data from the two different modalities can be compared directly, we propose a Multi-Scale Structure Preservation (MSSP) approach. |
| 1318 | CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation | Junsong Fan, Zhaoxiang Zhang, Tieniu Tan, Chunfeng Song, Jun Xiao | To leverage this information, we propose an end-to-end cross-image affinity module, which exploits pixel-level cross-image relationships with only image-level labels. |
| 1319 | Scale-Wise Convolution for Image Restoration | Yuchen Fan, Jiahui Yu, Ding Liu, Thomas S. Huang | In this paper, we show that properly modeling scale-invariance into neural networks can bring significant benefits to image restoration performance. |
| 1320 | EHSOD: CAM-Guided End-to-End Hybrid-Supervised Object Detection with Cascade Refinement | Linpu Fang, Hang Xu, Zhili Liu, Sarah Parisot, Zhenguo Li | In this paper, we study the hybrid-supervised object detection problem, aiming to train a high quality detector with only a limited amount of fully-annotated data and fully exploiting cheap data with image-level labels. |
| 1321 | Adversarial Attack on Deep Product Quantization Network for Image Retrieval | Yan Feng, Bin Chen, Tao Dai, Shu-Tao Xia | To this end, we propose product quantization adversarial generation (PQ-AG), a simple yet effective method to generate adversarial examples for product quantization based retrieval systems. |
| 1322 | Dynamic Sampling Network for Semantic Segmentation | Bin Fu, Junjun He, Zhengfu Zhang, Yu Qiao | To address this problem, this paper proposes a Context Guided Dynamic Sampling (CGDS) module to obtain an effective representation with rich shape and scale information by adaptively sampling useful segmentation information in spatial space. |
| 1323 | Ultrafast Video Attention Prediction with Coupled Knowledge Distillation | Kui Fu, Peipei Shi, Yafei Song, Shiming Ge, Xiangju Lu, Jia Li | To this end, we propose a coupled knowledge distillation strategy to augment and train the network effectively. |
| 1324 | Accurate Temporal Action Proposal Generation with Relation-Aware Pyramid Network | Jialin Gao, Zhixiang Shi, Guanshuo Wang, Jiani Li, Yufeng Yuan, Shiming Ge, Xi Zhou | To this end, we propose a Relation-aware pyramid Network (RapNet) to generate highly accurate temporal action proposals. |
| 1325 | Channel Interaction Networks for Fine-Grained Image Categorization | Yu Gao, Xintong Han, Xun Wang, Weilin Huang, Matthew Scott | In this paper, we propose a channel interaction network (CIN), which models the channel-wise interplay both within an image and across images. |
| 1326 | KnowIT VQA: Answering Knowledge-Based Questions about Videos | Noa Garcia, Mayu Otani, Chenhui Chu, Yuta Nakashima | We propose a novel video understanding task by fusing knowledge-based and video question answering. |
| 1327 | Deep Reinforcement Learning for Active Human Pose Estimation | Erik Gärtner, Aleksis Pirinen, Cristian Sminchisescu | In this paper we address the problem of an active observer with freedom to move and explore the scene spatially – in ‘time-freeze’ mode – and/or temporally, by selecting informative viewpoints that improve its estimation accuracy. |
| 1328 | Look One and More: Distilling Hybrid Order Relational Knowledge for Cross-Resolution Image Recognition | Shiming Ge, Kangkai Zhang, Haolin Liu, Yingying Hua, Shengwei Zhao, Xin Jin, Hao Wen | Inspired by that, we propose a teacher-student learning approach to facilitate low-resolution image recognition via hybrid order relational knowledge distillation. |
| 1329 | Symmetrical Synthesis for Deep Metric Learning | Geonmo Gu, Byungsoo Ko | In this paper, we address these problems by proposing a novel method of synthetic hard sample generation called symmetrical synthesis. |
| 1330 | FLNet: Landmark Driven Fetching and Learning Network for Faithful Talking Facial Animation Synthesis | Kuangxiao Gu, Yuqian Zhou, Thomas Huang | In this paper, We present a landmark driven two-stream network to generate faithful talking facial animation, in which more facial details are created, preserved and transferred from multiple source images instead of a single one. |
| 1331 | Pyramid Constrained Self-Attention Network for Fast Video Salient Object Detection | Yuchao Gu, Lijuan Wang, Ziqin Wang, Yun Liu, Ming-Ming Cheng, Shao-Ping Lu | Previous VSOD methods usually use Long Short-Term Memory (LSTM) or 3D ConvNet (C3D), which can only encode motion information through step-by-step propagation in the temporal domain. |
| 1332 | Constructing Multiple Tasks for Augmentation: Improving Neural Image Classification with K-Means Features | Tao Gui, Lizhi Qing, Qi Zhang, Jiacheng Ye, Hang Yan, Zichu Fei, Xuanjing Huang | To tackle this problem, we explored the idea of using unsupervised clustering to construct a variety of auxiliary tasks from unlabeled data or existing labeled data. |
| 1333 | Channel Pruning Guided by Classification Loss and Feature Importance | Jinyang Guo, Wanli Ouyang, Dong Xu | In this work, we propose a new layer-by-layer channel pruning method called Channel Pruning guided by classification Loss and feature Importance (CPLI). |
| 1334 | MarioNETte: Few-Shot Face Reenactment Preserving Identity of Unseen Targets | Sungjoo Ha, Martin Kersner, Beomsu Kim, Seokjun Seo, Dongyoung Kim | To overcome such problems, we introduce components that address the mentioned problem: image attention block, target feature alignment, and landmark transformer. |
| 1335 | SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications | Abdullah Hamdi, Matthias Mueller, Bernard Ghanem | In contrast, we present a general framework for adversarial attacks on trained agents, which covers semantic perturbations to the environment of the agent performing the task as well as pixel-level attacks. |
| 1336 | Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons | Ligong Han, Ruijiang Gao, Mun Kim, Xin Tao, Bo Liu, Dimitris Metaxas | To address this problem, we propose a novel generative adversarial network utilizing weak supervision in the form of pairwise comparisons (PC-GAN) for image attribute editing. |
| 1337 | Complementary-View Multiple Human Tracking | Ruize Han, Wei Feng, Jiewen Zhao, Zicheng Niu, Yujun Zhang, Liang Wan, Song Wang | In this paper, we model the data similarity in each view using appearance and motion reasoning and across views using appearance and spatial reasoning. We collect a new dataset consisting of top- and horizontal-view video pairs for performance evaluation and the experimental results show the effectiveness of the proposed method. |
| 1338 | Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling | Wenkai Han, Chenglu Wen, Cheng Wang, Xin Li, Qing Li | This paper presents a novel end-to-end graph model, named Point2Node, to represent a given point cloud. |
| 1339 | Tensor FISTA-Net for Real-Time Snapshot Compressive Imaging | Xiaochen Han, Bo Wu, Zheng Shou, Xiao-Yang Liu, Yimeng Zhang, Linghe Kong | In this paper, exploiting the powerful learning ability of deep neural networks (DNN), we propose a novel Tensor Fast Iterative Shrinkage-Thresholding Algorithm Net (Tensor FISTA-Net) as a decoder for SCI video cameras. |
| 1340 | Temporal Context Enhanced Feature Aggregation for Video Object Detection | Fei He, Naiyu Gao, Qiaozhe Li, Senyao Du, Xin Zhao, Kaiqi Huang | To handle the appearance deterioration problem, this paper proposes a temporal context enhanced network (TCENet) to exploit temporal context information by temporal aggregation for video object detection. |
| 1341 | Grapy-ML: Graph Pyramid Mutual Learning for Cross-Dataset Human Parsing | Haoyu He, Jing Zhang, Qiming Zhang, Dacheng Tao | In this paper, we propose a novel GRAph PYramid Mutual Learning (Grapy-ML) method to address the cross-dataset human parsing problem, where the annotations are at different granularities. |
| 1342 | Softmax Dissection: Towards Understanding Intra- and Inter-Class Objective for Embedding Learning | Lanqing He, Zhongdao Wang, Yali Li, Shengjin Wang | In this paper, we propose to dissect Softmax into independent intra- and inter-class objective (D-Softmax) with a clear understanding. |
| 1343 | RoadTagger: Robust Road Attribute Inference with Graph Neural Networks | Songtao He, Favyen Bastani, Satvat Jagwani, Edward Park, Sofiane Abbar, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Samuel Madden, Mohammad Amin Sadeghi | To overcome this limitation, we propose RoadTagger, an end-to-end architecture which combines both Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs) to infer road attributes. |
| 1344 | Joint Commonsense and Relation Reasoning for Image and Video Captioning | Jingyi Hou, Xinxiao Wu, Xiaoxun Zhang, Yayun Qi, Yunde Jia, Jiebo Luo | In this paper, we propose a joint commonsense and relation reasoning method that exploits prior knowledge for image and video captioning without relying on any detectors. |
| 1345 | Hierarchical Modes Exploring in Generative Adversarial Networks | Mengxiao Hu, Jinlong Li, Maolin Hu, Tao Hu | To prevent this happen, we proposed a hierarchical mode exploring method to alleviate mode collapse in cGANs by introducing a diversity measurement into the objective function as the regularization term. |
| 1346 | SPSTracker: Sub-Peak Suppression of Response Map for Robust Object Tracking | Qintao Hu, Lijun Zhou, Xiaoxiao Wang, Yao Mao, Jianlin Zhang, Qixiang Ye | In this paper, we propose a rectified online learning approach for sub-peak response suppression and peak response enforcement and target at handling progressive interference in a systematic way. |
| 1347 | 3D Shape Completion with Multi-View Consistent Inference | Tao Hu, Zhizhong Han, Matthias Zwicker | To resolve this issue, we propose a multi-view consistent inference technique for 3D shape completion, which we express as an energy minimization problem including a data term and a regularization term. |
| 1348 | GTC: Guided Training of CTC towards Efficient and Accurate Scene Text Recognition | Wenyang Hu, Xiaocong Cai, Jun Hou, Shuai Yi, Zhiping Lin | To design an efficient and effective model, we propose the guided training of CTC (GTC), where CTC model learns a better alignment and feature representations from a more powerful attentional guidance. |
| 1349 | Coarse-to-Fine Hyper-Prior Modeling for Learned Image Compression | Yueyu Hu, Wenhan Yang, Jiaying Liu | In this paper, we propose a coarse-to-fine framework with hierarchical layers of hyper-priors to conduct comprehensive analysis of the image and more effectively reduce spatial redundancy, which improves the rate-distortion performance of image compression significantly. |
| 1350 | Location-Aware Graph Convolutional Networks for Video Question Answering | Deng Huang, Peihao Chen, Runhao Zeng, Qing Du, Mingkui Tan, Chuang Gan | In this work, we propose to represent the contents in the video as a location-aware graph by incorporating the location information of an object into the graph construction. |
| 1351 | Unsupervised Deep Learning via Affinity Diffusion | Jiabo Huang, Qi Dong, Shaogang Gong, Xiatian Zhu | In this work, we introduce a general-purpose unsupervised deep learning approach to deriving discriminative feature representations. |
| 1352 | GlobalTrack: A Simple and Strong Baseline for Long-Term Tracking | Lianghua Huang, Xin Zhao, Kaiqi Huang | In this work, we aim to bridge this gap. |
| 1353 | Part-Level Graph Convolutional Network for Skeleton-Based Action Recognition | Linjiang Huang, Yan Huang, Wanli Ouyang, Liang Wang | In this work, we identify a problem posed by the GCNs for skeleton-based action recognition, namely part-level action modeling. |
| 1354 | Relational Prototypical Network for Weakly Supervised Temporal Action Localization | Linjiang Huang, Yan Huang, Wanli Ouyang, Liang Wang | In this paper, we propose a weakly supervised temporal action localization method on untrimmed videos based on prototypical networks. |
| 1355 | AWR: Adaptive Weighting Regression for 3D Hand Pose Estimation | Weiting Huang, Pengfei Ren, Jingyu Wang, Qi Qi, Haifeng Sun | In this paper, we propose an adaptive weighting regression (AWR) method to leverage the advantages of both detection-based and regression-based method. |
| 1356 | Domain Adaptive Attention Learning for Unsupervised Person Re-Identification | Yangru Huang, Peixi Peng, Yi Jin, Yidong Li, Junliang Xing | To address these two issues, this paper proposes a domain adaptive attention learning approach to reliably transfer discriminative representation from the labeled source domain to the unlabeled target domain. |
| 1357 | Weakly-Supervised Video Re-Localization with Multiscale Attention Model | Yung-Han Huang, Kuang-Jui Hsu, Shyh-Kang Jeng, Yen-Yu Lin | In this work, we propose an attention-based model to accomplish this task in a weakly supervised setting. |
| 1358 | SGAP-Net: Semantic-Guided Attentive Prototypes Network for Few-Shot Human-Object Interaction Recognition | Zhong Ji, Xiyao Liu, Yanwei Pang, Xuelong Li | Due to the fact that the intrinsic characteristic of HOI is diverse and interactive, we propose a Semantic-Guided Attentive Prototypes Network (SGAP-Net) to learn a semantic-guided metric space where HOI recognition can be performed by computing distances to attentive prototypes of each class. |
| 1359 | ElixirNet: Relation-Aware Network Architecture Adaptation for Medical Lesion Detection | Chenhan Jiang, Shaoju Wang, Xiaodan Liang, Hang Xu, Nong Xiao | In this paper, we introduce a novel ElixirNet that includes three components: 1) TruncatedRPN balances positive and negative data for false positive reduction; 2) Auto-lesion Block is automatically customized for medical images to incorporates relation-aware operations among region proposals, and leads to more suitable and efficient classification and localization. |
| 1360 | Divide and Conquer: Question-Guided Spatio-Temporal Contextual Attention for Video Question Answering | Jianwen Jiang, Ziqiang Chen, Haojie Lin, Xibin Zhao, Yue Gao | To tackle this problem, we propose a Question-Guided Spatio-Temporal Contextual Attention Network (QueST) method. |
| 1361 | Reasoning with Heterogeneous Graph Alignment for Video Question Answering | Pin Jiang, Yahong Han | We propose a deep heterogeneous graph alignment network over the video shots and question words. |
| 1362 | Recurrent Nested Model for Sequence Generation | Wenhao Jiang, Lin Ma, Wei Lu | In this paper, we make an attempt to make the encoder-decoder model deeper for sequence generation. |
| 1363 | DualVD: An Adaptive Dual Encoding Model for Deep Visual Understanding in Visual Dialogue | Xiaoze Jiang, Jing Yu, Zengchang Qin, Yingying Zhuang, Xingxing Zhang, Yue Hu, Qi Wu | In this research, we propose a novel model to depict an image from both visual and semantic perspectives. |
| 1364 | Rethinking Temporal Fusion for Video-Based Person Re-Identification on Semantic and Time Aspect | Xinyang Jiang, Yifei Gong, Xiaowei Guo, Qize Yang, Feiyue Huang, WEI-SHI ZHENG, Feng Zheng, Xing Sun | To address these issues, we propose a novel general temporal fusion framework to aggregate frame features on both semantic aspect and time aspect. |
| 1365 | Learning Light Field Angular Super-Resolution via a Geometry-Aware Network | Jing Jin, Junhui Hou, Hui Yuan, Sam Kwong | By making full use of the intrinsic geometry information of light fields, in this paper we propose an end-to-end learning-based approach aiming at angularly super-resolving a sparsely-sampled light field with a large baseline. |
| 1366 | EAC-Net: Efficient and Accurate Convolutional Network for Video Recognition | Bowei Jin, Zhuo Xu | In this paper, we explore a new architecture EAC-Net, enjoying both high efficiency and high performance. |
| 1367 | SSAH: Semi-Supervised Adversarial Deep Hashing with Self-Paced Hard Sample Generation | Sheng Jin, Shangchen Zhou, Yao Liu, Chao Chen, Xiaoshuai Sun, Hongxun Yao, Xian-Sheng Hua | In this paper, we propose a novel Semi-supervised Self-pace Adversarial Hashing method, named SSAH to solve the above problems in a unified framework. |
| 1368 | Uncertainty-Aware Multi-Shot Knowledge Distillation for Image-Based Object Re-Identification | Xin Jin, Cuiling Lan, Wenjun Zeng, Zhibo Chen | In this paper, we propose exploiting the multi-shots of the same identity to guide the feature learning of each individual image. |
| 1369 | Semantics-Aligned Representation Learning for Person Re-Identification | Xin Jin, Cuiling Lan, Wenjun Zeng, Guoqiang Wei, Zhibo Chen | In this paper, we propose a framework that drives the reID network to learn semantics-aligned feature representation through delicate supervision designs. |
| 1370 | Overcoming Language Priors in VQA via Decomposed Linguistic Representations | Chenchen Jing, Yuwei Wu, Xiaoxun Zhang, Yunde Jia, Qi Wu | In this paper, we present a novel method of language attention-based VQA that learns decomposed linguistic representations of questions and utilizes the representations to infer answers for overcoming language priors. |
| 1371 | Pose-Guided Multi-Granularity Attention Network for Text-Based Person Search | Ya Jing, Chenyang Si, Junbo Wang, Wei Wang, Liang Wang, Tieniu Tan | To exploit the multilevel corresponding visual contents, we propose a pose-guided multi-granularity attention network (PMA). |
| 1372 | Associative Variational Auto-Encoder with Distributed Latent Spaces and Associators | Dae Ung Jo, ByeongJu Lee, Jongwon Choi, Haanju Yoo, Jin Young Choi | In this paper, we propose a novel structure for a multi-modal data association referred to as Associative Variational Auto-Encoder (AVAE). |
| 1373 | Real-Time Object Tracking via Meta-Learning: Efficient Model Adaptation and One-Shot Channel Pruning | Ilchae Jung, Kihyun You, Hyeonwoo Noh, Minsu Cho, Bohyung Han | We propose a novel meta-learning framework for real-time object tracking with efficient model adaptation and channel pruning. |
| 1374 | Hide-and-Tell: Learning to Bridge Photo Streams for Visual Storytelling | Yunjae Jung, Dahun Kim, Sanghyun Woo, Kyungsu Kim, Sungjin Kim, In So Kweon | In this paper, we propose to explicitly learn to imagine a storyline that bridges the visual gap. |
| 1375 | Synthetic Depth Transfer for Monocular 3D Object Pose Estimation in the Wild | Yueying Kao, Weiming Li, Qiang Wang, Zhouchen Lin, Wooshik Kim, Sunghoon Hong | In this paper, we aim at extracting RGB and depth features from a single RGB image with the help of synthetic RGB-depth image pairs for object pose estimation. |
| 1376 | Group-Wise Dynamic Dropout Based on Latent Semantic Variations | Zhiwei Ke, Zhiwei Wen, Weicheng Xie, Yi Wang, Linlin Shen | In this paper, we propose an adaptive dropout to reduce the co-adaptations in a group-wise manner by coarse semantic information to improve feature discriminability. |
| 1377 | Deep Generative Probabilistic Graph Neural Networks for Scene Graph Generation | Mahmoud Khademi, Oliver Schulte | We propose a new algorithm, called Deep Generative Probabilistic Graph Neural Networks (DG-PGNN), to generate a scene graph for an image. |
| 1378 | Tell Me What They’re Holding: Weakly-Supervised Object Detection with Transferable Knowledge from Human-Object Interaction | Daesik Kim, Gyujeong Lee, Jisoo Jeong, Nojun Kwak | In this work, we introduce a novel weakly supervised object detection (WSOD) paradigm to detect objects belonging to rare classes that have not many examples using transferable knowledge from human-object interactions (HOI). |
| 1379 | MULE: Multimodal Universal Language Embedding | Donghyun Kim, Kuniaki Saito, Kate Saenko, Stan Sclaroff, Bryan Plummer | In this paper, we present a modular approach which can easily be incorporated into existing vision-language methods in order to support many languages. |
| 1380 | REST: Performance Improvement of a Black Box Model via RL-Based Spatial Transformation | Jae Myung Kim, Hyungjin Kim, Chanwoo Park, Jungwoo Lee | We propose an additional learner, REinforcement Spatial Transform learner (REST), that transforms the warped input data into samples regarded as in-distribution by the black-box models. |
| 1381 | Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection | Seijoon Kim, Seongsik Park, Byunggook Na, Sungroh Yoon | In this study, we investigate the performance degradation of SNNs in a more challenging regression problem (i.e., object detection). |
| 1382 | FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-Scale Temporal Loss | Soo Ye Kim, Jihyong Oh, Munchurl Kim | For this, we propose a novel training scheme with a multi-scale temporal loss that imposes temporal regularization on the input video sequence, which can be applied to any general video-related task. |
| 1383 | JSI-GAN: GAN-Based Joint Super-Resolution and Inverse Tone-Mapping with Pixel-Wise Task-Specific Filters for UHD HDR Video | Soo Ye Kim, Jihyong Oh, Munchurl Kim | In this paper, we take a divide-and-conquer approach in designing a novel GAN-based joint SR-ITM network, called JSI-GAN, which is composed of three task-specific subnets: an image reconstruction subnet, a detail restoration (DR) subnet and a local contrast enhancement (LCE) subnet. |
| 1384 | Unpaired Image Enhancement Featuring Reinforcement-Learning-Controlled Image Editing Software | Satoshi Kosugi, Toshihiko Yamasaki | To incorporate image editing software into a GAN, we propose a reinforcement learning framework where the generator works as the agent that selects the software’s parameters and is rewarded when it fools the discriminator. |
| 1385 | Adversary for Social Good: Protecting Familial Privacy through Joint Adversarial Attacks | Chetan Kumar, Riazat Ryan, Ming Shao | To address this issue, in this paper, we propose a novel adversarial attack algorithm for social good. |
| 1386 | Kinematic-Structure-Preserved Representation for Unsupervised 3D Human Pose Estimation | Jogendra Nath Kundu, Siddharth Seth, Rahul M V, Mugalodi Rakesh, Venkatesh Babu Radhakrishnan, Anirban Chakraborty | Though weakly-supervised models have been proposed to address this shortcoming, performance of such models relies on availability of paired supervision on some related task, such as 2D pose or multi-view image pairs. |
| 1387 | Background Suppression Network for Weakly-Supervised Temporal Action Localization | Pilhyeon Lee, Youngjung Uh, Hyeran Byun | In this paper, we design Background Suppression Network (BaS-Net) which introduces an auxiliary class for background and has a two-branch weight-sharing architecture with an asymmetrical training strategy. |
| 1388 | Multi-Question Learning for Visual Question Answering | Chenyi Lei, Lei Wu, Dong Liu, Zhao Li, Guoxin Wang, Haihong Tang, Houqiang Li | To explore these relations, we propose a new paradigm for VQA termed Multi-Question Learning (MQL). |
| 1389 | Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training | Gen Li, Nan Duan, Yuejian Fang, Ming Gong, Daxin Jiang | We propose Unicoder-VL, a universal encoder that aims to learn joint representations of vision and language in a pre-training manner. |
| 1390 | Multi-Spectral Vehicle Re-Identification: A Challenge | Hongchao Li, Chenglong Li, Xianpeng Zhu, Aihua Zheng, Bin Luo | In this work, we address the RGB and IR vehicle Re-ID problem and contribute a multi-spectral vehicle Re-ID benchmark named RGBN300, including RGB and NIR (Near Infrared) vehicle images of 300 identities from 8 camera views, giving in total 50125 RGB images and 50125 NIR images respectively. Our work provides a benchmark dataset for RGB-NIR and RGB-NIR-TIR multi-spectral vehicle Re-ID and a baseline network for both research and industrial communities. |
| 1391 | Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation | Jia Li, Wen Su, Zengfu Wang | Our approach not only works straightforwardly but also outperforms the baseline by about 15% in average precision and is comparable to the state of the art on the MS-COCO test-dev dataset. |
| 1392 | Learning Part Generation and Assembly for Structure-Aware Shape Synthesis | Jun Li, Chengjie Niu, Kai Xu | Enlightened by the fact that 3D shape structure is characterized as part composition and placement, we propose to model 3D shape variations with a part-aware deep generative network, coined as PAGENet. |
| 1393 | Hierarchical Knowledge Squeezed Adversarial Network Compression | Peng Li, Chang Shu, Yuan Xie, Yan Qu, Hui Kong | Observing that, the small network can not perfectly mimic a large one due to the huge gap of network scale, we propose a knowledge transfer method, involving effective intermediate supervision, under the adversarial training framework to learn the student network. |
| 1394 | Age Progression and Regression with Spatial Attention Modules | Qi Li, Yunfan Liu, Zhenan Sun | To address these issues, in this paper, we propose a framework based on conditional Generative Adversarial Networks (cGANs) to achieve age progression and regression simultaneously. |
| 1395 | Domain Conditioned Adaptation Network | Shuang Li, Chi Liu, Qiuxia Lin, Binhui Xie, Zhengming Ding, Gao Huang, Jian Tang | In this paper, we relax a shared-convnets assumption made by previous DA methods and propose a Domain Conditioned Adaptation Network (DCAN), which aims to excite distinct convolutional channels with a domain conditioned channel attention mechanism. |
| 1396 | Appearance and Motion Enhancement for Video-Based Person Re-Identification | Shuzhao Li, Huimin Yu, Haoji Hu | In this paper, we propose an Appearance and Motion Enhancement Model (AMEM) for video-based person re-identification to enrich the two kinds of information contained in the backbone network in a more interpretable way. |
| 1397 | Attention-Based Multi-Modal Fusion Network for Semantic Scene Completion | Siqi Li, Changqing Zou, Yipeng Li, Xibin Zhao, Yue Gao | This paper presents an end-to-end 3D convolutional network named attention-based multi-modal fusion network (AMFNet) for the semantic scene completion (SSC) task of inferring the occupancy and semantic labels of a volumetric 3D scene from single-view RGB-D images. |
| 1398 | OVL: One-View Learning for Human Retrieval | Wenjing Li, Zhongcheng Wu | To address this problem, this work introduces a novel one-view learning framework for person re-ID. |
| 1399 | Gated Fully Fusion for Semantic Segmentation | Xiangtai Li, Houlong Zhao, Lei Han, Yunhai Tong, Shaohua Tan, Kuiyuan Yang | In this paper, we propose a new architecture, named Gated Fully Fusion(GFF), to selectively fuse features from multiple levels using gates in a fully connected way. |
| 1400 | ScaleNet – Improve CNNs through Recursively Rescaling Objects | Xingyi Li, Zhongang Qi, Xiaoli Fern, Fuxin Li | In this paper, we propose ScaleNet, which recursively predicts object scale in a deep learning framework. |
| 1401 | Relation-Guided Spatial Attention and Temporal Refinement for Video-Based Person Re-Identification | Xingze Li, Wengang Zhou, Yun Zhou, Houqiang Li | In this paper, we propose two relation-guided modules to learn reinforced feature representations for effective re-identification. |
| 1402 | Geometry-Driven Self-Supervised Method for 3D Human Pose Estimation | Yang Li, Kan Li, Shuai Jiang, Ziyue Zhang, Congzhentao Huang, Richard Yi Da Xu | In this paper, we propose a novel self-supervised approach to avoid the need of manual annotations. |
| 1403 | Natural Image Matting via Guided Contextual Attention | Yaoyi Li, Hongtao Lu | Inspired by affinity-based method and the successes of contextual attention in inpainting, we develop a novel end-to-end approach for natural image matting with a guided contextual attention module, which is specifically designed for image matting. |
| 1404 | Learning Transferable Adversarial Examples via Ghost Networks | Yingwei Li, Song Bai, Yuyin Zhou, Cihang Xie, Zhishuai Zhang, Alan Yuille | In this paper, we propose Ghost Networks to improve the transferability of adversarial examples. |
| 1405 | Finding Action Tubes with a Sparse-to-Dense Framework | Yuxi Li, Weiyao Lin, Tao Wang, John See, Rui Qian, Ning Xu, Limin Wang, Shugong Xu | In this paper, we propose for the first time, an efficient framework that generates action tube proposals from video streams with a single forward pass in a sparse-to-dense manner. |
| 1406 | Real-Time Scene Text Detection with Differentiable Binarization | Minghui Liao, Zhaoyi Wan, Cong Yao, Kai Chen, Xiang Bai | In this paper, we propose a module named Differentiable Binarization (DB), which can perform the binarization process in a segmentation network. |
| 1407 | Object Instance Mining for Weakly Supervised Object Detection | Chenhao Lin, Siwen Wang, Dongqi Xu, Yu Lu, Wayne Zhang | To address this problem, this paper introduces an end-to-end object instance mining (OIM) framework for weakly supervised object detection. |
| 1408 | Multimodal Structure-Consistent Image-to-Image Translation | Che-Tsung Lin, Yen-Yi Wu, Po-Hao Hsu, Shang-Hong Lai | In this work, we introduce cycle-structure consistency for generating diverse and structure-preserved translated images across complex domains, such as between day and night, for object detector training. |
| 1409 | Fast Learning of Temporal Action Proposal via Dense Boundary Generator | Chuming Lin, Jian Li, Yabiao Wang, Ying Tai, Donghao Luo, Zhipeng Cui, Chengjie Wang, Jilin Li, Feiyue Huang, Rongrong Ji | In this paper, we propose an efficient and unified framework to generate temporal action proposals named Dense Boundary Generator (DBG), which draws inspiration from boundary-sensitive methods and implements boundary classification and action completeness regression for densely distributed proposals. |
| 1410 | Learning to Transfer: Unsupervised Domain Translation via Meta-Learning | Jianxin Lin, Yijun Wang, Zhibo Chen, Tianyu He | In this work, we take on unsupervised domain translation problems from a meta-learning perspective. |
| 1411 | Learning Cross-Aligned Latent Embeddings for Zero-Shot Cross-Modal Retrieval | Kaiyi Lin, Xing Xu, Lianli Gao, Zheng Wang, Heng Tao Shen | In this paper, we propose a novel method termed Learning Cross-Aligned Latent Embeddings (LCALE) as an alternative to these GAN based methods for ZS-CMR. |
| 1412 | Learning to Deblur Face Images via Sketch Synthesis | Songnan Lin, Jiawei Zhang, Jinshan Pan, Yicun Liu, Yongtian Wang, Jing Chen, Jimmy Ren | In this paper, we propose an effective face deblurring algorithm based on deep convolutional neural networks (CNNs). |
| 1413 | Self-Attention ConvLSTM for Spatiotemporal Prediction | Zhihui Lin, Maomao Li, Zhuobin Zheng, Yangyang Cheng, Chun Yuan | To extract spatial features with both global and local dependencies, we introduce the self-attention mechanism into ConvLSTM. |
| 1414 | Weakly-Supervised Video Moment Retrieval via Semantic Completion Network | Zhijie Lin, Zhou Zhao, Zhu Zhang, Qi Wang, Huasheng Liu | In this paper, we propose a novel weakly-supervised moment retrieval framework requiring only coarse video-level annotations for training. |
| 1415 | Zero-Shot Learning from Adversarial Feature Residual to Compact Visual Feature | Bo Liu, Qiulei Dong, Zhanyi Hu | Addressing this problem, we propose a novel adversarial network to synthesize compact semantic visual features for ZSL, consisting of a residual generator, a prototype predictor, and a discriminator. |
| 1416 | Filtration and Distillation: Enhancing Region Attention for Fine-Grained Visual Categorization | Chuanbin Liu, Hongtao Xie, Zheng-Jun Zha, Lingfeng Ma, Lingyun Yu, Yongdong Zhang | To address the above issues, in this paper, we propose a novel “Filtration and Distillation Learning” (FDL) model to enhance the region attention of discriminate parts for FGVC. |
| 1417 | HAL: Improved Text-Image Matching by Mitigating Visual Semantic Hubs | Fangyu Liu, Rongtian Ye, Xun Wang, Shuaipeng Li | In this work, we study the emergence of hubs in Visual Semantic Embeddings (VSE) with application to text-image matching. |
| 1418 | Federated Learning for Vision-and-Language Grounding Problems | Fenglin Liu, Xian Wu, Shen Ge, Wei Fan, Yuexian Zou | Inspired by the recent success of federated learning, we propose a federated learning framework to obtain various types of image representations from different tasks, which are then fused together to form fine-grained image representations. |
| 1419 | Learned Video Compression via Joint Spatial-Temporal Correlation Exploration | Haojie Liu, Han Shen, Lichao Huang, Ming Lu, Tong Chen, Zhan Ma | Thus, in this paper, we propose to exploit the temporal correlation using both first-order optical flow and second-order flow prediction. |
| 1420 | Interactive Dual Generative Adversarial Networks for Image Captioning | Junhao Liu, Kai Wang, Chunpu Xu, Zhou Zhao, Ruifeng Xu, Ying Shen, Min Yang | In this paper, we propose an Interactive Dual Generative Adversarial Network (IDGAN) for image captioning, which mutually combines the retrieval-based and generation-based methods to learn a better image captioning ensemble. |
| 1421 | Morphing and Sampling Network for Dense Point Cloud Completion | Minghua Liu, Lu Sheng, Sheng Yang, Jing Shao, Shi-Min Hu | For acquiring high-fidelity dense point clouds and avoiding uneven distribution, blurred details, or structural loss of existing methods’ results, we propose a novel approach to complete the partial point cloud in two stages. |
| 1422 | Multi-Task Driven Feature Models for Thermal Infrared Tracking | Qiao Liu, Xin Li, Zhenyu He, Nana Fan, Di Yuan, Wei Liu, Yongsheng Liang | To this end, we develop a multi-task framework to learn the TIR-specific discriminative features and fine-grained correlation features for TIR tracking. In addition, we develop a large-scale TIR training dataset to train the network for adapting the model to the TIR domain. |
| 1423 | Progressive Boundary Refinement Network for Temporal Action Detection | Qinying Liu, Zilei Wang | To tackle this issue, we propose an end-to-end progressive boundary refinement network (PBRNet) in this paper. |
| 1424 | A Generalized Framework for Edge-Preserving and Structure-Preserving Image Smoothing | Wei Liu, Pingping Zhang, Yinjie Lei, Xiaolin Huang, Jie Yang, Ian Reid | In this paper, a non-convex non-smooth optimization framework is proposed to achieve diverse smoothing natures where even contradictive smoothing behaviors can be achieved. |
| 1425 | Importance-Aware Semantic Segmentation in Self-Driving with Discrete Wasserstein Training | Xiaofeng Liu, Yuzhuo Han, Song Bai, Yi Ge, Tianxing Wang, Xu Han, Site Li, Jane You, Jun Lu | In this paper, we propose to incorporate the importance-aware inter-class correlation in a Wasserstein training framework by configuring its ground distance matrix. |
| 1426 | A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing | Yinglu Liu, Hailin Shi, Hao Shen, Yue Si, Xiaobo Wang, Tao Mei | In this paper, we make contributions on face parsing task from two aspects. |
| 1427 | Learning Cross-Modal Context Graph for Visual Grounding | Yongfei Liu, Bo Wan, Xiaodan Zhu, Xuming He | To address their limitations, this paper proposes a language-guided graph representation to capture the global context of grounding entities and their relations, and develop a cross-modal graph matching strategy for the multiple-phrase visual grounding task. |
| 1428 | CBNet: A Novel Composite Backbone Network Architecture for Object Detection | Yudong Liu, Yongtao Wang, Siwei Wang, Tingting Liang, Qijie Zhao, Zhi Tang, Haibin Ling | In this paper, we aim to achieve better detection performance by building a more powerful backbone from existing ones like ResNet and ResNeXt. |
| 1429 | Separate in Latent Space: Unsupervised Single Image Layer Separation | Yunfei Liu, Feng Lu | To address this problem, this paper proposes an unsupervised method that requires no ground truth data triplet in training. |
| 1430 | TEINet: Towards an Efficient Architecture for Video Recognition | Zhaoyang Liu, Donghao Luo, Yabiao Wang, Limin Wang, Ying Tai, Chengjie Wang, Jilin Li, Feiyue Huang, Tong Lu | To relieve this problem, we propose an efficient temporal module, termed as Temporal Enhancement-and-Interaction (TEI Module), which could be plugged into the existing 2D CNNs (denoted by TEINet). |
| 1431 | TANet: Robust 3D Object Detection from Point Clouds with Triple Attention | Zhe Liu, Xin Zhao, Tengteng Huang, Ruolan Hu, Yu Zhou, Xiang Bai | In this paper, we focus on exploring the robustness of the 3D object detection in point clouds, which has been rarely discussed in existing approaches. |
| 1432 | Training-Time-Friendly Network for Real-Time Object Detection | Zili Liu, Tu Zheng, Guodong Xu, Zheng Yang, Haifeng Liu, Deng Cai | In this work, we start with light-head, single-stage, and anchor-free designs, which enable fast inference speed. |
| 1433 | Hybrid Graph Neural Networks for Crowd Counting | Ao Luo, Fan Yang, Xin Li, Dong Nie, Zhicheng Jiao, Shangchen Zhou, Hong Cheng | In this paper, we present a novel network structure called Hybrid Graph Neural Network (HyGnn) which targets to relieve the problem by interweaving the multi-scale features for crowd density as well as its auxiliary task (localization) together and performing joint reasoning over a graph. |
| 1434 | Video Cloze Procedure for Self-Supervised Spatio-Temporal Learning | Dezhao Luo, Chang Liu, Yu Zhou, Dongbao Yang, Can Ma, Qixiang Ye, Weiping Wang | We propose a novel self-supervised method, referred to as Video Cloze Procedure (VCP), to learn rich spatial-temporal representations. |
| 1435 | Context-Aware Zero-Shot Recognition | Ruotian Luo, Ning Zhang, Bohyung Han, Linjie Yang | We present a novel problem setting in zero-shot learning, zero-shot object recognition and detection in the context. |
| 1436 | Learning Saliency-Free Model with Generic Features for Weakly-Supervised Semantic Segmentation | Wenfeng Luo, Meng Yang | To eliminate the demand of extra data for training saliency detector, we propose to discover class pattern inherent in the lower layer convolution features, which are scarcely explored as in previous CAM methods. |
| 1437 | An Integrated Enhancement Solution for 24-Hour Colorful Imaging | Feifan Lv, Yinqiang Zheng, Yicheng Li, Feng Lu | In this paper, we propose a novel and integrated enhancement solution that produces clear color images, whether at abundant sunlight daytime or extremely low-light nighttime. |
| 1438 | Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network | Zhe Ma, Jianfeng Dong, Zhongzi Long, Yao Zhang, Yuan He, Hui Xue, Shouling Ji | To this end, we propose an Attribute-Specific Embedding Network (ASEN) to jointly learn multiple attribute-specific embeddings in an end-to-end manner, thus measure the fine-grained similarity in the corresponding space. |
| 1439 | Domain Generalization Using a Mixture of Multiple Latent Domains | Toshihiko Matsuura, Tatsuya Harada | To address this scenario, we propose a method that iteratively divides samples into latent domains via clustering, and which trains the domain-invariant feature extractor shared among the divided latent domains via adversarial learning. |
| 1440 | High-Order Residual Network for Light Field Super-Resolution | Nan Meng, Xiaofei Wu, Jianzhuang Liu, Edmund Lam | In this paper, we propose a novel high-order residual network to learn the geometric features hierarchically from the LF for reconstruction. |
| 1441 | Shallow Feature Based Dense Attention Network for Crowd Counting | Yunqi Miao, Zijia Lin, Guiguang Ding, Jungong Han | In this paper, we propose a Shallow feature based Dense Attention Network (SDANet) for crowd counting from still images, which diminishes the impact of backgrounds via involving a shallow feature based attention model, and meanwhile, captures multi-scale information via densely connecting hierarchical image features. |
| 1442 | Learning to Follow Directions in Street View | Karl Moritz Hermann, Mateusz Malinowski, Piotr Mirowski, Andras Banki-Horvath, Keith Anderson, Raia Hadsell | This paper presents the StreetNav environment and tasks, models that establish strong baselines, and extensive analysis of the task and the trained agents. |
| 1443 | Pyramid Attention Aggregation Network for Semantic Segmentation of Surgical Instruments | Zhen-Liang Ni, Gui-Bin Bian, Guan-An Wang, Xiao-Hu Zhou, Zeng-Guang Hou, Hua-Bin Chen, Xiao-Liang Xie | In this paper, a novel network, Pyramid Attention Aggregation Network, is proposed to aggregate multi-scale attentive features for surgical instruments. |
| 1444 | Spatial-Temporal Gaussian Scale Mixture Modeling for Foreground Estimation | Qian Ning, Weisheng Dong, Fangfang Wu, Jinjian Wu, Jie Lin, Guangming Shi | In this paper, we proposed a novel spatial-temporal Gaussian scale mixture (STGSM) model for foreground estimation. |
| 1445 | Crowd Counting with Decomposed Uncertainty | Min-hwan Oh, Peder Olsen, Karthikeyan Natesan Ramamurthy | In this work, we focus on uncertainty estimation in the domain of crowd counting. |
| 1446 | Image Formation Model Guided Deep Image Super-Resolution | Jinshan Pan, Yang Liu, Deqing Sun, Jimmy Ren, Ming-Ming Cheng, Jian Yang, Jinhui Tang | We present a simple and effective image super-resolution algorithm that imposes an image formation constraint on the deep neural networks via pixel substitution. |
| 1447 | Adversarial Cross-Domain Action Recognition with Co-Attention | Boxiao Pan, Zhangjie Cao, Ehsan Adeli, Juan Carlos Niebles | This paper proposes a Temporal Co-attention Network (TCoN), which matches the distributions of temporally aligned action features between source and target domains using a novel cross-domain co-attention mechanism. |
| 1448 | Further Understanding Videos through Adverbs: A New Video Task | Bo Pang, Kaiwen Zha, Yifan Zhang, Cewu Lu | Accordingly, we propose the BA Understanding Network (BAUN) to solve this problem and the experiments reveal that our BAUN is more suitable for BA recognition (11% better than I3D). |
| 1449 | Visual Dialogue State Tracking for Question Generation | Wei Pang, Xiaojie Wang | This paper proposes visual dialogue state tracking (VDST) based method for question generation. |
| 1450 | Relation Network for Person Re-Identification | Hyunjong Park, Bumsub Ham | To address this issue, we propose a new relation network for person reID that considers relations between individual body parts and the rest of them. |
| 1451 | Explanation vs Attention: A Two-Player Game to Obtain Attention for VQA | Badri Patro, Anupriy, Vinay Namboodiri | In this paper, we aim to obtain improved attention for a visual question answering (VQA) task. |
| 1452 | LCD: Learned Cross-Domain Descriptors for 2D-3D Matching | Quang-Hieu Pham, Mikaela Angelina Uy, Binh-Son Hua, Duc Thanh Nguyen, Gemma Roig, Sai-Kit Yeung | In this work, we present a novel method to learn a local cross-domain descriptor for 2D image and 3D point cloud matching. To facilitate the training process, we built a new dataset by collecting ≈ 1.4 millions of 2D-3D correspondences with various lighting conditions and settings from publicly available RGB-D scenes. |
| 1453 | Exploit and Replace: An Asymmetrical Two-Stream Architecture for Versatile Light Field Saliency Detection | Yongri Piao, Zhengkun Rong, Miao Zhang, Huchuan Lu | In this paper, we introduce an asymmetrical two-stream architecture inspired by knowledge distillation to confront these challenges. |
| 1454 | Differentiable Grammars for Videos | AJ Piergiovanni, Anelia Angelova, Michael S. Ryoo | This paper proposes a novel algorithm which learns a formal regular grammar from real-world continuous data, such as videos. |
| 1455 | Region-Adaptive Dense Network for Efficient Motion Deblurring | Kuldeep Purohit, A. N. Rajagopalan | In this paper, we address the problem of dynamic scene deblurring in the presence of motion blur. |
| 1456 | Visualizing Deep Networks by Optimizing with Integrated Gradients | Zhongang Qi, Saeed Khorram, Li Fuxin | In this paper, we propose I-GOS, which optimizes for a heatmap so that the classification scores on the masked image would maximally decrease. |
| 1457 | Text Perceptron: Towards End-to-End Arbitrary-Shaped Text Spotting | Liang Qiao, Sanli Tang, Zhanzhan Cheng, Yunlu Xu, Yi Niu, Shiliang Pu, Fei Wu | To handle this incompatibility problem, in this paper we propose an end-to-end trainable text spotting approach named Text Perceptron. |
| 1458 | FFA-Net: Feature Fusion Attention Network for Single Image Dehazing | Xu Qin, Zhilin Wang, Yuanchao Bai, Xiaodong Xie, Huizhu Jia | In this paper, we propose an end-to-end feature fusion at-tention network (FFA-Net) to directly restore the haze-free image. |
| 1459 | Learning Meta Model for Zero- and Few-Shot Face Anti-Spoofing | Yunxiao Qin, Chenxu Zhao, Xiangyu Zhu, Zezheng Wang, Zitong Yu, Tianyu Fu, Feng Zhou, Jingping Shi, Zhen Lei | In this paper, we propose a novel Adaptive Inner-update Meta Face Anti-Spoofing (AIM-FAS) method to tackle this problem through meta-learning. To assess the proposed approach, we propose several benchmarks for zero- and few-shot FAS. |
| 1460 | DGCN: Dynamic Graph Convolutional Network for Efficient Multi-Person Pose Estimation | Zhongwei Qiu, Kai Qiu, Jianlong Fu, Dongmei Fu | In this paper, we propose a novel Dynamic Graph Convolutional Module (DGCM) to model rich relations in the keypoints graph. |
| 1461 | Improved Visual-Semantic Alignment for Zero-Shot Object Detection | Shafin Rahman, Salman Khan, Nick Barnes | Here, we propose an end-to-end deep learning framework underpinned by a novel loss function that handles class-imbalance and seeks to properly align the visual and semantic cues for improved zero-shot learning. |
| 1462 | Dynamic Graph Representation for Occlusion Handling in Biometrics | Min Ren, Yunlong Wang, Zhenan Sun, Tieniu Tan | To this end, we propose a novel unified framework integrated the merits of both CNNs and graphical models to learn dynamic graph representations for occlusion problems in biometrics, called Dynamic Graph Representation (DGR). |
| 1463 | Conquering the CNN Over-Parameterization Dilemma: A Volterra Filtering Approach for Action Recognition | Siddharth Roheda, Hamid Krim | In an attempt to reduce the complexity of Convolutional Neural Networks, we propose a Volterra filter-inspired Network architecture. |
| 1464 | Hidden Trigger Backdoor Attacks | Aniruddha Saha, Akshayvarun Subramanya, Hamed Pirsiavash | We propose a novel form of backdoor attack where poisoned data look natural with correct labels and also more importantly, the attacker hides the trigger in the poisoned data and keeps the trigger secret until the test time. |
| 1465 | Temporal Interlacing Network | Hao Shao, Shengju Qian, Yu Liu | In this work, we answer this question by presenting a simple yet powerful operator – temporal interlacing network (TIN). |
| 1466 | Regularized Fine-Grained Meta Face Anti-Spoofing | Rui Shao, Xiangyuan Lan, Pong C. Yuen | Many face anti-spoofing methods have been proposed, but most of them ignore the generalization ability to unseen attacks. |
| 1467 | Multimodal Interaction-Aware Trajectory Prediction in Crowded Space | Xiaodan Shi, Xiaowei Shao, Zipei Fan, Renhe Jiang, Haoran Zhang, Zhiling Guo, Guangming Wu, Wei Yuan, Ryosuke Shibasaki | To address those issues, we propose a spatio-temporal model that can aggregate the information from socially interacting agents and capture the multimodality of the motion patterns. |
| 1468 | Optimal Feature Transport for Cross-View Image Geo-Localization | Yujiao Shi, Xin Yu, Liu Liu, Tong Zhang, Hongdong Li | This paper proposes a novel Cross-View Feature Transport (CVFT) technique to explicitly establish cross-view domain transfer that facilitates feature alignment between ground and aerial images. |
| 1469 | Identifying Model Weakness with Adversarial Examiner | Michelle Shu, Chenxi Liu, Weichao Qiu, Alan Yuille | In this paper, we are interested in systematic exploration of the input data space to identify the weakness of the model to be evaluated. |
| 1470 | Efficient Residual Dense Block Search for Image Super-Resolution | Dehua Song, Chang Xu, Xu Jia, Yiyi Chen, Chunjing Xu, Yunhe Wang | Focusing on this issue, we propose an efficient residual dense block search algorithm with multiple objectives to hunt for fast, lightweight and accurate networks for image super-resolution. |
| 1471 | KPNet: Towards Minimal Face Detector | Guanglu Song, Yu Liu, Yuhang Zang, Xiaogang Wang, Biao Leng, Qingsheng Yuan | In this work, we find that the appearance feature of a generic face is discriminative enough for a tiny and shallow neural network to verify from the background. |
| 1472 | Multi-Spectral Salient Object Detection by Adversarial Domain Adaptation | Shaoyue Song, Hongkai Yu, Zhenjiang Miao, Jianwu Fang, Kang Zheng, Cong Ma, Song Wang | We model this research problem as an adversarial domain adaptation from the existing RGB image dataset (source domain) to the collected multi-spectral dataset (target domain). |
| 1473 | Stereoscopic Image Super-Resolution with Stereo Consistent Feature | Wonil Song, Sungil Choi, Somi Jeong, Kwanghoon Sohn | To address this issue, in this paper, we propose a self and parallax attention mechanism (SPAM) to aggregate the information from its own image and the counterpart stereo image simultaneously, thus reconstructing high-quality stereoscopic SR image pairs. |
| 1474 | An Efficient Framework for Dense Video Captioning | Maitreya Suin, A. N. Rajagopalan | Instead, we propose a deep reinforcement-based approach which enables an agent to describe multiple events in a video by watching a portion of the frames. |
| 1475 | Fine-Grained Recognition: Accounting for Subtle Differences between Similar Classes | Guolei Sun, Hisham Cholakkal, Salman Khan, Fahad Khan, Ling Shao | Here, we propose to explicitly force the network to find the subtle differences among closely related classes. |
| 1476 | Relation-Aware Pedestrian Attribute Recognition with Graph Convolutional Networks | Zichang Tan, Yang Yang, Jun Wan, Guodong Guo, Stan Z. Li | In this paper, we propose a new end-to-end network, named Joint Learning of Attribute and Contextual relations (JLAC), to solve the task of pedestrian attribute recognition. |
| 1477 | R²MRF: Defocus Blur Detection via Recurrently Refining Multi-Scale Residual Features | Chang Tang, Xinwang Liu, Xinzhong Zhu, En Zhu, Kun Sun, Pichao Wang, Lizhe Wang, Albert Zomaya | In order to address these issues, we propose a deep neural network which Recurrently Refines Multi-scale Residual Features (R2MRF) for defocus blur detection. |
| 1478 | V-PROM: A Benchmark for Visual Reasoning Using Visual Progressive Matrices | Damien Teney, Peng Wang, Jiewei Cao, Lingqiao Liu, Chunhua Shen, Anton van den Hengel | We propose a new large-scale benchmark to evaluates abstract reasoning over real visual data. |
| 1479 | End-to-End Thorough Body Perception for Person Search | Kun Tian, Houjing Huang, Yun Ye, Shiyu Li, Jinbin Lin, Guan Huang | In this paper, we propose an improved end-to-end multi-branch person search network to jointly optimize person detection, re-identification, instance segmentation, and keypoint detection. |
| 1480 | Differentiable Meta-Learning Model for Few-Shot Semantic Segmentation | Pinzhuo Tian, Zhangkai Wu, Lei Qi, Lei Wang, Yinghuan Shi, Yang Gao | To deal with this issue, we formulate the few-shot semantic segmentation task as a learning-based pixel classification problem, and propose a novel framework called MetaSegNet based on meta-learning. |
| 1481 | Attention-Based View Selection Networks for Light-Field Disparity Estimation | Yu-Ju Tsai, Yu-Lun Liu, Ming Ouhyoung, Yung-Yu Chuang | This paper introduces a novel deep network for estimating depth maps from a light field image. |
| 1482 | Image Cropping with Composition and Saliency Aware Aesthetic Score Map | Yi Tu, Li Niu, Weijie Zhao, Dawei Cheng, Liqing Zhang | In this paper, we propose an interpretable image cropping model to unveil the mystery. |
| 1483 | Optical Flow in Deep Visual Tracking | Mikko Vihlman, Arto Visala | This paper argues that deep architectures are often fit to learn implicit representations of optical flow. |
| 1484 | TextScanner: Reading Characters in Order for Robust Scene Text Recognition | Zhaoyi Wan, Minghang He, Haoran Chen, Xiang Bai, Cong Yao | To tackle these challenges, we propose in this paper an alternative approach, called TextScanner, for scene text recognition. |
| 1485 | Progressive Feature Polishing Network for Salient Object Detection | Bo Wang, Quan Chen, Min Zhou, Zhiqiang Zhang, Xiaogang Jin, Kun Gai | We present Progressive Feature Polishing Network (PFPN), a simple yet effective framework to progressively polish the multi-level features to be more accurate and representative. |
| 1486 | Region-Based Global Reasoning Networks | Chuanming Wang, Huiyuan Fu, Charles X. Ling, Peilun Du, Huadong Ma | In this paper, we propose an novel approach that explores the relationship between regions which have richer semantics than pixels. |
| 1487 | Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-Identification | Guan-An Wang, Tianzhu Zhang, Yang Yang, Jian Cheng, Jianlong Chang, Xu Liang, Zeng-Guang Hou | Different from existing methods, in this paper, we propose to generate cross-modality paired-images and perform both global set-level and fine-grained instance-level alignments. |
| 1488 | Context Modulated Dynamic Networks for Actor and Action Video Segmentation with Language Queries | Hao Wang, Cheng Deng, Fan Ma, Yi Yang | Specifically, we propose a context modulated dynamic convolutional operation in the proposed framework. |
| 1489 | All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting | Hao Wang, Pu Lu, Hui Zhang, Mingkun Yang, Xiang Bai, Yongchao Xu, Mengchao He, Yongpan Wang, Wenyu Liu | With the representation of such boundary points, we establish a simple yet effective scheme for end-to-end text spotting, which can read the text of arbitrary shapes. |
| 1490 | Temporally Grounding Language Queries in Videos by Contextual Boundary-Aware Prediction | Jingwen Wang, Lin Ma, Wenhao Jiang | We address this issue by proposing an end-to-end boundary-aware model, which uses a lightweight branch to predict semantic boundaries corresponding to the given linguistic information. |
| 1491 | Show, Recall, and Tell: Image Captioning with Recall Mechanism | Li Wang, Zechen Bai, Yonghua Zhang, Hongtao Lu | In this paper, we propose a novel recall mechanism to imitate the way human conduct captioning. |
| 1492 | POST: POlicy-Based Switch Tracking | Ning Wang, Wengang Zhou, Guojun Qi, Houqiang Li | In this paper, we propose POST, a POlicy-based Switch Tracker for robust and efficient visual tracking. |
| 1493 | Sparsity-Inducing Binarized Neural Networks | Peisong Wang, Xiangyu He, Gang Li, Tianli Zhao, Jian Cheng | In this work, we propose the Sparsity-inducing Binarized Neural Network (Si-BNN), to quantize the activations to be either 0 or +1, which introduces sparsity into binary representation. |
| 1494 | Multi-Speaker Video Dialog with Frame-Level Temporal Localization | Qiang Wang, Pin Jiang, Zhiyi Guo, Yahong Han, Zhou Zhao | In this paper, we introduce a novel task of Multi-Speaker Video Dialog with frame-level Temporal Localization (MSVD-TL) to make video dialog systems more applicable. |
| 1495 | RDSNet: A New Deep Architecture forReciprocal Object Detection and Instance Segmentation | Shaoru Wang, Yongchao Gong, Junliang Xing, Lichao Huang, Chang Huang, Weiming Hu | This paper presents RDSNet, a novel deep architecture for reciprocal object detection and instance segmentation. |
| 1496 | Decoupled Attention Network for Text Recognition | Tianwei Wang, Yuanzhi Zhu, Lianwen Jin, Canjie Luo, Xiaoxue Chen, Yaqiang Wu, Qianying Wang, Mingxiang Cai | To remedy this issue, we propose a decoupled attention network (DAN), which decouples the alignment operation from using historical decoding results. |
| 1497 | One-Shot Learning for Long-Tail Visual Relation Detection | Weitao Wang, Meng Wang, Sen Wang, Guodong Long, Lina Yao, Guilin Qi, Yang Chen | With this in mind, we designed a novel model for visual relation detection that works in one-shot settings. |
| 1498 | Consistent Video Style Transfer via Compound Regularization | Wenjing Wang, Jizheng Xu, Li Zhang, Yue Wang, Jiaying Liu | Combining with the new cost formula, we design a zero-shot video style transfer framework. |
| 1499 | Mis-Classified Vector Guided Softmax Loss for Face Recognition | Xiaobo Wang, Shifeng Zhang, Shuo Wang, Tianyu Fu, Hailin Shi, Tao Mei | Thus we can address all the above issues and achieve more discriminative face features. |
| 1500 | Symbiotic Attention with Privileged Information for Egocentric Action Recognition | Xiaohan Wang, Yu Wu, Linchao Zhu, Yi Yang | In this paper, we propose a novel Symbiotic Attention framework leveraging Privileged information (SAP) for egocentric video recognition. |
| 1501 | Task-Aware Monocular Depth Estimation for 3D Object Detection | Xinlong Wang, Wei Yin, Tao Kong, Yuning Jiang, Lei Li, Chunhua Shen | Applying ForeSeE to 3D object detection, we achieve 7.5 AP gains and set new state-of-the-art results among other monocular methods. |
| 1502 | Multi-Label Classification with Label Graph Superimposing | Ya Wang, Dongliang He, Fu Li, Xiang Long, Zhichao Zhou, Jinwen Ma, Shilei Wen | In this paper, we propose a label graph superimposing framework to improve the conventional GCN+CNN framework developed for multi-label recognition in the following two aspects. |
| 1503 | Pruning from Scratch | Yulong Wang, Xiaolu Zhang, Lingxi Xie, Jun Zhou, Hang Su, Bo Zhang, Xiaolin Hu | Therefore, we propose a novel network pruning pipeline which allows pruning from scratch with little training overhead. |
| 1504 | Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions | Zhenyi Wang, Ping Yu, Yang Zhao, Ruiyi Zhang, Yufan Zhou, Junsong Yuan, Changyou Chen | In this paper, we focus on skeleton-based action generation and propose to model smooth and diverse transitions on a latent space of action sequences with much lower dimensionality. |
| 1505 | Graph-Propagation Based Correlation Learning for Weakly Supervised Fine-Grained Image Classification | Zhuhui Wang, Shijie Wang, Haojie Li, Zhi Dou, Jianjun Li | To address these issues, we propose an end-to-end Graph-propagation based Correlation Learning (GCL) model to fully mine and exploit the discriminative potentials of region correlations for WFGIC. |
| 1506 | Localize, Assemble, and Predicate: Contextual Object Proposal Embedding for Visual Relation Detection | Ruihai Wu, Kehan Xu, Chenchen Liu, Nan Zhuang, Yadong Mu | To address this problem, we propose localize-assemble-predicate network (LAP-Net), which decomposes VRD into three sub-tasks: localizing individual objects, assembling and predicting the subject-object pairs. |
| 1507 | EFANet: Exchangeable Feature Alignment Network for Arbitrary Style Transfer | Zhijie Wu, Chunjin Song, Yang Zhou, Minglun Gong, Hui Huang | Instead, we propose a novel transfer framework, EFANet, that aims to jointly analyze and better align exchangeable features extracted from the content and style image pair. |
| 1508 | Adaptive Cross-Modal Embeddings for Image-Text Alignment | Jonatas Wehrmann, Camila Kolling, Rodrigo C Barros | Adaptive Cross-Modal Embeddings for Image-Text Alignment |
| 1509 | F³Net: Fusion, Feedback and Focus for Salient Object Detection | Jun Wei, Shuhui Wang, Qingming Huang | In this paper, we propose the F3Net to solve above problem, which mainly consists of cross feature module (CFM) and cascaded feedback decoder (CFD) trained by minimizing a new pixel position aware loss (PPA). |
| 1510 | 3D Single-Person Concurrent Activity Detection Using Stacked Relation Network | Yi Wei, Wenbo Li, Yanbo Fan, Linghan Xu, Ming-Ching Chang, Siwei Lyu | For the sake of generalization, we propose an approach based on a decompositional design to learn a dedicated feature representation for each activity class. |
| 1511 | Heuristic Black-Box Adversarial Attacks on Video Recognition Models | Zhipeng Wei, Jingjing Chen, Xingxing Wei, Linxi Jiang, Tat-Seng Chua, Fengfeng Zhou, Yu-Gang Jiang | To overcome this challenge, we propose a heuristic black-box attack model that generates adversarial perturbations only on the selected frames and regions. |
| 1512 | Efficient Querying from Weighted Binary Codes | Zhenyu Weng, Yuesheng Zhu | In this paper, we propose a new method to rank the weighted binary codes and return the nearest weighted binary codes of the query efficiently. |
| 1513 | Online Hashing with Efficient Updating of Binary Codes | Zhenyu Weng, Yuesheng Zhu | In this paper, we propose a novel online hashing framework to update the binary codes efficiently without accumulating the whole database. |
| 1514 | Tracklet Self-Supervised Learning for Unsupervised Person Re-Identification | Guile Wu, Xiatian Zhu, Shaogang Gong | In this work, we formulate a novel tracklet self-supervised learning (TSSL) method, which is capable of capitalising directly from abundant unlabelled tracklet data, to optimise a feature embedding space for both video and image unsupervised re-id. |
| 1515 | CircleNet for Hip Landmark Detection | Hai Wu, Hongtao Xie, Chuanbin Liu, Zheng-Jun Zha, Jun Sun, Yongdong Zhang | In this paper, we propose a much simpler and more efficient framework called CircleNet to improve the accuracy of landmark detection by predicting landmark and corresponding radius. We construct a professional DDH dataset for the first time and evaluate our CircleNet on it. |
| 1516 | 3D Human Pose Estimation via Explicit Compositional Depth Maps | Haiping Wu, Bin Xiao | In this work, we tackle the problem of estimating 3D human pose in camera space from a monocular image. |
| 1517 | Tree-Structured Policy Based Progressive Reinforcement Learning for Temporally Language Grounding in Video | Jie Wu, Guanbin Li, Si Liu, Liang Lin | Inspired by human’s coarse-to-fine decision-making paradigm, we formulate a novel Tree-Structured Policy based Progressive Reinforcement Learning (TSP-PRL) framework to sequentially regulate the temporal boundary by an iterative refinement process. |
| 1518 | Distraction-Aware Feature Learning for Human Attribute Recognition via Coarse-to-Fine Attention Mechanism | Mingda Wu, Di Huang, Yuanfang Guo, Yunhong Wang | In this paper, we propose a novel deep learning approach to HAR, namely Distraction-aware HAR (Da-HAR). |
| 1519 | Patch Proposal Network for Fast Semantic Segmentation of High-Resolution Images | Tong Wu, Zhenzhen Lei, Bingqian Lin, Cuihua Li, Yanyun Qu, Yuan Xie | To solve this problem, we introduce a patch proposal network (PPN) in this paper, which adaptively distinguishes the critical patches from the trivial ones to fuse with the whole image for refining segmentation. |
| 1520 | SalSAC: A Video Saliency Prediction Model with Shuffled Attentions and Correlation-Based ConvLSTM | Xinyi Wu, Zhenyao Wu, Jinglin Zhang, Lili Ju, Song Wang | In this paper, we propose a novel end-to-end neural network “SalSAC” for video saliency prediction, which uses the CNN-LSTM-Attention as the basic architecture and utilizes the information from both static and dynamic aspects. |
| 1521 | Recognizing Instagram Filtered Images with Feature De-Stylization | Zhe Wu, Zuxuan Wu, Bharat Singh, Larry Davis | This paper presents a study on how popular pretrained models are affected by commonly used Instagram filters. |
| 1522 | Convolutional Hierarchical Attention Network for Query-Focused Video Summarization | Shuwen Xiao, Zhou Zhao, Zijian Zhang, Xiaohui Yan, Min Yang | In this paper, we consider the task as a problem of computing similarity between video shots and query. |
| 1523 | Adversarial Learning of Privacy-Preserving and Task-Oriented Representations | Taihong Xiao, Yi-Hsuan Tsai, Kihyuk Sohn, Manmohan Chandraker, Ming-Hsuan Yang | Our work aims at learning a privacy-preserving and task-oriented representation to defend against such model inversion attacks. |
| 1524 | Motion-Based Generator Model: Unsupervised Disentanglement of Appearance, Trackable and Intrackable Motions in Dynamic Patterns | Jianwen Xie, Ruiqi Gao, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu | We use a maximum likelihood algorithm to learn the model parameters that iterates between inferring latent noise vectors that drive the transition model and updating the parameters given the inferred latent vectors. |
| 1525 | Segmenting Medical MRI via Recurrent Decoding Cell | Ying Wen, Kai Xie, Lianghua He | In this paper, we propose a novel feature fusion unit called Recurrent Decoding Cell (RDC) which leverages convolutional RNNs to memorize the long-term context information from the previous layers in the decoding phase. |
| 1526 | PI-RCNN: An Efficient Multi-Sensor 3D Object Detector with Point-Based Attentive Cont-Conv Fusion Module | Liang Xie, Chao Xiang, Zhengxu Yu, Guodong Xu, Zheng Yang, Deng Cai, Xiaofei He | In this paper, we propose a novel fusion approach named Point-based Attentive Cont-conv Fusion(PACF) module, which fuses multi-sensor features directly on 3D points. |
| 1527 | Video Face Super-Resolution with Motion-Adaptive Feedback Cell | Jingwei Xin, Nannan Wang, Jie Li, Xinbo Gao, Zhifeng Li | In this paper, we propose a Motion-Adaptive Feedback Cell (MAFC), a simple but effective block, which can efficiently capture the motion compensation and feed it back to the network in an adaptive way. |
| 1528 | Facial Attribute Capsules for Noise Face Super Resolution | Jingwei Xin, Nannan Wang, Xinrui Jiang, Jie Li, Xinbo Gao, Zhifeng Li | In this paper, we propose a Facial Attribute Capsules Network (FACN) to deal with the problem of high-scale super-resolution of noisy face image. |
| 1529 | FusionDN: A Unified Densely Connected Network for Image Fusion | Han Xu, Jiayi Ma, Zhuliang Le, Junjun Jiang, Xiaojie Guo | In this paper, we present a new unsupervised and unified densely connected network for different types of image fusion tasks, termed as FusionDN. |
| 1530 | Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN | Hang Xu, Linpu Fang, Xiaodan Liang, Wenxiong Kang, Zhenguo Li | In this paper, we address the problem of designing a universal object detection model that exploits diverse category granularity from multiple domains and predict all kinds of categories in one system. |
| 1531 | Geometry Sharing Network for 3D Point Cloud Classification and Segmentation | Mingye Xu, Zhipeng Zhou, Yu Qiao | To address this challenge, we propose Geometry Sharing Network (GS-Net) which effectively learns point descriptors with holistic context to enhance the robustness to geometric transformations. |
| 1532 | Learning Inverse Depth Regression for Multi-View Stereo with Correlation Cost Volume | Qingshan Xu, Wenbing Tao | Inspired by the group-wise correlation in stereo matching, we propose an average group-wise correlation similarity measure to construct a lightweight cost volume. |
| 1533 | Planar Prior Assisted PatchMatch Multi-View Stereo | Qingshan Xu, Wenbing Tao | By taking advantage of planar models and PatchMatch multi-view stereo, we propose a planar prior assisted PatchMatch multi-view stereo framework in this paper. |
| 1534 | A Proposal-Based Approach for Activity Image-to-Video Retrieval | Ruicong Xu, Li Niu, Jianfu Zhang, Liqing Zhang | In this paper, we utilize R-C3D model to represent a video by a bag of activity proposals, which can filter out background segments to some extent. |
| 1535 | GDFace: Gated Deformation for Multi-View Face Image Synthesis | Xuemiao Xu, Keke Li, Cheng Xu, Shengfeng He | In this paper, we propose a Gated Deformable Face Synthesis Network to model the deformation of faces that aids the synthesis of the target face image. |
| 1536 | CF-LSTM: Cascaded Feature-Based Long Short-Term Networks for Predicting Pedestrian Trajectory | Yi Xu, Jing Yang, Shaoyi Du | In order to address this issue, we propose a novel feature-cascaded framework for long short-term network (CF-LSTM) without extra artificial settings or social rules. |
| 1537 | SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines | Yinda Xu, Zeyu Wang, Zuoxin Li, Ye Yuan, Gang Yu | Based on a careful analysis, we propose a set of practical guidelines of target state estimation for high-performance generic object tracker design. |
| 1538 | ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection | Zhenbo Xu, Wei Zhang, Xiaoqing Ye, Xiao Tan, Wei Yang, Shilei Wen, Errui Ding, Ajin Meng, Liusheng Huang | In this paper, we present a novel framework named ZoomNet for stereo imagery-based 3D detection. In addition, since the official KITTI benchmark lacks fine-grained annotations like pixel-wise part locations, we also present our KFG dataset by augmenting KITTI with detailed instance-wise annotations including pixel-wise part location, pixel-wise disparity, etc.. |
| 1539 | Shape-Aware Organ Segmentation by Predicting Signed Distance Maps | Yuan Xue, Hui Tang, Zhi Qiao, Guanzhong Gong, Yong Yin, Zhen Qian, Chao Huang, Wei Fan, Xiaolei Huang | In this work, we propose to resolve the issue existing in current deep learning based organ segmentation systems that they often produce results that do not capture the overall shape of the target organ and often lack smoothness. |
| 1540 | FAS-Net: Construct Effective Features Adaptively for Multi-Scale Object Detection | Jiangqiao Yan, Yue Zhang, Zhonghan Chang, Tengfei Zhang, Menglong Yan, Wenhui Diao, Hongqi Wang, Xian Sun | In this work, a novel method called feature adaptive selection subnetwork (FAS-Net) is proposed to construct effective features for detecting objects of different scales. |
| 1541 | Gated Convolutional Networks with Hybrid Connectivity for Image Classification | Chuanguang Yang, Zhulin An, Hui Zhu, Xiaolong Hu, Kun Zhang, Kaiqiang Xu, Chao Li, Yongjun Xu | We propose a simple yet effective method to reduce the redundancy of DenseNet by substantially decreasing the number of stacked modules by replacing the original bottleneck by our SMG module, which is augmented by local residual. |
| 1542 | Mining on Heterogeneous Manifolds for Zero-Shot Cross-Modal Image Retrieval | Fan Yang, Zheng Wang, Jing Xiao, Shin'ichi Satoh | We propose a bi-directional random walk scheme to mining more reliable relationships between images by traversing heterogeneous manifolds in the feature space of each modality. |
| 1543 | Asymmetric Co-Teaching for Unsupervised Cross-Domain Person Re-Identification | Fengxiang Yang, Ke Li, Zhun Zhong, Zhiming Luo, Xing Sun, Hao Cheng, Xiaowei Guo, Feiyue Huang, Rongrong Ji, Shaozi Li | In this study, we argue that by explicitly adding a sample filtering procedure after the clustering, the mined examples can be much more efficiently used. |
| 1544 | Learning to Incorporate Structure Knowledge for Image Inpainting | Jie Yang, Zhiquan Qi, Yong Shi | This paper develops a multi-task learning framework that attempts to incorporate the image structure knowledge to assist image inpainting, which is not well explored in previous works. |
| 1545 | An Adversarial Perturbation Oriented Domain Adaptation Approach for Semantic Segmentation | Jihan Yang, Ruijia Xu, Ruiyu Li, Xiaojuan Qi, Xiaoyong Shen, Guanbin Li, Liang Lin | In contrast to adversarial alignment, we propose to explicitly train a domain-invariant classifier by generating and defensing against pointwise feature space adversarial perturbations. |
| 1546 | FAN-Face: a Simple Orthogonal Improvement to Deep Face Recognition | Jing Yang, Adrian Bulat, Georgios Tzimiropoulos | This paper proposes a simple approach to face recognition which gradually integrates features from different layers of a facial landmark localization network into different layers of the recognition network. |
| 1547 | Towards Scale-Free Rain Streak Removal via Self-Supervised Fractal Band Learning | Wenhan Yang, Shiqi Wang, Dejia Xu, Xiaodong Wang, Jiaying Liu | In this paper, we propose a novel deep-learning based rain streak removal method injected with self-supervision to improve the ability to remove rain streaks in various scales. |
| 1548 | SOGNet: Scene Overlap Graph Network for Panoptic Segmentation | Yibo Yang, Hongyang Li, Xia Li, Qijie Zhao, Jianlong Wu, Zhouchen Lin | In this study, we aim to model overlap relations among instances and resolve them for panoptic segmentation. |
| 1549 | Release the Power of Online-Training for Robust Visual Tracking | Yifan Yang, Guorong Li, Yuankai Qi, QIngming Huang | In this paper, we propose to improve the tracking accuracy via online training. |
| 1550 | Context-Transformer: Tackling Object Confusion for Few-Shot Detection | Ze Yang, Yali Wang, Xianyu Chen, Jianzhuang Liu, Yu Qiao | To tackle this problem, we propose a novel Context-Transformer within a concise deep transfer framework. |
| 1551 | SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detection | Lewei Yao, Hang Xu, Wei Zhang, Xiaodan Liang, Zhenguo Li | In this paper, we present a two-stage coarse-to-fine searching strategy named Structural-to-Modular NAS (SM-NAS) for searching a GPU-friendly design of both an efficient combination of modules and better modular-level architecture for object detection. |
| 1552 | Deep Discriminative CNN with Temporal Ensembling for Ambiguously-Labeled Image Classification | Yao Yao, Jiehui Deng, Xiuhua Chen, Chen Gong, Jianxin Wu, Jian Yang | In this paper, we study the problem of image classification where training images are ambiguously annotated with multiple candidate labels, among which only one is correct but is not accessible during the training phase. |
| 1553 | Object-Guided Instance Segmentation for Biological Images | Jingru Yi, Hui Tang, Pengxiang Wu, Bo Liu, Daniel J. Hoeppner, Dimitris N. Metaxas, Lianyi Han, Wei Fan | In this paper, we propose a new box-based instance segmentation method. |
| 1554 | Leveraging Multi-View Image Sets for Unsupervised Intrinsic Image Decomposition and Highlight Separation | Renjiao Yi, Ping Tan, Stephen Lin | We present an unsupervised approach for factorizing object appearance into highlight, shading, and albedo layers, trained by multi-view real images. To do so, we construct a multi-view dataset by collecting numerous customer product photos online, which exhibit large illumination variations that make them suitable for training of reflectance separation and can facilitate object-level decomposition. |
| 1555 | Joint Super-Resolution and Alignment of Tiny Faces | Yu Yin, Joseph Robinson, Yulun Zhang, Yun Fu | Thus, we propose a joint alignment and SR network to simultaneously detect facial landmarks and super-resolve tiny faces. |
| 1556 | Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution | Yingruo Fan, Jacqueline Lam, Victor Li | In contrast, we present a new learning framework that automatically learns the latent relationships of AUs via establishing semantic correspondences between feature maps. |
| 1557 | Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification | Renchun You, Zhiyao Guo, Lei Cui, Xiang Long, Yingze Bao, Shilei Wen | Based on the constructed label graph, we propose an adjacency-based similarity graph embedding method to learn semantic label embeddings, which explicitly exploit label relationships. |
| 1558 | Pointwise Rotation-Invariant Network with Adaptive Sampling and 3D Spherical Voxel Convolution | Yang You, Yujing Lou, Qi Liu, Yu-Wing Tai, Lizhuang Ma, Cewu Lu, Weiming Wang | In this paper, we propose a brand new point-set learning framework PRIN, namely, Pointwise Rotation-Invariant Network, focusing on rotation-invariant feature extraction in point clouds analysis. |
| 1559 | Cascading Convolutional Color Constancy | Huanglin Yu, Ke Chen, Kaiqi Wang, Yanlin Qian, Zhaoxiang Zhang, Kui Jia | In this paper, we introduce a novel algorithm – Cascading Convolutional Color Constancy (in short, C4) to improve robustness of regression learning and achieve stable generalization capability across datasets (different cameras and scenes) in a unique framework. |
| 1560 | Region Normalization for Image Inpainting | Tao Yu, Zongyu Guo, Xin Jin, Shilin Wu, Zhibo Chen, Weiping Li, Zhizheng Zhang, Sen Liu | In this work, we show that the mean and variance shifts caused by full-spatial FN limit the image inpainting network training and we propose a spatial region-wise normalization named Region Normalization (RN) to overcome the limitation. |
| 1561 | Patchy Image Structure Classification Using Multi-Orientation Region Transform | Xiaohan Yu, Yang Zhao, Yongsheng Gao, Shengwu Xiong, Xiaohui Yuan | To address above limitations, this paper proposes a novel Multi-Orientation Region Transform (MORT), which can effectively characterize both contour and structure features simultaneously, for patchy image structure classification. |
| 1562 | Human Synthesis and Scene Compositing | Mihai Zanfir, Elisabeta Oneata, Alin-Ionut Popa, Andrei Zanfir, Cristian Sminchisescu | In this paper, we propose a HUSC (HUman Synthesis and Scene Compositing) framework for the realistic synthesis of humans with different appearance, in novel poses and scenes. |
| 1563 | Realistic Face Reenactment via Self-Supervised Disentangling of Identity and Pose | Xianfang Zeng, Yusu Pan, Mengmeng Wang, Jiangning Zhang, Yong Liu | To alleviate the demand for manual annotations, in this paper, we propose a novel self-supervised hybrid model (DAE-GAN) that learns how to reenact face naturally given large amounts of unlabeled videos. |
| 1564 | Reliability Does Matter: An End-to-End Weakly Supervised Semantic Segmentation Approach | Bingfeng Zhang, Jimin Xiao, Yunchao Wei, Mingjie Sun, Kaizhu Huang | In this work, we harness the image-level labels to produce reliable pixel-level annotations and design a fully end-to-end network to learn to predict segmentation maps. |
| 1565 | Shape-Oriented Convolution Neural Network for Point Cloud Analysis | Chaoyi Zhang, Yang Song, Lina Yao, Weidong Cai | Shape-Oriented Convolution Neural Network for Point Cloud Analysis |
| 1566 | Web-Supervised Network with Softly Update-Drop Training for Fine-Grained Visual Classification | Chuanyi Zhang, Yazhou Yao, Huafeng Liu, Guo-Sen Xie, Xiangbo Shu, Tianfei Zhou, Zheng Zhang, Fumin Shen, Zhenmin Tang | In this paper, we propose a novel approach to remove irrelevant samples from the real-world web images during training, and only utilize useful images for updating the networks. |
| 1567 | FDN: Feature Decoupling Network for Head Pose Estimation | Hao Zhang, Mengmeng Wang, Yong Liu, Yi Yuan | In this paper, we propose a novel three-branch network architecture, termed as Feature Decoupling Network (FDN), a more powerful architecture for landmark-free head pose estimation from a single RGB image. |
| 1568 | Rethinking the Image Fusion: A Fast Unified Image Fusion Network based on Proportional Maintenance of Gradient and Intensity | Hao Zhang, Han Xu, Yang Xiao, Xiaojie Guo, Jiayi Ma | In this paper, we propose a fast unified image fusion network based on proportional maintenance of gradient and intensity (PMGI), which can end-to-end realize a variety of image fusion tasks, including infrared and visible image fusion, multi-exposure image fusion, medical image fusion, multi-focus image fusion and pan-sharpening. |
| 1569 | Model Watermarking for Image Processing Networks | Jie Zhang, Dongdong Chen, Jing Liao, Han Fang, Weiming Zhang, Wenbo Zhou, Hao Cui, Nenghai Yu | In this paper, we propose the first model watermarking framework for protecting image processing models. |
| 1570 | Deep Object Co-Segmentation via Spatial-Semantic Network Modulation | Kaihua Zhang, Jin Chen, Bo Liu, Qingshan Liu | This paper presents a spatial and semantic modulated deep network framework for object co-segmentation. |
| 1571 | Pixel-Aware Deep Function-Mixture Network for Spectral Super-Resolution | Lei Zhang, Zhiqiang Lang, Peng Wang, Wei Wei, Shengcai Liao, Ling Shao, Yanning Zhang | To address this problem, we propose a pixel-aware deep function-mixture network for SSR, which is composed of a new class of modules, termed function-mixture (FM) blocks. |
| 1572 | RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal | Ling Zhang, Chengjiang Long, Xiaolong Zhang, Chunxia Xiao | In this paper, we propose a general and novel framework RIS-GAN which explores residual and illumination with Generative Adversarial Networks for shadow removal. |
| 1573 | 3D Crowd Counting via Multi-View Fusion with 3D Gaussian Kernels | Qi Zhang, Antoni B. Chan | Unlike MVMS, we propose to solve the multi-view crowd counting task through 3D feature fusion with 3D scene-level density maps, instead of the 2D ground-plane ones. |
| 1574 | Deep Camouflage Images | Qing Zhang, Gelin Yin, Yongwei Nie, Wei-Shi Zheng | To overcome these limitations, we present a novel neural style transfer approach that adopts the visual perception mechanism to create camouflage images, which allows us to hide objects more effectively while producing natural-looking results. |
| 1575 | AutoRemover: Automatic Object Removal for Autonomous Driving Videos | Rong Zhang, Wei Li, Peng Wang, Chenye Guan, Jin Fang, Yuhang Song, Jinhui Yu, Baoquan Chen, Weiwei Xu, Ruigang Yang | Motivated by the need for photo-realistic simulation in autonomous driving, in this paper we present a video inpainting algorithm AutoRemover, designed specifically for generating street-view videos without any moving objects. To deal with shadows, we build up an autonomous driving shadow dataset and design a deep neural network to detect shadows automatically. |
| 1576 | Knowledge Integration Networks for Action Recognition | Shiwen Zhang, Sheng Guo, Limin Wang, Weilin Huang, Matthew Scott | In this work, we propose Knowledge Integration Networks (referred as KINet) for video action recognition. |
| 1577 | Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language | Songyang Zhang, Houwen Peng, Jianlong Fu, Jiebo Luo | In this paper, we model the temporal relations between video moments by a two-dimensional map, where one dimension indicates the starting time of a moment and the other indicates the end time. |
| 1578 | Single Camera Training for Person Re-Identification | Tianyu Zhang, Lingxi Xie, Longhui Wei, Yongfei Zhang, Bo Li, Qi Tian | We start with a regular deep network for feature extraction, upon which we propose a novel loss function named multi-camera negative loss (MCNL). |
| 1579 | Multi-Instance Multi-Label Action Recognition and Localization Based on Spatio-Temporal Pre-Trimming for Untrimmed Videos | Xiao-Yu Zhang, Haichao Shi, Changsheng Li, Peng Li | In this paper, we propose a novel multi-instance multi-label modeling network based on spatio-temporal pre-trimming to recognize actions and locate corresponding frames in untrimmed videos. |
| 1580 | FACT: Fused Attention for Clothing Transfer with Generative Adversarial Networks | Yicheng Zhang, Lei Li, Li Song, Rong Xie, Wenjun Zhang | To tackle this problem, we propose a novel semantic-based Fused Attention model for Clothing Transfer (FACT), which allows fine-grained synthesis, high global consistency and plausible hallucination in images. |
| 1581 | Find Objects and Focus on Highlights: Mining Object Semantics for Video Highlight Detection via Graph Neural Networks | Yingying Zhang, Junyu Gao, Xiaoshan Yang, Chang Liu, Yan Li, Changsheng Xu | Therefore, we propose a novel video highlight framework, named VH-GNN, to construct an object-aware graph and model the relationships between objects from a global view. |
| 1582 | When Radiology Report Generation Meets Knowledge Graph | Yixiao Zhang, Xiaosong Wang, Ziyue Xu, Qihang Yu, Alan Yuille, Daguang Xu | Based on these concerns, we propose to utilize a pre-constructed graph embedding module (modeled with a graph convolutional neural network) on multiple disease findings to assist the generation of reports in this work. |
| 1583 | Exploiting Motion Information from Unlabeled Videos for Static Image Action Recognition | Yiyi Zhang, Li Niu, Ziqi Pan, Meichao Luo, Jianfu Zhang, Dawei Cheng, Liqing Zhang | In this paper, we integrate the above two strategies in a unified framework, which consists of Visual Representation Enhancement (VRE) module and Motion Representation Augmentation (MRA) module. |
| 1584 | Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching | Youmin Zhang, Yimin Chen, Xiao Bai, Suihanjin Yu, Kun Yu, Zhiwei Li, Kuiyuan Yang | In this paper, we propose to directly add constraints to the cost volume by filtering cost volume with unimodal distribution peaked at true disparities. |
| 1585 | Fully Convolutional Network for Consistent Voxel-Wise Correspondence | Yungeng Zhang, Yuru Pei, Yuke Guo, Gengyu Ma, Tianmin Xu, Hongbin Zha | In this paper, we propose a fully convolutional network-based dense map from voxels to invertible pair of displacement vector fields regarding a template grid for the consistent voxel-wise correspondence. |
| 1586 | Zero-Shot Sketch-Based Image Retrieval via Graph Convolution Network | Zhaolong Zhang, Yuejie Zhang, Rui Feng, Tao Zhang, Weiguo Fan | In this paper, we propose a SketchGCN model utilizing the graph convolution network, which simultaneously considers both the visual information and the semantic information. |
| 1587 | JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds | Lin Zhao, Wenbing Tao | In this paper, we propose a novel joint instance and semantic segmentation approach, which is called JSNet, in order to address the instance and semantic segmentation of 3D point clouds simultaneously. |
| 1588 | Spherical Criteria for Fast and Accurate 360° Object Detection | Pengyu Zhao, Ansheng You, Yuanxing Zhang, Jiaying Liu, Kaigui Bian, Yunhai Tong | In this paper, we introduce a novel spherical criteria for fast and accurate 360◦ object detection, including both spherical bounding boxes and spherical IoU (SphIoU). To validate the design of spherical criteria and Reprojection R-CNN, we construct two unbiased synthetic datasets for training and evaluation. |
| 1589 | GTNet: Generative Transfer Network for Zero-Shot Object Detection | Shizhen Zhao, Changxin Gao, Yuanjie Shao, Lerenhan Li, Changqian Yu, Zhong Ji, Nong Sang | We propose a Generative Transfer Network (GTNet) for zero-shot object detection (ZSD). |
| 1590 | Multi-Source Distilling Domain Adaptation | Sicheng Zhao, Guangzhi Wang, Shanghang Zhang, Yang Gu, Yaxian Li, Zhichao Song, Pengfei Xu, Runbo Hu, Hua Chai, Kurt Keutzer | Conventional DA methods usually assume that the labeled data is sampled from a single source distribution. |
| 1591 | MemCap: Memorizing Style Knowledge for Image Captioning | Wentian Zhao, Xinxiao Wu, Xiaoxun Zhang | In this paper, we propose MemCap, a novel stylized image captioning method that explicitly encodes the knowledge about linguistic styles with memory mechanism. |
| 1592 | Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression | Zhaohui Zheng, Ping Wang, Wei Liu, Jinze Li, Rongguang Ye, Dongwei Ren | In this paper, we propose a Distance-IoU (DIoU) loss by incorporating the normalized distance between the predicted box and the target box, which converges much faster in training than IoU and GIoU losses. |
| 1593 | Random Erasing Data Augmentation | Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, Yi Yang | In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN). |
| 1594 | Spatial-Temporal Multi-Cue Network for Continuous Sign Language Recognition | Hao Zhou, Wengang Zhou, Yun Zhou, Houqiang Li | By injecting multi-cue learning into neural network design, we propose a spatial-temporal multi-cue (STMC) network to solve the vision-based sequence learning problem. |
| 1595 | Discriminative and Robust Online Learning for Siamese Visual Tracking | Jinghao Zhou, Peng Wang, Haoyang Sun | Therefore, we propose an online module with an attention mechanism for offline siamese networks to extract target-specific features under L2 error. |
| 1596 | Deep Domain-Adversarial Image Generation for Domain Generalisation | Kaiyang Zhou, Yongxin Yang, Timothy Hospedales, Tao Xiang | In this paper, we propose a novel DG approach based on Deep Domain-Adversarial Image Generation (DDAIG). |
| 1597 | Progressive Bi-C3D Pose Grammar for Human Pose Estimation | Lu Zhou, Yingying Chen, Jinqiao Wang, Hanqing Lu | In this paper, we propose a progressive pose grammar network learned with Bi-C3D (Bidirectional Convolutional 3D) for human pose estimation. |
| 1598 | Unified Vision-Language Pre-Training for Image Captioning and VQA | Luowei Zhou, Hamid Palangi, Lei Zhang, Houdong Hu, Jason Corso, Jianfeng Gao | This paper presents a unified Vision-Language Pre-training (VLP) model. |
| 1599 | Ladder Loss for Coherent Visual-Semantic Embedding | Mo Zhou, Zhenxing Niu, Le Wang, Zhanning Gao, Qilin Zhang, Gang Hua | In this paper, we introduce a continuous variable to model the relevance degree between queries and multiple candidates, and propose to learn a coherent embedding space, where candidates with higher relevance degrees are mapped closer to the query than those with lower relevance degrees. |
| 1600 | Generate, Segment, and Refine: Towards Generic Manipulation Segmentation | Peng Zhou, Bor-Chun Chen, Xintong Han, Mahyar Najibi, Abhinav Shrivastava, Ser-Nam Lim, Larry Davis | We address this problem in this paper, for which we introduce a manipulated image generation process that creates true positives using currently available datasets. |
| 1601 | Motion-Attentive Transition for Zero-Shot Video Object Segmentation | Tianfei Zhou, Shunzhou Wang, Yi Zhou, Yazhou Yao, Jianwu Li, Ling Shao | In this paper, we present a novel Motion-Attentive Transition Network (MATNet) for zero-shot video object segmentation, which provides a new way of leveraging motion information to reinforce spatio-temporal object representation. |
| 1602 | When AWGN-Based Denoiser Meets Real Noises | Yuqian Zhou, Jianbo Jiao, Haibin Huang, Yang Wang, Jue Wang, Honghui Shi, Thomas Huang | In this paper, we propose a novel approach to boost the performance of a real image denoiser which is trained only with synthetic pixel-independent noise data dominated by AWGN. |
| 1603 | Multi-Type Self-Attention Guided Degraded Saliency Detection | Ziqi Zhou, Zheng Wang, Huchuan Lu, Song Wang, Meijun Sun | In this paper, we systematically analyze the current status of the research on detecting salient objects from degraded images and then propose a new multi-type self-attention network, namely MSANet, for degraded saliency detection. |
| 1604 | Towards Omni-Supervised Face Alignment for Large Scale Unlabeled Videos | Congcong Zhu, Hao Liu*(corresponding author), Zhenhua Yu, Xuehong Sun | In this paper, we propose a spatial-temporal relational reasoning networks (STRRN) approach to investigate the problem of omni-supervised face alignment in videos. |
| 1605 | FASTER Recurrent Networks for Efficient Video Classification | Linchao Zhu, Du Tran, Laura Sevilla-Lara, Yi Yang, Matt Feiszli, Heng Wang | In this paper, we propose a novel framework named FASTER, i.e., Feature Aggregation for Spatio-TEmporal Redundancy. |
| 1606 | EEMEFN: Low-Light Image Enhancement via Edge-Enhanced Multi-Exposure Fusion Network | Minfeng Zhu, Pingbo Pan, Wei Chen, Yi Yang | In this paper, we propose a two-stage method called Edge-Enhanced Multi-Exposure Fusion Network (EEMEFN) to enhance extremely low-light images. |
| 1607 | Viewpoint-Aware Loss with Angular Regularization for Person Re-Identification | Zhihui Zhu, Xinyang Jiang, Feng Zheng, Xiaowei Guo, Feiyue Huang, Xing Sun, Weishi Zheng | To address this problem, we propose a novel approach, called Viewpoint-Aware Loss with Angular Regularization (VA-reID). |
| 1608 | iFAN: Image-Instance Full Alignment Networks for Adaptive Object Detection | Chenfan Zhuang, Xintong Han, Weilin Huang, Matthew Scott | We propose Image-Instance Full Alignment Networks (iFAN) to tackle this problem by precisely aligning feature distributions on both image and instance levels: 1) Image-level alignment: multi-scale features are roughly aligned by training adversarial domain classifiers in a hierarchically-nested fashion. |
| 1609 | Learning Attentive Pairwise Interaction for Fine-Grained Classification | Peiqin Zhuang, Yali Wang, Yu Qiao | Inspired by this fact, this paper proposes a simple but effective Attentive Pairwise Interaction Network (API-Net), which can progressively recognize a pair of fine-grained images by interaction. |