Paper Digest: AAAI 2019 Highlights

January 26, 2019October 8, 2019 admin

The AAAI Conference on Artificial Intelligence (AAAI) is one of the top artificial intelligence conferences in the world. In 2019, it is to be held in Honolulu, Hawaii. There were 7,095 paper submissions, of which more than 1100 were accepted.

To help AI 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.

We thank all authors for writing these interesting papers, and readers for reading our digests. If you do not want to miss any interesting AI paper, you are welcome to sign up our free paper digest service to get new paper updates customized to your own interests on a daily basis.

Paper Digest Team
team@paperdigest.org

TABLE 1: AAAI 2019 Papers

	Title	Authors	Highlight
1	Incorporating Behavioral Constraints in Online AI Systems	Avinash Balakrishnan, Djallel Bouneffouf, Nicholas Mattei, Francesca Rossi	To define this agent, we propose to adopt a novel extension to the classical contextual multi-armed bandit setting and we provide a new algorithm called Behavior Constrained Thompson Sampling (BCTS) that allows for online learning while obeying exogenous constraints.
2	Outlier Aware Network Embedding for Attributed Networks	Sambaran Bandyopadhyay, N. Lokesh, M. N. Murty	Towards this end, we propose an unsupervised outlier aware network embedding algorithm (ONE) for attributed networks, which minimizes the effect of the outlier nodes, and hence generates robust network embeddings.
3	Comparative Document Summarisation via Classification	Umanga Bista, Alexander Mathews, Minjeong Shin, Aditya Krishna Menon, Lexing Xie	In particular, by casting the problem as a binary classification amongst different groups, we derive objectives based on the notion of maximum mean discrepancy, as well as a simple yet effective gradient-based optimisation strategy.
4	ColNet: Embedding the Semantics of Web Tables for Column Type Prediction	Jiaoyan Chen, Ernesto Jiménez-Ruiz, Ian Horrocks, Charles Sutton	In this paper we propose a neural network based column type annotation framework named ColNet which is able to integrate KB reasoning and lookup with machine learning and can automatically train Convolutional Neural Networks for prediction.
5	Improving One-Class Collaborative Filtering via Ranking-Based Implicit Regularizer	Jin Chen, Defu Lian, Kai Zheng	In this paper, we propose a ranking-based implicit regularizer and provide a new general framework for OCCF, to avert the ground-truth ratings of unobserved samples.
6	Answer Identification from Product Reviews for User Questions by Multi-Task Attentive Networks	Long Chen, Ziyu Guan, Wei Zhao, Wanqing Zhao, Xiaopeng Wang, Zhou Zhao, Huan Sun	In this paper, we investigate how to provide a quick response to the asker by plausible answer identification from product reviews.
7	Dynamic Explainable Recommendation Based on Neural Attentive Models	Xu Chen, Yongfeng Zhang, Zheng Qin	With the desire to fill up this gap, in this paper, we build a novel Dynamic Explainable Recommender (called DER) for more accurate user modeling and explanations.
8	DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System	Zhi-Hong Deng, Ling Huang, Chang-Dong Wang, Jian-Huang Lai, Philip S. Yu	To this end, we propose a general framework named DeepCF, short for Deep Collaborative Filtering, to combine the strengths of the two types of methods and overcome such flaws.
9	TableSense: Spreadsheet Table Detection with Convolutional Neural Networks	Haoyu Dong, Shijie Liu, Shi Han, Zhouyu Fu, Dongmei Zhang	Considering the analogy between a cell matrix as spreadsheet and a pixel matrix as image, and encouraged by the successful application of Convolutional Neural Networks (CNN) in computer vision, we have developed TableSense, a novel end-to-end framework for spreadsheet table detection.
10	Triple Classification Using Regions and Fine-Grained Entity Typing	Tiansi Dong, Zhigang Wang, Juanzi Li, Christian Bauckhage, Armin B. Cremers	We propose a new region-based embedding approach using fine-grained type chains.
11	Dynamic Layer Aggregation for Neural Machine Translation with Routing-by-Agreement	Zi-Yi Dou, Zhaopeng Tu, Xing Wang, Longyue Wang, Shuming Shi, Tong Zhang	Inspired by recent progress on capsule networks, in this paper we propose to use routing-by-agreement strategies to aggregate layers dynamically.
12	Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems	Wenjing Fu, Zhaohui Peng, Senzhang Wang, Yang Xu, Jin Li	In this paper, we propose a Review and Content based Deep Fusion Model named RC-DFM for crossdomain recommendation.
13	Feature Sampling Based Unsupervised Semantic Clustering for Real Web Multi-View Content	Xiaolong Gong, Linpeng Huang, Fuwei Wang	In this paper, we propose a novel multi-view clustering algorithm based on the non-negative matrix factorization that attempts to use feature sampling strategy in order to reduce the complexity during the iteration process.
14	Cooperative Multimodal Approach to Depression Detection in Twitter	Tao Gui, Liang Zhu, Qi Zhang, Minlong Peng, Xu Zhou, Keyu Ding, Zhigang Chen	In this work, we propose the use of a novel cooperative multi-agent model to address these challenges.
15	Anchors Bring Ease: An Embarrassingly Simple Approach to Partial Multi-View Clustering	Jun Guo, Jiahui Ye	To address this issue, this paper proposes a simple yet effective Anchorbased Partial Multi-view Clustering (APMC) method, which utilizes anchors to reconstruct instance-to-instance relationships for clustering.
16	Y2Seq2Seq: Cross-Modal Representation Learning for 3D Shape and Text by Joint Reconstruction and Prediction of View and Word Sequences	Zhizhong Han, Mingyang Shang, Xiyang Wang, Yu-Shen Liu, Matthias Zwicker	To resolve this issue, we propose Y2Seq2Seq, a view-based model, to learn cross-modal representations by joint reconstruction and prediction of view and word sequences.
17	Learning to Align Question and Answer Utterances in Customer Service Conversation with Recurrent Pointer Networks	Shizhu He, Kang Liu, Weiting An	In this work, we propose end-to-end models for aligning question (Q) and answer (A) utterances in CS conversation with recurrent pointer networks (RPN). We construct a dataset from an in-house online CS.
18	Exploiting Background Knowledge in Compact Answer Generation for Why-Questions	Ryu Iida, Canasai Kruengkrai, Ryo Ishida, Kentaro Torisawa, Jong-Hoon Oh, Julien Kloetzer	This paper proposes a novel method for generating compact answers to open-domain why-questions, such as the following answer, “Because deep learning technologies were introduced,” to the question, “Why did Google’s machine translation service improve so drastically?”
19	Graph Convolutional Networks Meet Markov Random Fields: Semi-Supervised Community Detection in Attribute Networks	Di Jin, Ziyang Liu, Weihao Li, Dongxiao He, Weixiong Zhang	Here, we propose to integrate the techniques of GCN and MRF to solve the problem of semi-supervised community detection in attributed networks with semantic information.
20	Incorporating Network Embedding into Markov Random Field for Better Community Detection	Di Jin, Xinxin You, Weihao Li, Dongxiao He, Peng Cui, Françoise Fogelman-Soulié, Tanmoy Chakraborty	To address this problem, we propose a general Markov Random Field (MRF) framework to incorporate coupling in network embedding which allows better detecting network communities.
21	Crawling the Community Structure of Multiplex Networks	Ricky Laishram, Jeremy D. Wendt, Sucheta Soundarajan	We propose MultiComSample (MCS), a novel algorithm for crawling a multiplex network.
22	Coupled CycleGAN: Unsupervised Hashing Network for Cross-Modal Retrieval	Chao Li, Cheng Deng, Lei Wang, De Xie, Xianglong Liu	In this paper, we proposed Unsupervised coupled Cycle generative adversarial Hashing networks (UCH), for cross-modal retrieval, where outer-cycle network is used to learn powerful common representation, and inner-cycle network is explained to generate reliable hash codes.
23	Supervised User Ranking in Signed Social Networks	Xiaoming Li, Hui Fang, Jie Zhang	To address these two issues, we propose a supervised method based on random walk to learn social strengths between each user and her neighbors, in which the random walk more likely visits “potential friends” and less likely visits “potential enemies”.
24	Personalized Question Routing via Heterogeneous Network Embedding	Zeyu Li, Jyun-Yu Jiang, Yizhou Sun, Wei Wang	To tackle these challenges, we propose NeRank that (1) jointly learns representations of question content, question raiser, and question answerers by a heterogeneous information network embedding algorithm and a long short-term memory (LSTM) model.
25	Popularity Prediction on Online Articles with Deep Fusion of Temporal Process and Content Features	Dongliang Liao, Jin Xu, Gongfu Li, Weijie Huang, Weiqing Liu, Jing Li	In this paper, we propose a Deep Fusion of Temporal process and Content features (DFTC) method to tackle them.
26	Discrete Social Recommendation	Chenghao Liu, Xin Wang, Tao Lu, Wenwu Zhu, Jianling Sun, Steven C. H. Hoi	To address these issues, this work proposes a novel discrete social recommendation (DSR) method which learns binary codes in a unified framework for users and items, considering social information.
27	SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning	Jiaqi Ma, Zhe Zhao, Jilin Chen, Ang Li, Lichan Hong, Ed H. Chi	In this work, we propose a novel framework called SubNetwork Routing (SNR) to achieve more flexible parameter sharing while maintaining the computational advantage of the classic multi-task neural-network model.
28	DTMT: A Novel Deep Transition Architecture for Neural Machine Translation	Fandong Meng, Jinchao Zhang	This model enhances the hidden-to-hidden transition with multiple non-linear transformations, as well as maintains a linear transformation path throughout this deep transition by the well-designed linear transformation mechanism to alleviate the gradient vanishing problem.
29	Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search	Jinfeng Rao, Wei Yang, Yuhao Zhang, Ferhan Ture, Jimmy Lin	This paper proposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network), a novel neural ranking model specifically designed for ranking short social media posts.
30	Unsupervised Neural Machine Translation with SMT as Posterior Regularization	Shuo Ren, Zhirui Zhang, Shujie Liu, Ming Zhou, Shuai Ma	To address this issue, we introduce phrase based Statistic Machine Translation (SMT) models which are robust to noisy data, as posterior regularizations to guide the training of unsupervised NMT models in the iterative back-translation process.
31	Mining Entity Synonyms with Efficient Neural Set Generation	Jiaming Shen, Ruiliang Lyu, Xiang Ren, Michelle Vanni, Brian Sadler, Jiawei Han	Here we propose a new framework, named SynSetMine, that efficiently generates entity synonym sets from a given vocabulary, using example sets from external knowledge bases as distant supervision.
32	Surveys without Questions: A Reinforcement Learning Approach	Atanu R Sinha, Deepali Jain, Nikhil Sheoran, Sopan Khosla, Reshmi Sasidharan	We introduce a new way to interpret values generated by the value function of RL, as proxy ratings.
33	ATP: Directed Graph Embedding with Asymmetric Transitivity Preservation	Jiankai Sun, Bortik Bandyopadhyay, Armin Bashizade, Jiongqian Liang, P. Sadayappan, Srinivasan Parthasarathy	The technique incorporates graph hierarchy and reachability information naturally by relying on a nonlinear transformation that operates on the core reachability and implicit hierarchy within such graphs.
34	Learning from Web Data Using Adversarial Discriminative Neural Networks for Fine-Grained Classification	Xiaoxiao Sun, Liyi Chen, Jufeng Yang	In this paper, we take a fundamentally different view and propose an adversarial discriminative loss to advocate representation coherence between standard and web data.
35	Meimei: An Efficient Probabilistic Approach for Semantically Annotating Tables	Kunihiro Takeoka, Masafumi Oyamada, Shinji Nakadai, Takeshi Okadome	This paper presents a novel approach for table data annotation that combines a latent probabilistic model with multilabel classifiers.
36	DeepTileBars: Visualizing Term Distribution for Neural Information Retrieval	Zhiwen Tang, Grace Hui Yang	Inspired by TileBars, a classical term distribution visualization method, in this paper, we propose a novel Neu-IR model that handles query-to-document matching at the subtopic and higher levels.
37	Entity Alignment between Knowledge Graphs Using Attribute Embeddings	Bayu Distiawan Trisedya, Jianzhong Qi, Rui Zhang	We propose to learn embeddings that can capture the similarity between entities in different knowledge graphs.
38	VistaNet: Visual Aspect Attention Network for Multimodal Sentiment Analysis	Quoc-Tuan Truong, Hady W. Lauw	In this work, we propose Visual Aspect Attention Network or VistaNet, leveraging both textual and visual components.
39	UGSD: User Generated Sentiment Dictionaries from Online Customer Reviews	Chun-Hsiang Wang, Kang-Chun Fan, Chuan-Ju Wang, Ming-Feng Tsai	Given these kinds of user reviews, this paper proposes UGSD, a representation learning framework for constructing domain-specific sentiment dictionaries from online customer reviews, in which we leverage the relationship between user-generated reviews and the ratings of the reviews to associate the reviewer sentiment with certain entities.
40	Community Detection in Social Networks Considering Topic Correlations	Yingkui Wang, Di Jin, Katarzyna Musial, Jianwu Dang	A plethora of models integrating topic model and network topologies have been proposed.
41	Community Focusing: Yet Another Query-Dependent Community Detection	Zhuo Wang, Weiping Wang, Chaokun Wang, Xiaoyan Gu, Bo Li, Dan Meng	As density is the major consideration of community search, most methods of community search often find a dense subgraph with many vertices far from the query nodes, which are not very related to the query nodes.
42	Multi-Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System	Hong Wen, Jing Zhang, Quan Lin, Keping Yang, Pipei Huang	In this paper, we tackle this problem by proposing multiLevel Deep Cascade Trees (ldcTree), which is a novel decision tree ensemble approach.
43	Session-Based Recommendation with Graph Neural Networks	Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan	To obtain accurate item embedding and take complex transitions of items into account, we propose a novel method, i.e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity.
44	CISI-net: Explicit Latent Content Inference and Imitated Style Rendering for Image Inpainting	Jing Xiao, Liang Liao, Qiegen Liu, Ruimin Hu	To avoid generating such incorrect content, in this paper, we propose a content inference and style imitation network (CISI-net), which explicitly separate the image data into content code and style code.
45	Structured and Sparse Annotations for Image Emotion Distribution Learning	Haitao Xiong, Hongfu Liu, Bineng Zhong, Yun Fu	Motivated by these observations, we present a convolutional neural network based framework called Structured and Sparse annotations for image emotion Distribution Learning (SSDL) to tackle two challenges.
46	Multi-Interactive Memory Network for Aspect Based Multimodal Sentiment Analysis	Nan Xu, Wenji Mao, Guandan Chen	As a fundamental task of sentiment analysis, aspect-level sentiment analysis aims to identify the sentiment polarity of a specific aspect in the context. We provide a new publicly available multimodal aspect-level sentiment dataset to evaluate our model, and the experimental results demonstrate the effectiveness of our proposed model for this new task.
47	Multi-View Information-Theoretic Co-Clustering for Co-Occurrence Data	Peng Xu, Zhaohong Deng, Kup-Sze Choi, Longbing Cao, Shitong Wang	To take advantage of two-sided clustering for the co-occurrences in the scene of multi-view clustering, a two-sided multi-view clustering method is proposed, i.e., multi-view information-theoretic co-clustering (MV-ITCC).
48	Context-Aware Self-Attention Networks	Baosong Yang, Jian Li, Derek F. Wong, Lidia S. Chao, Xing Wang, Zhaopeng Tu	In this work, we focus on improving self-attention networks through capturing the richness of context.
49	Adversarial Training for Community Question Answer Selection Based on Multi-Scale Matching	Xiao Yang, Madian Khabsa, Miaosen Wang, Wei Wang, Ahmed Hassan Awadallah, Daniel Kifer, C. Lee Giles	We frame this task as a binary (relevant/irrelevant) classification problem, and present an adversarial training framework to alleviate label imbalance issue.
50	TransNFCM: Translation-Based Neural Fashion Compatibility Modeling	Xun Yang, Yunshan Ma, Lizi Liao, Meng Wang, Tat-Seng Chua	Inspired by the recent advances in multirelational knowledge representation learning and deep neural networks, this paper proposes a novel Translation-based Neural Fashion Compatibility Modeling (TransNFCM) framework, which jointly optimizes fashion item embeddings and category-specific complementary relations in a unified space via an end-to-end learning manner.
51	Data Augmentation Based on Adversarial Autoencoder Handling Imbalance for Learning to Rank	Qian Yu, Wai Lam	We propose a data generation model based on Adversarial Autoencoder (AAE) for tackling the data imbalance in LTR via informative data augmentation.
52	Cross-Relation Cross-Bag Attention for Distantly-Supervised Relation Extraction	Yujin Yuan, Liyuan Liu, Siliang Tang, Zhongfei Zhang, Yueting Zhuang, Shiliang Pu, Fei Wu, Xiang Ren	In this paper, we propose to conduct multi-instance learning with a novel Cross-relation Cross-bag Selective Attention (C2SA), which leads to noise-robust training for distant supervised relation extractor.
53	Text Assisted Insight Ranking Using Context-Aware Memory Network	Qi Zeng, Liangchen Luo, Wenhao Huang, Yang Tang	In this paper, we propose an insight ranking model that consists of two parts: A neural ranking model explores the data characteristics, such as the header semantics and the data statistical features, and a memory network model introduces table structure and context information into the ranking process. We also build a dataset with text assistance.
54	Hierarchical Reinforcement Learning for Course Recommendation in MOOCs	Jing Zhang, Bowen Hao, Bo Chen, Cuiping Li, Hong Chen, Jimeng Sun	To address such a challenge, we propose a hierarchical reinforcement learning algorithm to revise the user profiles and tune the course recommendation model on the revised profiles.
55	Regularizing Neural Machine Translation by Target-Bidirectional Agreement	Zhirui Zhang, Shuangzhi Wu, Shujie Liu, Mu Li, Ming Zhou, Tong Xu	To address this issue, we propose a novel model regularization method for NMT training, which aims to improve the agreement between translations generated by left-to-right (L2R) and right-to-left (R2L) NMT decoders.
56	Addressing the Under-Translation Problem from the Entropy Perspective	Yang Zhao, Jiajun Zhang, Chengqing Zong, Zhongjun He, Hua Wu	In this paper, we focus on the under-translation problem and attempt to find out what kinds of source words are more likely to be ignored.
57	Robust Online Matching with User Arrival Distribution Drift	Yu-Hang Zhou, Chen Liang, Nan Li, Cheng Yang, Shenghuo Zhu, Rong Jin	In this paper, we consider a novel user arrival model where users are drawn from drifting distribution, which is a hybrid case between the adversarial and stochastic model, and propose a new approach RDLA to deal with such assumption.
58	Predicting Hurricane Trajectories Using a Recurrent Neural Network	Sheila Alemany, Jonathan Beltran, Adrian Perez, Sam Ganzfried	We propose the application of a fully connected RNN to predict the trajectory of hurricanes.
59	Automatic Detection and Compression for Passive Acoustic Monitoring of the African Forest Elephant	Johan Bjorck, Brendan H. Rappazzo, Di Chen, Richard Bernstein, Peter H. Wrege, Carla P. Gomes	In this work, we consider applying machine learning to the analysis and compression of audio signals in the context of monitoring elephants in sub-Saharan Africa. In collaboration with conservation efforts, we construct a large labeled dataset of passive acoustic recordings of the African Forest Elephant via crowdsourcing, compromising thousands of hours of recordings in the wild.
60	Gated Residual Recurrent Graph Neural Networks for Traffic Prediction	Cen Chen, Kenli Li, Sin G. Teo, Xiaofeng Zou, Kang Wang, Jie Wang, Zeng Zeng	In the literature, many research works have applied deep learning methods on traffic prediction problems combining convolutional neural networks (CNNs) with recurrent neural networks (RNNs), which CNNs are utilized for spatial dependency and RNNs for temporal dynamics.
61	Bias Reduction via End-to-End Shift Learning: Application to Citizen Science	Di Chen, Carla P. Gomes	We propose the Shift Compensation Network (SCN), an end-to-end learning scheme which learns the shift from the scientific objectives to the biased data while compensating for the shift by re-weighting the training data.
62	Coverage Centrality Maximization in Undirected Networks	Gianlorenzo D’Angelo, Martin Olsen, Lorenzo Severini	In this paper we study the centrality maximization problem in undirected networks for one of the most important shortestpath based centrality measures, the coverage centrality.
63	Bayesian Fairness	Christos Dimitrakakis, Yang Liu, David C. Parkes, Goran Radanovic	We argue that recent notions of fairness in machine learning need to explicitly incorporate parameter uncertainty, hence we introduce the notion of Bayesian fairness as a suitable candidate for fair decision rules.
64	Understanding Dropouts in MOOCs	Wenzheng Feng, Jie Tang, Tracy Xiao Liu	In this paper, employing a dataset from XuetangX1, one of the largest MOOCs in China, we conduct a systematical study for the dropout problem in MOOCs.
65	Blameworthiness in Multi-Agent Settings	Meir Friedenberg, Joseph Y. Halpern	We provide a formal definition of blameworthiness in settings where multiple agents can collaborate to avoid a negative outcome.
66	Optimal Surveillance of Covert Networks by Minimizing Inverse Geodesic Length	Serge Gaspers, Kamran Najeebullah	In this paper, we design algorithms and study the complexity of MINIGL-ED.
67	Resisting Adversarial Attacks Using Gaussian Mixture Variational Autoencoders	Partha Ghosh, Arpan Losalka, Michael J. Black	In this work, we show how one can defend against them both under a unified framework.
68	Migration as Submodular Optimization	Paul Gölz, Ariel D. Procaccia	Specifically, we cast our problem as the maximization of an approximately submodular function subject to matroid constraints, and prove that the worst-case guarantees given by the classic greedy algorithm extend to this setting.
69	PGANs: Personalized Generative Adversarial Networks for ECG Synthesis to Improve Patient-Specific Deep ECG Classification	Tomer Golany, Kira Radinsky	In this work, we propose a semisupervised approach for patient-specific ECG classification.
70	Selecting Compliant Agents for Opt-in Micro-Tolling	Josiah P. Hanna, Guni Sharon, Stephen D. Boyles, Peter Stone	Since previous work suggests this problem is NP-hard, we examine a heuristic approach.
71	HireNet: A Hierarchical Attention Model for the Automatic Analysis of Asynchronous Video Job Interviews	Léo Hemamou, Ghazi Felhi, Vincent Vandenbussche, Jean-Claude Martin, Chloé Clavel	We propose a new hierarchical attention model called HireNet that aims at predicting the hirability of the candidates as evaluated by recruiters. As part of a project to help recruiters, we collected a corpus of more than 7000 candidates having asynchronous video job interviews for real positions and recording videos of themselves answering a set of questions.
72	Learning Diffusions without Timestamps	Hao Huang, Qian Yan, Ting Gan, Di Niu, Wei Lu, Yunjun Gao	In this work, we study how to carry out diffusion network inference without infection timestamps, using only the final infection statuses of nodes in each historical diffusion process, which are more readily accessible in practice.
73	CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison	Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, Jayne Seekins, David A. Mong, Safwan S. Halabi, Jesse K. Sandberg, Ricky Jones, David B. Larson, Curtis P. Langlotz, Bhavik N. Patel, Matthew P. Lungren, Andrew Y. Ng	We present CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 patients. We release the dataset to the public as a standard benchmark to evaluate performance of chest radiograph interpretation models.
74	Evolutionarily Learning Multi-Aspect Interactions and Influences from Network Structure and Node Content	Songlei Jian, Liang Hu, Longbing Cao, Kai Lu, Hang Gao	Here, we propose a multi-aspect interaction and influence-unified evolutionary coupled system (MAI-ECS) for network representation by involving node content and linkage-based network structure.
75	Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty	Muhammad Raza Khan, Joshua E. Blumenstock	In this paper, we develop a graph-based convolutional network for learning on multi-view networks.
76	Algorithms for Estimating Trends in Global Temperature Volatility	Arash Khodadadi, Daniel J. McDonald	In this paper, we develop methodology for estimating such trends using multi-resolution climate data from polar orbiting weather satellites.
77	Allocating Interventions Based on Predicted Outcomes: A Case Study on Homelessness Services	Amanda Kube, Sanmay Das, Patrick J. Fowler	Using data from the homeless system, we use a counterfactual approach to show potential for substantial benefits in terms of reducing the number of families who experience repeat episodes of homelessness by choosing optimal allocations (based on predicted outcomes) to a fixed number of beds in different types of homelessness service facilities.
78	ReAl-LiFE: Accelerating the Discovery of Individualized Brain Connectomes on GPUs	Sawan Kumar, Varsha Sreenivasan, Partha Talukdar, Franco Pestilli, Devarajan Sridharan	We propose our GPU-accelerated approach as a widely relevant tool for non-negative least squares optimization, across many domains.
79	Latent Dirichlet Allocation for Internet Price War	Chenchen Li, Xiang Yan, Xiaotie Deng, Yuan Qi, Wei Chu, Le Song, Junlong Qiao, Jianshan He, Junwu Xiong	We formalize the problem as a stochastic game with imperfect and incomplete information and develop a variant of Latent Dirichlet Allocation (LDA) to infer latent variables under the current market environment, which represents preferences of customers and strategies of competitors.
80	Deep Hierarchical Graph Convolution for Election Prediction from Geospatial Census Data	Mike Li, Elija Perrier, Chang Xu	In this paper, we demonstrate the utility of GCNNs for GIS analysis via a multi-graph hierarchical spatial-filter GCNN network model in the context of GIS systems to predict election outcomes using socio-economic features drawn from the 2016 Australian Census.
81	Who Blames Whom in a Crisis? Detecting Blame Ties from News Articles Using Neural Networks	Shuailong Liang, Olivia Nicol, Yue Zhang	In this study, we define a new task, Blame Tie Extraction, and construct a new dataset related to the United States financial crisis (20072010) from The New York Times, The Wall Street Journal and USA Today.
82	Convex Formulations for Fair Principal Component Analysis	Matt Olfat, Anil Aswani	These formulations are semidefinite programs, and we demonstrate their effectiveness using several datasets.
83	Violence Rating Prediction from Movie Scripts	Victor R. Martinez, Krishna Somandepalli, Karan Singla, Anil Ramakrishna, Yalda T. Uhls, Shrikanth Narayanan	In this work, we propose to characterize aspects of violent content in movies solely from the language used in the scripts.
84	Image Aesthetic Assessment Assisted by Attributes through Adversarial Learning	Bowen Pan, Shangfei Wang, Qisheng Jiang	In this paper, we propose a novel image aesthetic assessment assisted by attributes through both representation-level and label-level.
85	A Model-Free Affective Reinforcement Learning Approach to Personalization of an Autonomous Social Robot Companion for Early Literacy Education	Hae Won Park, Ishaan Grover, Samuel Spaulding, Louis Gomez, Cynthia Breazeal	We present an innovative personalized social robot learning companion system that utilizes children’s verbal and nonverbal affective cues to modulate their engagement and maximize their long-term learning gains.
86	Emergency Department Online Patient-Caregiver Scheduling	Hanan Rosemarin, Ariel Rosenfeld, Sarit Kraus	In this paper, we propose a novel online deep-learning scheduling approach for the automatic assignment and scheduling of medical personnel to arriving patients.
87	Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery	Tim G. J. Rudner, Marc Rußwurm, Jakub Fil, Ramona Pelich, Benjamin Bischke, Veronika Kopačková, Piotr Biliński	We propose a novel approach for rapid segmentation of flooded buildings by fusing multiresolution, multisensor, and multitemporal satellite imagery in a convolutional neural network. We release the first open-source dataset of fully preprocessed and labeled multiresolution, multispectral, and multitemporal satellite images of disaster sites along with our source code.
88	Learning to Address Health Inequality in the United States with a Bayesian Decision Network	Tavpritesh Sethi, Anant Mittal, Shubham Maheshwari, Samarth Chugh	In this work, we reveal actionable interventions for decreasing the longevitygap in the United States by analyzing a County-level data resource containing healthcare, socio-economic, behavioral, education and demographic features.
89	Axiomatic Characterization of Data-Driven Influence Measures for Classification	Jakub Sliwinski, Martin Strobel, Yair Zick	We study the following problem: given a labeled dataset and a specific datapoint ∼x, how did the i-th feature influence the classification for ∼x?
90	Hotels-50K: A Global Hotel Recognition Dataset	Abby Stylianou, Hong Xuan, Maya Shende, Jonathan Brandt, Richard Souvenir, Robert Pless	We present a baseline approach based on a standard network architecture and a collection of data-augmentation approaches tuned to this problem domain.
91	A Study of Educational Data Mining: Evidence from a Thai University	Ruangsak Trakunphutthirak, Yen Cheung, Vincent C. S. Lee	This study uses an additional data source from a university log file to predict academic performance.
92	AutoZOOM: Autoencoder-Based Zeroth Order Optimization Method for Attacking Black-Box Neural Networks	Chun-Chen Tu, Paishun Ting, Pin-Yu Chen, Sijia Liu, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh, Shin-Ming Cheng	To bridge this gap, we propose a generic framework for query-efficient blackbox attacks.
93	Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing	Jill-Jenn Vie, Hisashi Kashima	We here show that factorization machines (FMs), a model for regression or classification, encompasses several existing models in the educational literature as special cases, notably additive factor model, performance factor model, and multidimensional item response theory.
94	Forbidden Nodes Aware Community Search	Chaokun Wang, Junchao Zhu	In this paper, we argue that there are many real scenarios where some nodes are not allowed to appear in the community. Then, we introduce a new concept called forbidden nodes and present a new problem of forbidden nodes aware community search to describe these scenarios.
95	Bidirectional Inference Networks:A Class of Deep Bayesian Networks for Health Profiling	Hao Wang, Chengzhi Mao, Hao He, Mingmin Zhao, Tommi S. Jaakkola, Dina Katabi	To address these issues, we propose bidirectional inference networks (BIN), which stich together multiple probabilistic neural networks, each modeling a conditional dependency.
96	DeepETA: A Spatial-Temporal Sequential Neural Network Model for Estimating Time of Arrival in Package Delivery System	Fan Wu, Lixia Wu	This paper proposed a novel spatial-temporal sequential neural network model (DeepETA) to take fully advantages of the above factors.
97	Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference	Mike Wu, Milan Mosse, Noah Goodman, Chris Piech	In this paper, we introduce a human-in-the-loop “rubric sampling” approach to tackle the “zero shot” feedback challenge.
98	Detecting Incongruity between News Headline and Body Text via a Deep Hierarchical Encoder	Seunghyun Yoon, Kunwoo Park, Joongbo Shin, Hongjun Lim, Seungpil Won, Meeyoung Cha, Kyomin Jung	This research introduces million-scale pairs of news headline and body text dataset with incongruity label, which can uniquely be utilized for detecting news stories with misleading headlines.
99	EnsNet: Ensconce Text in the Wild	Shuaitao Zhang, Yuliang Liu, Lianwen Jin, Yaoxiong Huang, Songxuan Lai	A new method is proposed for removing text from natural images.
100	Weakly-Supervised Simultaneous Evidence Identification and Segmentation for Automated Glaucoma Diagnosis	Rongchang Zhao, Wangmin Liao, Beiji Zou, Zailiang Chen, Shuo Li	In this paper, we propose an innovative Weakly-Supervised Multi-Task Learning method (WSMTL) for accurate evidence identification, optic disc segmentation and automated glaucoma diagnosis.
101	A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization	Sendong Zhao, Ting Liu, Sicheng Zhao, Fei Wang	To exploit these benefits in a more sophisticated way, we propose a novel deep neural multi-task learning framework with explicit feedback strategies to jointly model recognition and normalization.
102	Exploiting Time-Series Image-to-Image Translation to Expand the Range of Wildlife Habitat Analysis	Ruobing Zheng, Ze Luo, Baoping Yan	In this paper, we innovatively exploit the image-to-image translation technology to expand the range of wildlife habitat analysis.
103	Adversarial Unsupervised Representation Learning for Activity Time-Series	Karan Aggarwal, Shafiq Joty, Luis Fernandez-Luque, Jaideep Srivastava	In this paper, we propose a novel unsupervised representation learning technique called activ-ity2vecthat learns and “summarizes” the discrete-valued ac-tivity time-series.
104	Beyond Speech: Generalizing D-Vectors for Biometric Verification	Jacob Baldwin, Ryan Burnham, Andrew Meyer, Robert Dora, Robert Wright	We present a comprehensive empirical analysis comparing our framework to the state-of-the-art in both domains.
105	Deep Latent Generative Models for Energy Disaggregation	Gissella Bejarano, David DeFazio, Arti Ramesh	In this work, we propose a deep latent generative model based on variational recurrent neural networks (VRNNs) for energy disaggregation.
106	Predicting Concrete and Abstract Entities in Modern Poetry	Fiammetta Caccavale, Anders Søgaard	This paper considers the problem of teaching a neural language model to select poetic entities, based on local context windows.
107	Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation	Cheng Chen, Qi Dou, Hao Chen, Jing Qin, Pheng-Ann Heng	This paper presents a novel unsupervised domain adaptation framework, called Synergistic Image and Feature Adaptation (SIFA), to effectively tackle the problem of domain shift.
108	Region-Based Message Exploration over Spatio-Temporal Data Streams	Lisi Chen, Shuo Shang	We develop a region-based message exploration mechanism that retrieve spatio-temporal message clusters from a stream of spatio-temporal messages based on users’ preferences on message topic and message spatial distribution.
109	Deriving Subgoals Autonomously to Accelerate Learning in Sparse Reward Domains	Michael Dann, Fabio Zambetta, John Thangarajah	In this work, we describe a new, autonomous approach for deriving subgoals from raw pixels that is more efficient than competing methods.
110	Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting	Zulong Diao, Xin Wang, Dafang Zhang, Yingru Liu, Kun Xie, Shaoyao He	To track the spatial dependencies among traffic data, we propose a dynamic spatio-temporal GCNN for accurate traffic forecasting.
111	Turbo Learning Framework for Human-Object Interactions Recognition and Human Pose Estimation	Wei Feng, Wentao Liu, Tong Li, Jing Peng, Chen Qian, Xiaolin Hu	In this paper, we propose a turbo learning framework to perform HOI recognition and pose estimation simultaneously.
112	Efficient Region Embedding with Multi-View Spatial Networks: A Perspective of Locality-Constrained Spatial Autocorrelations	Yanjie Fu, Pengyang Wang, Jiadi Du, Le Wu, Xiaolin Li	In this study, we investigate the problem of learning an embedding space for regions.
113	VidyutVanika: A Reinforcement Learning Based Broker Agent for a Power Trading Competition	Susobhan Ghosh, Easwar Subramanian, Sanjay P. Bhat, Sujit Gujar, Praveen Paruchuri	In this paper, we design an autonomous broker VidyutVanika, the runner-up in the 2018 Power TAC competition.
114	Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting	Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, Huaiyu Wan	In this paper, we propose a novel attention based spatial-temporal graph convolutional network (ASTGCN) model to solve traffic flow forecasting problem.
115	Deep Reinforcement Learning for Syntactic Error Repair in Student Programs	Rahul Gupta, Aditya Kanade, Shirish Shevade	Towards this, we design a novel programming language correction framework amenable to reinforcement learning.
116	Exploiting Sentence Embedding for Medical Question Answering	Yu Hao, Xien Liu, Ji Wu, Ping Lv	In this paper, we present a supervised learning framework to exploit sentence embedding for the medical question answering task.
117	Cash-Out User Detection Based on Attributed Heterogeneous Information Network with a Hierarchical Attention Mechanism	Binbin Hu, Zhiqiang Zhang, Chuan Shi, Jun Zhou, Xiaolong Li, Yuan Qi	Specifically, we model different types of objects and their rich attributes and interaction relations in the scenario of credit payment service with an Attributed Heterogeneous Information Network (AHIN).
118	Combo-Action: Training Agent For FPS Game with Auxiliary Tasks	Shiyu Huang, Hang Su, Jun Zhu, Ting Chen	In this paper, we explore a novel method which can plan on temporally-extended action sequences, which we refer as Combo-Action to compress the action space.
119	Connecting the Digital and Physical World: Improving the Robustness of Adversarial Attacks	Steve T.K. Jan, Joseph Messou, Yen-Chen Lin, Jia-Bin Huang, Gang Wang	In this work, we explore the feasibility of generating robust adversarial examples that remain effective in the physical domain.
120	A Memetic Approach for Sequential Security Games on a Plane with Moving Targets	Jan Karwowski, Jacek Mańdziuk, Adam Żychowski, Filip Grajek, Bo An	This paper introduces a new type of Security Games (SG) played on a plane with targets moving along predefined straight line trajectories and its respective Mixed Integer Linear Programming (MILP) formulation.
121	Crash to Not Crash: Learn to Identify Dangerous Vehicles Using a Simulator	Hoon Kim, Kangwook Lee, Gyeongjo Hwang, Changho Suh	In order to improve the quality of synthetic labels, we propose a new label adaptation technique that first extracts internal states of vehicles from the underlying driving simulator, and then refines labels by predicting future paths of vehicles based on a well-studied motion model.
122	Traffic Updates: Saying a Lot While Revealing a Little	John Krumm, Eric Horvitz	We show how to drastically reduce the number of transmissions in two ways, both based on a Markov random field for modeling traffic speed and flow.
123	Adversarial Learning for Weakly-Supervised Social Network Alignment	Chaozhuo Li, Senzhang Wang, Yukun Wang, Philip Yu, Yanbo Liang, Yun Liu, Zhoujun Li	We propose three models SNNAu, SNNAb and SNNAo to learn the projection function under the weakly-supervised adversarial learning framework.
124	Learning Heterogeneous Spatial-Temporal Representation for Bike-Sharing Demand Prediction	Youru Li, Zhenfeng Zhu, Deqiang Kong, Meixiang Xu, Yao Zhao	To address this issue, we proposed a novel model named STG2Vec to learn the representation from heterogeneous spatial-temporal graph.
125	SEGAN: Structure-Enhanced Generative Adversarial Network for Compressed Sensing MRI Reconstruction	Zhongnian Li, Tao Zhang, Peng Wan, Daoqiang Zhang	To tackle this problem, we propose the Structure-Enhanced GAN (SEGAN) that aims at restoring structure information at both local and global scale.
126	DeepSTN+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis	Ziqian Lin, Jie Feng, Ziyang Lu, Yong Li, Depeng Jin	In this paper, we propose DeepSTN+, a deep learning-based convolutional model, to predict crowd flows in the metropolis.
127	Perceptual-Sensitive GAN for Generating Adversarial Patches	Aishan Liu, Xianglong Liu, Jiaxin Fan, Yuqing Ma, Anlan Zhang, Huiyuan Xie, Dacheng Tao	To address this problem, this paper proposes a perceptual-sensitive generative adversarial network (PS-GAN) that can simultaneously enhance the visual fidelity and the attacking ability for the adversarial patch.
128	Joint Representation Learning for Multi-Modal Transportation Recommendation	Hao Liu, Ting Li, Renjun Hu, Yanjie Fu, Jingjing Gu, Hui Xiong	To this end, in this paper, we propose a joint representation learning framework for multi-modal transportation recommendation based on a carefully-constructed multi-modal transportation graph.
129	DeepFuzz: Automatic Generation of Syntax Valid C Programs for Fuzz Testing	Xiao Liu, Xiaoting Li, Rupesh Prajapati, Dinghao Wu	In this paper, we propose a grammarbased fuzzing tool called DEEPFUZZ.
130	Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective	Chengqiang Lu, Qi Liu, Chao Wang, Zhenya Huang, Peize Lin, Lixin He	In this paper, we propose a generalizable and transferable Multilevel Graph Convolutional neural Network (MGCN) for molecular property prediction.
131	Play as You Like: Timbre-Enhanced Multi-Modal Music Style Transfer	Chien-Yu Lu, Min-Xin Xue, Chia-Che Chang, Che-Rung Lee, Li Su	In this paper, we propose an unsupervised music style transfer method without the need for parallel data.
132	AffinityNet: Semi-Supervised Few-Shot Learning for Disease Type Prediction	Tianle Ma, Aidong Zhang	Here we present the Affinity Network Model (AffinityNet), a data efficient deep learning model that can learn from a limited number of training examples and generalize well.
133	Scalable Robust Kidney Exchange	Duncan C McElfresh, Hoda Bidkhori, John P Dickerson	We provide two scalable robust methods to handle two distinct types of uncertainty in kidney exchange—over the quality and the existence of a potential match.
134	Difficulty-Aware Attention Network with Confidence Learning for Medical Image Segmentation	Dong Nie, Li Wang, Lei Xiang, Sihang Zhou, Ehsan Adeli, Dinggang Shen	To address this challenge, we propose a difficulty-aware deep segmentation network with confidence learning for end-to-end segmentation.
135	Pathological Evidence Exploration in Deep Retinal Image Diagnosis	Yuhao Niu, Lin Gu, Feng Lu, Feifan Lv, Zongji Wang, Imari Sato, Zijian Zhang, Yangyan Xiao, Xunzhang Dai, Tingting Cheng	To visualize the symptom and feature encoded in this descriptor, we propose a GAN based method to synthesize pathological retinal image given the descriptor and a binary vessel segmentation.
136	Building Causal Graphs from Medical Literature and Electronic Medical Records	Galia Nordon, Gideon Koren, Varda Shalev, Benny Kimelfeld, Uri Shalit, Kira Radinsky	We present a novel approach for automatically constructing causal graphs between medical conditions.
137	NeVAE: A Deep Generative Model for Molecular Graphs	Bidisha Samanta, Abir DE, Gourhari Jana, Pratim Kumar Chattaraj, Niloy Ganguly, Manuel Gomez Rodriguez	In this paper, we propose NeVAE, a novel variational autoencoder for molecular graphs, whose encoder and decoder are specially designed to account for the above properties by means of several technical innovations.
138	PhoneMD: Learning to Diagnose Parkinson’s Disease from Smartphone Data	Patrick Schwab, Walter Karlen	Here, we present a machine-learning approach towards distinguishing between people with and without Parkinson’s disease using long-term data from smartphone-based walking, voice, tapping and memory tests.
139	GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination	Junyuan Shang, Cao Xiao, Tengfei Ma, Hongyan Li, Jimeng Sun	To fill this gap, we propose the Graph Augmented Memory Networks (GAMENet), which integrates the drug-drug interactions knowledge graph by a memory module implemented as a graph convolutional networks, and models longitudinal patient records as the query.
140	The Kelly Growth Optimal Portfolio with Ensemble Learning	Weiwei Shen, Bin Wang, Jian Pu, Jun Wang	In order to fill voids, especially grappling with the difficulty from small samples, in this paper, we propose a growth optimal portfolio strategy equipped with ensemble learning.
141	Spatiality Preservable Factored Poisson Regression for Large-Scale Fine-Grained GPS-Based Population Analysis	Masamichi Shimosaka, Yuta Hayakawa, Kota Tsubouchi	We propose herein a brand new population prediction model to capture the population trends in a fine-grained point of interest (POI) densely distributed over large areas and understand the relationship of each POI in terms of spatiality preservation.
142	Subtask Gated Networks for Non-Intrusive Load Monitoring	Changho Shin, Sunghwan Joo, Jaeryun Yim, Hyoseop Lee, Taesup Moon, Wonjong Rhee	In this work, we focus on the idea that appliances have on/off states, and develop a deep network for further performance improvements.
143	Improving Search with Supervised Learning in Trick-Based Card Games	Christopher Solinas, Douglas Rebstock, Michael Buro	In this paper, we focus on the effect of sampling on the strength of a player and propose a novel method of sampling more realistic states given move history.
144	Exploiting the Contagious Effect for Employee Turnover Prediction	Mingfei Teng, Hengshu Zhu, Chuanren Liu, Chen Zhu, Hui Xiong	To this end, in this paper, we propose a contagious effect heterogeneous neural network (CEHNN) for turnover prediction by integrating the employee profiles, the environmental factors, and more importantly, the influence of turnover behaviors of co-workers.
145	PerformanceNet: Score-to-Audio Music Generation with Multi-Band Convolutional Residual Network	Bryan Wang, Yi-Hsuan Yang	To build such an AI performer, we propose in this paper a deep convolutional model that learns in an end-to-end manner the score-to-audio mapping between a symbolic representation of music called the pianorolls and an audio representation of music called the spectrograms.
146	Differentially Private Empirical Risk Minimization with Smooth Non-Convex Loss Functions: A Non-Stationary View	Di Wang, Jinhui Xu	In this paper, we study the Differentially Private Empirical Risk Minimization (DP-ERM) problem with non-convex loss functions and give several upper bounds for the utility in different settings.
147	Private Model Compression via Knowledge Distillation	Ji Wang, Weidong Bao, Lichao Sun, Xiaomin Zhu, Bokai Cao, Philip S. Yu	What is worse, app service providers need to collect and utilize a large volume of users’ data, which contain sensitive information, to build the sophisticated DNN models.
148	Functional Connectivity Network Analysis with Discriminative Hub Detection for Brain Disease Identification	Mingliang Wang, Jiashuang Huang, Mingxia Liu, Daoqiang Zhang	To address these two issues, we propose a Connectivity Network analysis method with discriminative Hub Detection (CNHD) for brain disease diagnosis using functional magnetic resonance imaging (fMRI) data.
149	Hierarchical Macro Strategy Model for MOBA Game AI	Bin Wu	In this paper, we propose a novel learning-based Hierarchical Macro Strategy model for mastering MOBA games, a sub-genre of RTS games.
150	G2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification	Bingzhe Wu, Xiaolu Zhang, Shiwan Zhao, Lingxi Xie, Caihong Zeng, Zhihong Liu, Guangyu Sun	This paper provides an alternative solution for integrating multi-stained visual cues for glomerulus classification.
151	On Strength Adjustment for MCTS-Based Programs	I-Chen Wu, Ti-Rong Wu, An-Jen Liu, Hung Guei, Tinghan Wei	This paper proposes an approach to strength adjustment for MCTS-based game-playing programs.
152	A2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes	Kui Xu, Zhe Wang, Jianping Shi, Hongsheng Li, Qiangfeng Cliff Zhang	In this paper, we propose a learning-based method and formulate this problem as a vision-inspired 3D detection and pose estimation task.
153	TET-GAN: Text Effects Transfer via Stylization and Destylization	Shuai Yang, Jiaying Liu, Wenjing Wang, Zongming Guo	In this paper, we focus on the use of the powerful representation abilities of deep neural features for text effects transfer.
154	Learning Phenotypes and Dynamic Patient Representations via RNN Regularized Collective Non-Negative Tensor Factorization	Kejing Yin, Dong Qian, William K. Cheung, Benjamin C. M. Fung, Jonathan Poon	In this paper, we propose a novel Collective Non-negative Tensor Factorization (CNTF) model where each patient is represented by a temporal tensor, and all of the temporal tensors are factorized collectively with the phenotype definitions being shared across all patients.
155	MetaStyle: Three-Way Trade-off among Speed, Flexibility, and Quality in Neural Style Transfer	Chi Zhang, Yixin Zhu, Song-Chun Zhu	Motivated by this idea, we propose a novel method, coined MetaStyle, which formulates the neural style transfer as a bilevel optimization problem and combines learning with only a few post-processing update steps to adapt to a fast approximation model with satisfying artistic effects, comparable to the optimization-based methods for an arbitrary style.
156	Optimal Interdiction of Urban Criminals with the Aid of Real-Time Information	Youzhi Zhang, Qingyu Guo, Bo An, Long Tran-Thanh, Nicholas R. Jennings	To mitigate this loss, we propose a novel NEtwork purSuiT game (NEST) model that captures the interaction between an escaping adversary and a defender with multiple resources and real-time information available.
157	Incorporating Semantic Similarity with Geographic Correlation for Query-POI Relevance Learning	Ji Zhao, Dan Peng, Chuhan Wu, Huan Chen, Meiyu Yu, Wanji Zheng, Li Ma, Hua Chai, Jieping Ye, Xiaohu Qie	In this paper, we propose a novel Query-POI relevance model for effective POI retrieval for ondemand ride-hailing services.
158	SAFE: A Neural Survival Analysis Model for Fraud Early Detection	Panpan Zheng, Shuhan Yuan, Xintao Wu	In this paper, we propose a survival analysis based fraud early detection model, SAFE, which maps dynamic user activities to survival probabilities that are guaranteed to be monotonically decreasing along time.
159	One-Class Adversarial Nets for Fraud Detection	Panpan Zheng, Shuhan Yuan, Xintao Wu, Jun Li, Aidong Lu	In this paper, we develop one-class adversarial nets (OCAN) for fraud detection with only benign users as training data.
160	DeepDPM: Dynamic Population Mapping via Deep Neural Network	Zefang Zong, Jie Feng, Kechun Liu, Hongzhi Shi, Yong Li	In DeepDPM, we utilize super-resolution convolutional neural network(SRCNN) based model to directly map coarse data into higher resolution data, and a timeembedded long short-term memory model to effectively capture the periodicity nature to smooth the finer-scaled results from the previous static SRCNN model.
161	Cognitive Deficit of Deep Learning in Numerosity	Xiaolin Wu, Xi Zhang, Xiao Shu	A recurrent neural network capable of subitizing does exist, which we construct by encoding a mechanism of mathematical morphology into the CNN convolutional kernels.
162	Direct Training for Spiking Neural Networks: Faster, Larger, Better	Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, Yuan Xie, Luping Shi	We address this issue from two aspects: (1) We propose a neuron normalization technique to adjust the neural selectivity and develop a direct learning algorithm for deep SNNs.
163	TDSNN: From Deep Neural Networks to Deep Spike Neural Networks with Temporal-Coding	Lei Zhang, Shengyuan Zhou, Tian Zhi, Zidong Du, Yunji Chen	In this paper, we propose a novel method to convert DNNs to temporal-coding SNNs, called TDSNN.
164	MPD-AL: An Efficient Membrane Potential Driven Aggregate-Label Learning Algorithm for Spiking Neurons	Malu Zhang, Jibin Wu, Yansong Chua, Xiaoling Luo, Zihan Pan, Dan Liu, Haizhou Li	In order to address these limitations, we propose a novel membrane-potential driven aggregate-label learning algorithm, namely MPD-AL.
165	Human-Like Sketch Object Recognition via Analogical Learning	Kezhen Chen, Irina Rabkina, Matthew D. McLure, Kenneth D. Forbus	This paper introduces an approach that combines automatically constructed qualitative visual representations with analogical learning to tackle a hard computer vision problem, object recognition from sketches.
166	Attentive Tensor Product Learning	Qiuyuan Huang, Li Deng, Dapeng Wu, Chang Liu, Xiaodong He	This paper proposes a novel neural architecture — Attentive Tensor Product Learning (ATPL) — to represent grammatical structures of natural language in deep learning models.
167	Scalable Recollections for Continual Lifelong Learning	Matthew Riemer, Tim Klinger, Djallel Bouneffouf, Michele Franceschini	In this paper, we consider the problem of efficient and effective storage of experiences over very large time-frames.
168	Simulation-Based Approach to Efficient Commonsense Reasoning in Very Large Knowledge Bases	Abhishek Sharma, Keith M. Goolsbey	In this paper, we describe a search heuristic that uses a Monte-Carlo simulation technique to choose inference steps.
169	Modelling Autobiographical Memory Loss across Life Span	Di Wang, Ah-Hwee Tan, Chunyan Miao, Ahmed A. Moustafa	In this paper, we study how people generally lose their memories and emulate various memory loss phenomena using a neurocomputational autobiographical memory model. For model validation, we collect a memory dataset comprising more than one thousand life events and emulate the three key memory loss processes with model parameters learnt from memory recall behavioural patterns found in human subjects of different age groups.
170	Deep Bayesian Optimization on Attributed Graphs	Jiaxu Cui, Bo Yang, Xia Hu	To bridge the gap, in this paper, we propose a novel scalable Deep Graph Bayesian Optimization (DGBO) method on attributed graphs.
171	Interpretable Predictive Modeling for Climate Variables with Weighted Lasso	Sijie He, Xinyan Li, Vidyashankar Sivakumar, Arindam Banerjee	In this paper, we consider the problem of predicting monthly deseasonalized land temperature at different locations worldwide based on sea surface temperature (SST).
172	A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems	Ling Pan, Qingpeng Cai, Zhixuan Fang, Pingzhong Tang, Longbo Huang	In this paper, we propose a novel deep reinforcement learning framework for incentivizing users to rebalance such systems.
173	Deep Reinforcement Learning for Green Security Games with Real-Time Information	Yufei Wang, Zheyuan Ryan Shi, Lantao Yu, Yi Wu, Rohit Singh, Lucas Joppa, Fei Fang	Deep Reinforcement Learning for Green Security Games with Real-Time Information
174	A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data	Chuxu Zhang, Dongjin Song, Yuncong Chen, Xinyang Feng, Cristian Lumezanu, Wei Cheng, Jingchao Ni, Bo Zong, Haifeng Chen, Nitesh V. Chawla	In this paper, we propose a Multi-Scale Convolutional Recurrent Encoder-Decoder (MSCRED), to perform anomaly detection and diagnosis in multivariate time series data.
175	Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making	Sina Aghaei, Mohammad Javad Azizi, Phebe Vayanos	In this paper, we unify the definitions of unfairness across classification and regression.
176	Clairvoyant Restarts in Branch-and-Bound Search Using Online Tree-Size Estimation	Daniel Anderson, Gregor Hendel, Pierre Le Bodic, Merlin Viernickel	We propose a simple and general online method to measure the search progress within the Branch-and-Bound algorithm, from which we estimate the size of the remaining search tree.
177	A SAT+CAS Approach to Finding Good Matrices: New Examples and Counterexamples	Curtis Bright, Dragomir Ž. Ðoković, Ilias Kotsireas, Vijay Ganesh	Our method applies the SAT+CAS paradigm of combining computer algebra functionality with modern SAT solvers to efficiently search large spaces which are specified by both algebraic and logical constraints.
178	Improving Optimization Bounds Using Machine Learning: Decision Diagrams Meet Deep Reinforcement Learning	Quentin Cappart, Emmanuel Goutierre, David Bergman, Louis-Martin Rousseau	In this paper, we propose an innovative and generic approach based on deep reinforcement learning for obtaining an ordering for tightening the bounds obtained with relaxed and restricted DDs.
179	Model-Based Diagnosis of Hybrid Systems Using Satisfiability Modulo Theory	Alexander Diedrich, Alexander Maier, Oliver Niggemann	In this paper we present a novel model-based diagnosis approach for automatically diagnosing hybrid systems.
180	On Geometric Alignment in Low Doubling Dimension	Hu Ding, Mingquan Ye	In this paper, we propose an effective framework to compress the high dimensional geometric patterns and approximately preserve the alignment quality.
181	A Nonconvex Projection Method for Robust PCA	Aritra Dutta, Filip Hanzely, Peter Richtàrik	In this paper, we propose a nonconvex feasibility reformulation of RPCA problem and apply an alternating projection method to solve it.
182	Solving Integer Quadratic Programming via Explicit and Structural Restrictions	Eduard Eiben, Robert Ganian, Dusan Knop, Sebastian Ordyniak	We study the parameterized complexity of Integer Quadratic Programming under two kinds of restrictions: explicit restrictions on the domain or coefficients, and structural restrictions on variable interactions.
183	Stochastic Submodular Maximization with Performance-Dependent Item Costs	Takuro Fukunaga, Takuya Konishi, Sumio Fujita, Ken-ichi Kawarabayashi	We present an adaptive algorithm for this problem with a theoretical guaran-√ tee that its expected objective value is at least (1−1/ 4 e)/2 times the maximum value attained by any adaptive algorithms.
184	Constraint-Based Sequential Pattern Mining with Decision Diagrams	Amin Hosseininasab, Willem-Jan van Hoeve, Andre A. Cire	We introduce novel techniques for constraint-based sequential pattern mining that rely on a multi-valued decision diagram (MDD) representation of the database.
185	Faster Gradient-Free Proximal Stochastic Methods for Nonconvex Nonsmooth Optimization	Feihu Huang, Bin Gu, Zhouyuan Huo, Songcan Chen, Heng Huang	To fill this gap, in the paper, we propose a class of faster zeroth-order proximal stochastic methods with the variance reduction techniques of SVRG and SAGA, which are denoted as ZO-ProxSVRG and ZO-ProxSAGA, respectively.
186	Abduction-Based Explanations for Machine Learning Models	Alexey Ignatiev, Nina Narodytska, Joao Marques-Silva	This paper develops a constraint-agnostic solution for computing explanations for any ML model.
187	Separator-Based Pruned Dynamic Programming for Steiner Tree	Yoichi Iwata, Takuto Shigemura	In this paper, we present a novel separator-based pruning technique for speeding up a theoretically fast DP algorithm.
188	Asynchronous Delay-Aware Accelerated Proximal Coordinate Descent for Nonconvex Nonsmooth Problems	Ehsan Kazemi, Liqiang Wang	In this paper, we extend APCD method to the accelerated algorithm (AAPCD) for nonsmooth and nonconvex problems that satisfies the sufficient descent property, by comparing between the function values at proximal update and a linear extrapolated point using a delay-aware momentum value.
189	A Recursive Algorithm for Projected Model Counting	Jean-Marie Lagniez, Pierre Marquis	We present a recursive algorithm for projected model counting, i.e., the problem consisting in determining the number of models k∃X.Σk of a propositional formula Σ after eliminating from it a given set X of variables.
190	RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets	Liping Li, Wei Xu, Tianyi Chen, Georgios B. Giannakis, Qing Ling	In this paper, we propose a class of robust stochastic subgradient methods for distributed learning from heterogeneous datasets at presence of an unknown number of Byzantine workers.
191	Adaptive Proximal Average Based Variance Reducing Stochastic Methods for Optimization with Composite Regularization	Jingchang Liu, Linli Xu, Junliang Guo, Xin Sheng	In this paper, we propose two fast PA based VR stochastic methods – APA-SVRG and APA-SAGA.
192	Automatic Construction of Parallel Portfolios via Explicit Instance Grouping	Shengcai Liu, Ke Tang, Xin Yao	This paper investigates solving ACPP from this perspective, and especially studies how to obtain a good instance grouping.
193	On Sampling Complexity of the Semidefinite Affine Rank Feasibility Problem	Igor Molybog, Javad Lavaei	In this paper, we study the semidefinite affine rank feasibility problem, which consists in finding a positive semidefinite matrix of a given rank from its linear measurements.
194	Revisiting Projection-Free Optimization for Strongly Convex Constraint Sets	Jarrid Rector-Brooks, Jun-Kun Wang, Barzan Mozafari	We revisit the Frank-Wolfe (FW) optimization under strongly convex constraint sets.
195	A PSPACE Subclass of Dependency Quantified Boolean Formulas and Its Effective Solving	Christoph Scholl, Jie-Hong Roland Jiang, Ralf Wimmer, Aile Ge-Ernst	We show that this calculus is in fact incomplete for general DQBFs, but complete for a subclass of DQBFs, where any two existential variables have either identical or disjoint dependency sets over the universal variables.
196	BIRD: Engineering an Efficient CNF-XOR SAT Solver and Its Applications to Approximate Model Counting	Mate Soos, Kuldeep S. Meel	The primary contribution of this paper is an affirmative answer to the above question.
197	Algorithms for Average Regret Minimization	Sabine Storandt, Stefan Funke	In this paper, we study a problem from the realm of multicriteria decision making in which the goal is to select from a given set S of d-dimensional objects a minimum sized subset S0 with bounded regret.
198	Concurrency Debugging with MaxSMT	Miguel Terra-Neves, Nuno Machado, Ines Lynce, Vasco Manquinho	This paper proposes the use of MaxSMT for the generation of minimal reports for multi-threaded software with concurrency bugs.
199	Bayesian Functional Optimisation with Shape Prior	Pratibha Vellanki, Santu Rana, Sunil Gupta, David Rubin de Celis Leal, Alessandra Sutti, Murray Height, Svetha Venkatesh	We develop a novel Bayesian optimisation framework for such functional optimisation of expensive black-box processes.
200	Learning Optimal Classification Trees Using a Binary Linear Program Formulation	Sicco Verwer, Yingqian Zhang	In our new binary formulation, we aim to circumvent this problem by making the formulation size largely independent from the training data size.
201	Asynchronous Proximal Stochastic Gradient Algorithm for Composition Optimization Problems	Pengfei Wang, Risheng Liu, Nenggan Zheng, Zhefeng Gong	To address these challenges, we propose an asynchronous parallel algorithm, named Async-ProxSCVR, which effectively combines asynchronous parallel implementation and variance reduction method.
202	Low-Rank Semidefinite Programming for the MAX2SAT Problem	Po-Wei Wang, J. Zico Kolter	This paper proposes a new algorithm for solving MAX2SAT problems based on combining search methods with semidefinite programming approaches.
203	Task Embedded Coordinate Update: A Realizable Framework for Multivariate Non-Convex Optimization	Yiyang Wang, Risheng Liu, Long Ma, Xiaoliang Song	We in this paper propose a realizable framework TECU, which embeds task-specific strategies into update schemes of coordinate descent, for optimizing multivariate non-convex problems with coupled objective functions.
204	Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization	Bryan Wilder, Bistra Dilkina, Milind Tambe	We focus on combinatorial optimization problems and introduce a general framework for decision-focused learning, where the machine learning model is directly trained in conjunction with the optimization algorithm to produce highquality decisions.
205	Adding Constraints to Bayesian Inverse Problems	Jiacheng Wu, Jian-Xun Wang, Shawn C. Shadden	Thus, we propose an approach to improve parameter estimation in such inverse problems by incorporating constraints in a Bayesian inference framework.
206	Optimizing in the Dark: Learning an Optimal Solution through a Simple Request Interface	Qiao Xiang, Haitao Yu, James Aspnes, Franck Le, Linghe Kong, Y. Richard Yang	In this paper, we design BoxOpt, a novel system that leverages efficient oracle construction techniques in optimization and learning theory to automatically, and swiftly learn the optimal resource reservations without exchanging any private information between the network and the user.
207	Generalized Batch Normalization: Towards Accelerating Deep Neural Networks	Xiaoyong Yuan, Zheng Feng, Matthew Norton, Xiaolin Li	Utilizing recently introduced concepts from statistics and quantitative risk management, we present a general variant of Batch Normalization (BN) that offers accelerated convergence of Neural Network training compared to conventional BN.
208	Tackling Sparse Rewards in Real-Time Games with Statistical Forward Planning Methods	Raluca D. Gaina, Simon M. Lucas, Diego Pérez-Liébana	The work presented in this paper focuses on the latter case, which most agents struggle with.
209	Regular Boardgames	Jakub Kowalski, Maksymilian Mika, Jakub Sutowicz, Marek Szykuła	We propose a new General Game Playing (GGP) language called Regular Boardgames (RBG), which is based on the theory of regular languages.
210	3D Face Synthesis Driven by Personality Impression	Yining Lang, Wei Liang, Yujia Wang, Lap-Fai Yu	In this paper, we propose a novel approach to synthesize 3D faces based on personality impression for creating virtual characters.
211	Learning to Write Stories with Thematic Consistency and Wording Novelty	Juntao Li, Lidong Bing, Lisong Qiu, Dongmin Chen, Dongyan Zhao, Rui Yan	To fill this gap, we propose a cache-augmented conditional variational autoencoder for story generation, where the cache module allows to improve thematic consistency while the conditional variational autoencoder part is used for generating stories with less common words by using a continuous latent variable.
212	The Pure Price of Anarchy of Pool Block Withholding Attacks in Bitcoin Mining	Colleen Alkalay-Houlihan, Nisarg Shah	We study the game induced by one such attack – the pool block withholding attack – in which mining pools (groups of miners) attack other mining pools.
213	Fair Division with a Secretive Agent	Eshwar Ram Arunachaleswaran, Siddharth Barman, Nidhi Rathi	One of the main technical contributions of this paper is the development of novel connections between different fairdivision paradigms, e.g., we use our existential results for envy-free rent-division to develop an efficient EF1 algorithm.
214	Pareto Optimal Allocation under Compact Uncertain Preferences	Haris Aziz, Peter Biro, Ronald de Haan, Baharak Rastegari	In this paper, we focus on three uncertain preferences models whose size is polynomial in the number of agents and items.
215	On the Proximity of Markets with Integral Equilibria	Siddharth Barman, Sanath Kumar Krishnamurthy	In this work we show that one can always bypass this hurdle and, up to a bounded change in agents’ budgets, obtain markets that admit an integral equilibrium.
216	Unknown Agents in Friends Oriented Hedonic Games: Stability and Complexity	Nathanaël Barrot, Kazunori Ota, Yuko Sakurai, Makoto Yokoo	We study hedonic games under friends appreciation, where each agent considers other agents friends, enemies, or unknown agents.
217	Generalized Distance Bribery	Dorothea Baumeister, Tobias Hogrebe, Lisa Rey	We generalize this definition by introducing a bound on the distance between the original and the bribed votes.
218	From Recommendation Systems to Facility Location Games	Omer Ben-Porat, Gregory Goren, Itay Rosenberg, Moshe Tennenholtz	Motivated by modern applications, we propose the widely studied framework of facility location games to study recommendation systems with strategic content providers.
219	Convergence of Learning Dynamics in Information Retrieval Games	Omer Ben-Porat, Itay Rosenberg, Tennenholtz	We introduce the study of author learning dynamics in such contexts.
220	Low-Distortion Social Welfare Functions	Gerdus Benadè, Ariel D. Procaccia, Mingda Qiao	We propose to address this problem by assuming that voters’ utilities for rankings are induced by unknown weights and unknown utility functions, which, moreover, have a combinatorial (subadditive) structure.
221	On Rational Delegations in Liquid Democracy	Daan Bloembergen, Davide Grossi, Martin Lackner	We propose and study a game-theoretic model of liquid democracy to address the following question: when is it rational for a voter to delegate her vote?
222	Primarily about Primaries	Allan Borodin, Omer Lev, Nisarg Shah, Tyrone Strangway	We present a model to analyze such multi-stage elections, and conduct the first quantitative comparison (to the best of our knowledge) of the direct and primary voting systems with two political parties in terms of the quality of the elected candidate.
223	Walrasian Dynamics in Multi-Unit Markets	Simina Brânzei, Aris Filos-Ratsikas	We study the dynamics of (Walrasian) envy-free pricing mechanisms in this environment, showing that for any such pricing mechanism, the best response dynamic starting from truth-telling converges to a pure Nash equilibrium with small loss in revenue and welfare.
224	Fast Iterative Combinatorial Auctions via Bayesian Learning	Gianluca Brero, Sébastien Lahaie, Sven Seuken	In this paper, we generalize their work to settings with no restrictions on bidder valuations.
225	Solving Imperfect-Information Games via Discounted Regret Minimization	Noam Brown, Tuomas Sandholm	In this paper we introduce novel CFR variants that 1) discount regrets from earlier iterations in various ways (in some cases differently for positive and negative regrets), 2) reweight iterations in various ways to obtain the output strategies, 3) use a non-standard regret minimizer and/or 4) leverage “optimistic regret matching”.
226	Partial Verification as a Substitute for Money	Sofia Ceppi, Ian Kash, Rafael Frongillo	In this paper we develop tools to answer the following question.
227	Randomized Wagering Mechanisms	Yiling Chen, Yang Liu, Juntao Wang	In this paper, we expand the design space of wagering mechanisms to allow randomization and ask whether there are randomized wagering mechanisms that can achieve all previously considered desirable properties, including Pareto optimality.
228	Group Fairness for the Allocation of Indivisible Goods	Vincent Conitzer, Rupert Freeman, Nisarg Shah, Jennifer Wortman Vaughan	Thus, we introduce two “up to one good” style relaxations.
229	Solving Large Extensive-Form Games with Strategy Constraints	Trevor Davis, Kevin Waugh, Michael Bowling	In this work we introduce a generalized form of Counterfactual Regret Minimization that provably finds optimal strategies under any feasible set of convex constraints.
230	On the Complexity of the Inverse Semivalue Problem for Weighted Voting Games	Ilias Diakonikolas, Chrystalla Pavlou	In this work, we study the computational complexity of the inverse problem when the power index belongs to the class of semivalues.
231	Balancing Relevance and Diversity in Online Bipartite Matching via Submodularity	John P. Dickerson, Karthik Abinav Sankararaman, Aravind Srinivasan, Pan Xu	In this paper, we propose the Online Submodular Bipartite Matching (OSBM) problem, where the goal is to maximize a submodular function f over the set of matched edges.
232	Online Pandora’s Boxes and Bandits	Hossein Esfandiari, MohammadTaghi HajiAghayi, Brendan Lucier, Michael Mitzenmacher	We aim for approximation algorithms against adversaries that can choose the largest prize over any opened box, and use optimal offline policies to decide which boxes to open (without knowledge of the value inside)1.
233	Random Dictators with a Random Referee: Constant Sample Complexity Mechanisms for Social Choice	Brandon Fain, Ashish Goel, Kamesh Munagala, Nina Prabhu	Our primary contribution is the first social choice mechanism with constant sample complexity and constant Squared Distortion (which also implies constant Distortion).
234	Approximation and Hardness of Shift-Bribery	Piotr Faliszewski, Pasin Manurangsi, Krzysztof Sornat	We give the first polynomial-time approximation scheme for the case of positional scoring rules, and for the Copeland rule we show strong inapproximability results.
235	How Similar Are Two Elections?	Piotr Faliszewski, Piotr Skowron, Arkadii Slinko, Stanisław Szufa, Nimrod Talmon	We introduce the ELECTION ISOMORPHISM problem and a family of its approximate variants, which we refer to as dISOMORPHISM DISTANCE (d-ID) problems (where d is a metric between preference orders).
236	Online Convex Optimization for Sequential Decision Processes and Extensive-Form Games	Gabriele Farina, Christian Kroer, Tuomas Sandholm	In this work we derive a new framework for regret minimization on sequential decision problems and extensive-form games with general compact convex sets at each decision point and general convex losses, as opposed to prior work which has been for simplex decision points and linear losses.
237	An Improved Quasi-Polynomial Algorithm for Approximate Well-Supported Nash Equilibria	Michail Fasoulakis, Evangelos Markakis	We focus on the problem of computing approximate Nash equilibria in bimatrix games.
238	Very Hard Electoral Control Problems	Zack Fitzsimmons, Edith Hemaspaandra, Alexander Hoover, David E. Narváez	Thus for such systems control is not in NP, and in fact we show that it is typically complete for ∑p2 (i.e., NPNP, the second level of the polynomial hierarchy).
239	Fair Knapsack	Till Fluschnik, Piotr Skowron, Mervin Triphaus, Kai Wilker	This way we introduce the concepts of individually best, diverse, and fair knapsack.
240	A Bridge between Liquid and Social Welfare in Combinatorial Auctions with Submodular Bidders	Dimitris Fotakis, Kyriakos Lotidis, Chara Podimata	Our objective is to maximize the liquid welfare, a notion of efficiency for budgetconstrained bidders introduced by Dobzinski and Paes Leme (2014).
241	An Equivalence between Wagering and Fair-Division Mechanisms	Rupert Freeman, David M. Pennock, Jennifer Wortman Vaughan	We draw a surprising and direct mathematical equivalence between the class of allocation mechanisms for divisible goods studied in the context of fair division and the class of weakly budget-balanced wagering mechanisms designed for eliciting probabilities.
242	Fair and Efficient Memory Sharing: Confronting Free Riders	Eric J. Friedman, Vasilis Gkatzelis, Christos-Alexandros Psomas, Scott Shenker	In this paper we explore the power and limitations of truthful mechanisms in this setting.
243	Multi-Unit Bilateral Trade	Matthias Gerstgrasser, Paul W. Goldberg, Bart de Keijzer, Philip Lazos, Alexander Skopalik	We consider two classes of valuation functions for the buyer and seller: Valuations that are increasing in the number of units in possession, and the more specific class of valuations that are increasing and submodular.
244	On the Distortion Value of the Elections with Abstention	Mohammad Ghodsi, Mohamad Latifian, Masoud Seddighin	In this study, we wish to answer the following question: how does the distortion value change if we allow less motivated agents to abstain from the election?
245	Pareto Efficient Auctions with Interest Rates	Gagan Goel, Vahab Mirrokni, Renato Paes Leme	We consider auction settings in which agents have limited access to monetary resources but are able to make payments larger than their available resources by taking loans with a certain interest rate.
246	Deep Bayesian Trust: A Dominant and Fair Incentive Mechanism for Crowd	Naman Goel, Boi Faltings	We propose a novel mechanism that assigns gold tasks to only a few workers and exploits transitivity to derive accuracy of the rest of the workers from their peers’ accuracy.
247	You Get What You Share: Incentives for a Sharing Economy	Sreenivas Gollapudi, Kostas Kollias, Debmalya Panigrahi	In this paper, we study settings where a large population self-organizes into sharing groups.
248	Computing the Yolk in Spatial Voting Games without Computing Median Lines	Joachim Gudmundsson, Sampson Wong	We present near-linear time algorithms for computing the yolk in the plane.
249	On the Inducibility of Stackelberg Equilibrium for Security Games	Qingyu Guo, Jiarui Gan, Fei Fang, Long Tran-Thanh, Milind Tambe, Bo An	On the Inducibility of Stackelberg Equilibrium for Security Games
250	Solving Partially Observable Stochastic Games with Public Observations	Karel Horák, Branislav Bošanský	We propose such a subclass for two-player zero-sum games with discounted-sum objective function—POSGs with public observations (POPOSGs)—where each player is able to reconstruct beliefs of the other player over the unobserved states.
251	Object Reachability via Swaps along a Line	Sen Huang, Mingyu Xiao	We consider the problem whether an object is reachable for a given agent under a social network, where a trade between two agents is allowed if they are neighbors in the network and no participant has a deficit from the trade.
252	Pareto-Optimal Allocation of Indivisible Goods with Connectivity Constraints	Ayumi Igarashi, Dominik Peters	We study the problem of allocating indivisible items to agents with additive valuations, under the additional constraint that bundles must be connected in an underlying item graph.
253	Forming Probably Stable Communities with Limited Interactions	Ayumi Igarashi, Jakub Sliwinski, Yair Zick	We model this setting as a hedonic game, where players are connected by an underlying interaction network, and can only consider joining groups that are connected subgraphs of the underlying graph.
254	Approximate Inference of Outcomes in Probabilistic Elections	Batya Kenig, Benny Kimelfeld	We study the complexity of estimating the probability of an outcome in an election over probabilistic votes.
255	“Reverse Gerrymandering”: Manipulation in Multi-Group Decision Making	Omer Lev, Yoad Lewenberg	In this paper we explore an iterative dynamic in this setting, finding that allowing this decentralized system results, in some particular cases, in a stable equilibrium, though in general, the setting may end up in a cycle.
256	Heuristic Voting as Ordinal Dominance Strategies	Omer Lev, Reshef Meir, Svetlana Obraztsova, Maria Polukarov	To this end, we present a framework that allows for “shades of gray” of likelihood without probabilities.
257	Cooperation Enforcement and Collusion Resistance in Repeated Public Goods Games	Kai Li, Dong Hao	Here, we show that such strategies do exist.
258	Revenue Enhancement via Asymmetric Signaling in Interdependent-Value Auctions	Zhuoshu Li, Sanmay Das	We consider the problem of designing the information environment for revenue maximization in a sealed-bid second price auction with two bidders.
259	Dynamic Contracting under Positive Commitment	Ilan Lobel, Renato Paes Leme	We model this problem as a dynamic game where the seller chooses a mechanism at each period subject to a sequential rationality constraint, and characterize the perfect Bayesian equilibrium of this dynamic game.
260	When Do Envy-Free Allocations Exist?	Pasin Manurangsi, Warut Suksompong	We consider a fair division setting in which m indivisible items are to be allocated among n agents, where the agents have additive utilities and the agents’ utilities for individual items are independently sampled from a distribution.
261	Quasi-Perfect Stackelberg Equilibrium	Alberto Marchesi, Gabriele Farina, Christian Kroer, Nicola Gatti, Tuomas Sandholm	In this paper, we introduce the axiomatic definition of quasi-perfect Stackelberg equilibrium.
262	Optimal Dynamic Auctions Are Virtual Welfare Maximizers	Vahab Mirrokni, Renato Paes Leme, Pingzhong Tang, Song Zuo	In this paper, we show that any optimal dynamic auction is a virtual welfare maximizer subject to some monotone allocation constraints.
263	Deception in Finitely Repeated Security Games	Thanh H. Nguyen, Yongzhao Wang, Arunesh Sinha, Michael P. Wellman	We present a detailed computation and analysis of both players’ optimal strategies given the attacker may play deceptively.
264	Fairly Allocating Many Goods with Few Queries	Hoon Oh, Ariel D. Procaccia, Warut Suksompong	For two agents with arbitrary monotonic valuations, we design an algorithm that computes an allocation satisfying envy-freeness up to one good (EF1), a relaxation of envy-freeness, using a logarithmic number of queries.
265	Learning Optimal Strategies to Commit To	Binghui Peng, Weiran Shen, Pingzhong Tang, Song Zuo	In this paper, we study the problem of learning the optimal leader strategy in Stackelberg (security) games and develop novel algorithms as well as new hardness results.
266	Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games Using Baselines	Martin Schmid, Neil Burch, Marc Lanctot, Matej Moravcik, Rudolf Kadlec, Michael Bowling	In this paper, we introduce a variance reduction technique (VR-MCCFR) that applies to any sampling variant of MCCFR.
267	Mechanism Design for Multi-Type Housing Markets with Acceptable Bundles	Sujoy Sikdar, Sibel Adalı Lirong Xia	We extend the Top-Trading-Cycles (TTC) mechanism to select strict core allocations for housing markets with multiple types of items, where each agent may be endowed and allocated with multiple items of each type.
268	Learning Deviation Payoffs in Simulation-Based Games	Samuel Sokota, Caleb Ho, Bryce Wiedenbeck	We present a novel approach for identifying approximate role-symmetric Nash equilibria in large simulation-based games.
269	A Framework for Approval-Based Budgeting Methods	Nimrod Talmon, Piotr Faliszewski	We define and study a general framework for approval-based budgeting methods and compare certain methods within this framework by their axiomatic and computational properties.
270	Practical Algorithms for Multi-Stage Voting Rules with Parallel Universes Tiebreaking	Jun Wang, Sujoy Sikdar, Tyler Shepherd, Zhibing Zhao, Chunheng Jiang, Lirong Xia	We propose the first algorithms for computing the set of alternatives that are winners under some tiebreaking mechanism under STV and RP, which is also known as parallel-universes tiebreaking (PUT).
271	Random Walk Decay Centrality	Tomasz Wąs, Talal Rahwan, Oskar Skibski	We propose a new centrality measure, called the Random Walk Decay centrality.
272	Poll-Confident Voters in Iterative Voting	Anaëlle Wilczynski	We propose a descriptive model, inspired by political elections, where the information about the vote intentions of the electorate comes from public opinion polls and a social network, modeled as a graph over the voters.
273	Defending Elections against Malicious Spread of Misinformation	Bryan Wilder, Yevgeniy Vorobeychik	Nevertheless, we give provable polynomial-time approximation algorithms for computing the defender’s minimax optimal strategy across a range of settings, encompassing different population structures as well as models of the information available to each player.
274	A Unified Approach to Online Matching with Conflict-Aware Constraints	Pan Xu, Yexuan Shi, Hao Cheng, John Dickerson, Karthik Abinav Sankararaman, Aravind Srinivasan, Yongxin Tong, Leonidas Tsepenekas	In this paper, we propose a unifying model, generalizing the conflict models proposed in (She et al., TKDE 2016) and (Chen et al., TKDE 16).
275	A Better Algorithm for Societal Tradeoffs	Hanrui Zhang, Yu Cheng, Vincent Conitzer	In this paper, we present a significantly improved algorithm and evaluate it experimentally.
276	A PAC Framework for Aggregating Agents’ Judgments	Hanrui Zhang, Vincent Conitzer	We propose a formal learning-theoretic framework for this setting.
277	Preference-Aware Task Assignment in On-Demand Taxi Dispatching: An Online Stable Matching Approach	Boming Zhao, Pan Xu, Yexuan Shi, Yongxin Tong, Zimu Zhou, Yuxiang Zeng	To address this problem, we propose preference-aware task assignment using online stable matching.
278	Distributionally Adversarial Attack	Tianhang Zheng, Changyou Chen, Kui Ren	In this paper, we achieve the goal by proposing distributionally adversarial attack (DAA), a framework to solve an optimal adversarial-data distribution, a perturbed distribution that satisfies the L∞ constraint but deviates from the original data distribution to increase the generalization risk maximally.
279	A Two-Individual Based Evolutionary Algorithm for the Flexible Job Shop Scheduling Problem	Junwen Ding, Zhipeng Lü, Chu-Min Li, Liji Shen, Liping Xu, Fred Glover	In this paper, we propose an evolutionary algorithm using only two individuals, called master-apprentice evolutionary algorithm (MAE), for solving the flexible job shop scheduling problem (FJSP).
280	Greedy Maximization of Functions with Bounded Curvature under Partition Matroid Constraints	Tobias Friedrich, Andreas Göbel, Frank Neumann, Francesco Quinzan, Ralf Rothenberger	Overall, we present evidence to support the idea that, when dealing with constrained maximization problems with bounded curvature, one needs not search for (approximate) monotonicity to get good approximate solutions.
281	Heuristic Search Algorithm for Dimensionality Reduction Optimally Combining Feature Selection and Feature Extraction	Baokun He, Swair Shah, Crystal Maung, Gordon Arnold, Guihong Wan, Haim Schweitzer	We study a generalization that approximates the data with both selected and extracted features.
282	On the Optimal Efficiency of Cost-Algebraic A*	Robert C. Holte, Sandra Zilles	In this paper, we investigate cost-algebraic A’s optimal efficiency: in the cost-algebraic setting, under what conditions is A guaranteed to expand the fewest possible states?
283	Running Time Analysis of MOEA/D with Crossover on Discrete Optimization Problem	Zhengxin Huang, Yuren Zhou, Zefeng Chen, Xiaoyu He	In this paper, we present a running time analysis of a simple MOEA with crossover based on the MOEA/D framework (MOEA/D-C) on four discrete optimization problems.
284	Bézier Simplex Fitting: Describing Pareto Fronts of´ Simplicial Problems with Small Samples in Multi-Objective Optimization	Ken Kobayashi, Naoki Hamada, Akiyoshi Sannai, Akinori Tanaka, Kenichi Bannai, Masashi Sugiyama	To reduce the required sample size, this paper proposes a Bézier simplex model and its fitting algorithm.
285	Fine-Grained Search Space Classification for Hard Enumeration Variants of Subset Problems	Juho Lauri, Sourav Dutta	We propose a simple, powerful, and flexible machine learning framework for (i) reducing the search space of computationally difficult enumeration variants of subset problems and (ii) augmenting existing state-of-the-art solvers with informative cues arising from the input distribution.
286	On the Time Complexity of Algorithm Selection Hyper-Heuristics for Multimodal Optimisation	Andrei Lissovoi, Pietro S. Oliveto, John Alasdair Warwicker	In this paper we extend our understanding to the domain of multimodal optimisation by considering a hyper-heuristic from the literature that can switch between elitist and nonelitist heuristics during the run.
287	Evolving Action Abstractions for Real-Time Planning in Extensive-Form Games	Julian R. H. Mariño, Rubens O. Moraes, Claudio Toledo, Levi H. S. Lelis	In this paper we show that the problem of generating action abstractions can be cast as a problem of selecting a subset of pure strategies from a pool of options.
288	Real-Time Planning as Decision-Making under Uncertainty	Andrew Mitchell, Wheeler Ruml, Fabian Spaniol, Jorg Hoffmann, Marek Petrik	In this paper, we reconsider real-time planning as a problem of decision-making under uncertainty.
289	Evolving Solutions to Community-Structured Satisfiability Formulas	Frank Neumann, Andrew M. Sutton	We prove that when the formula clusters into communities of size s ∈ ω(logn) ∩O(nε/(2ε+2)) for some constant 0 <ε< 1, and there is a nonuniform distribution over communities, a simple evolutionary algorithm called the (1+1) EA finds a satisfying assignment in polynomial time on a 1−o(1) fraction of formulas with at least constant constraint density.
290	Pareto Optimization for Subset Selection with Dynamic Cost Constraints	Vahid Roostapour, Aneta Neumann, Frank Neumann, Tobias Friedrich	In this paper, we consider the subset selection problem for function f with constraint bound B which changes over time.
291	Stepping Stones to Inductive Synthesis of Low-Level Looping Programs	Christopher D. Rosin	We present MAKESPEARE, a simple delayed-acceptance hillclimbing method that synthesizes low-level looping programs from input/output examples.
292	Allocating Planning Effort When Actions Expire	Shahaf S. Shperberg, Andrew Coles, Bence Cserna, Erez Karpas, Wheeler Ruml, Solomon E. Shimony	This paper formalizes this metareasoning problem, studies its theoretical properties, and presents several algorithms for solving it.
293	Enriching Non-Parametric Bidirectional Search Algorithms	Shahaf S. Shperberg, Ariel Felner, Nathan R. Sturtevant, Solomon E. Shimony, Avi Hayoun	We introduce new variants of NBS that are aimed at finding all optimal solutions.
294	Bounded Suboptimal Search with Learned Heuristics for Multi-Agent Systems	Markus Spies, Marco Todescato, Hannes Becker, Patrick Kesper, Nicolai Waniek, Meng Guo	Therefore, we (i) propose a novel method that utilizes learned heuristics to guide Focal Search A∗, a variant of A∗ with guarantees on bounded suboptimality; (ii) compare the complexity and performance of jointly learning individual policies for multiple robots with an approach that learns one policy for all robots; (iii) thoroughly examine how learned policies generalize to previously unseen environments and demonstrate considerably improved performance in a simulated complex dynamic coverage problem.
295	An Improved Generic Bet-and-Run Strategy with Performance Prediction for Stochastic Local Search	Thomas Weise, Zijun Wu, Markus Wagner	We propose using more advanced methods to discriminate between “good” and “bad” sample runs with the goal of increasing the correlation of the chosen run with the a-posteriori best one.
296	Fuzzy-Classification Assisted Solution Preselection in Evolutionary Optimization	Aimin Zhou, Jinyuan Zhang, Jianyong Sun, Guixu Zhang	Facing this challenge, this paper proposes a fuzzy classification based preselection (FCPS) scheme, which utilizes the membership function to measure the quality of candidate solutions.
297	One-Network Adversarial Fairness	Tameem Adel, Isabel Valera, Zoubin Ghahramani, Adrian Weller	Given any existing differentiable classifier, we make only slight adjustments to the architecture including adding a new hidden layer, in order to enable the concurrent adversarial optimization for fairness and accuracy.
298	Making Money from What You Know – How to Sell Information?	Shani Alkoby, Zihe Wang, David Sarne, Pingzhong Tang	In this paper, we consider the common problem of a strategic information provider offering prospective buyers information which can disambiguate uncertainties the buyers have, which can be valuable for their decision making.
299	Updates in Human-AI Teams: Understanding and Addressing the Performance/Compatibility Tradeoff	Gagan Bansal, Besmira Nushi, Ece Kamar, Daniel S. Weld, Walter S. Lasecki, Eric Horvitz	We propose a re-training objective to improve the compatibility of an update by penalizing new errors.
300	Human-in-the-Loop Feature Selection	Alvaro H. C. Correia, Freddy Lecue	We present a human-in-the-loop framework that interacts with domain experts by collecting their feedback regarding the variables (of few samples) they evaluate as the most relevant for the task at hand.
301	Verifying Robustness of Gradient Boosted Models	Gil Einziger, Maayan Goldstein, Yaniv Sa’ar, Itai Segall	This work introduces VERIGB, a tool for quantifying the robustness of gradient boosted models.
302	Counterfactual Randomization: Rescuing Experimental Studies from Obscured Confounding	Andrew Forney, Elias Bareinboim	Our paper presents a novel experimental design that can be noninvasively layered atop past and future RCTs to not only expose the presence of UCs in a system, but also reveal patient- and practitioner-specific treatment effects in order to improve decision-making.
303	Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-Time	Vinicius G. Goecks, Gregory M. Gremillion, Vernon J. Lawhern, John Valasek, Nicholas R. Waytowich	We demonstrate this method in an autonomous perching task using a quadrotor with continuous roll, pitch, yaw, and throttle commands and imagery captured from a downward-facing camera in a high-fidelity simulated environment.
304	Task Transfer by Preference-Based Cost Learning	Mingxuan Jing, Xiaojian Ma, Wenbing Huang, Fuchun Sun, Huaping Liu	In this paper, we relax these two strong conditions by developing a novel task transfer framework where the expert preference is applied as a guidance.
305	A Unified Framework for Planning in Adversarial and Cooperative Environments	Anagha Kulkarni, Siddharth Srivastava, Subbarao Kambhampati	By slightly varying our framework, we present an approach for producing legible plans in cooperative settings such that the observation sequence projected by the plan is consistent with at most j goals from a set of confounding goals.
306	RGBD Based Gaze Estimation via Multi-Task CNN	Dongze Lian, Ziheng Zhang, Weixin Luo, Lina Hu, Minye Wu, Zechao Li, Jingyi Yu, Shenghua Gao	Thus we propose a CNN-based multi-task learning framework to simultaneously refine depth images and predict gaze points.
307	Deep Neural Networks Constrained by Decision Rules	Yuzuru Okajima, Kunihiko Sadamasa	In this paper, to balance the accuracy of neural networks and the interpretability of decision rules, we propose a hybrid technique called rule-constrained networks, namely, neural networks that make decisions by selecting decision rules from a given ruleset.
308	Geometry-Aware Face Completion and Editing	Linsen Song, Jie Cao, Lingxiao Song, Yibo Hu, Ran He	This paper proposes a geometry-aware Face Completion and Editing NETwork (FCENet) by systematically studying facial geometry from the unmasked region.
309	Generation of Policy-Level Explanations for Reinforcement Learning	Nicholay Topin, Manuela Veloso	To address this need, we introduce Abstracted Policy Graphs, which are Markov chains of abstract states.
310	Learning Models of Sequential Decision-Making with Partial Specification of Agent Behavior	Vaibhav V. Unhelkar, Julie A. Shah	To achieve better model alignment, we provide a novel approach capable of learning aligned models that conform to partial knowledge of the agent’s behavior.
311	Augmenting Markov Decision Processes with Advising	Loïs Vanhée, Laurent Jeanpierre, Abdel-Illah Mouaddib	This paper introduces Advice-MDPs, an expansion of Markov Decision Processes for generating policies that take into consideration advising on the desirability, undesirability, and prohibition of certain states and actions.
312	FLEX: Faithful Linguistic Explanations for Neural Net Based Model Decisions	Sandareka Wickramanayake, Wynne Hsu, Mong Li Lee	In this work, we propose a framework called FLEX (Faithful Linguistic EXplanations) that generates post-hoc linguistic justifications to rationalize the decision of a Convolutional Neural Network.
313	Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning	Ziyu Yao, Xiujun Li, Jianfeng Gao, Brian Sadler, Huan Sun	In this paper, we investigate interactive semantic parsing, where the agent can ask the user clarification questions to resolve ambiguities via a multi-turn dialogue, on an important type of programs called “If-Then recipes.”
314	Deep Transformation Method for Discriminant Analysis of Multi-Channel Resting State fMRI	Abhay M S Aradhya, Aditya Joglekar, Sundaram Suresh, M. Pratama	In this paper, we propose a new Deep Transformation Method (DTM) that extracts the discriminant latent feature space from rsfMRI and projects it in the subsequent layer for classification of rs-fMRI data.
315	AI-Sketcher : A Deep Generative Model for Producing High-Quality Sketches	Nan Cao, Xin Yan, Yang Shi, Chaoran Chen	To address these issues, we introduced AI-Sketcher, a deep generative model for generating high-quality multiclass sketches.
316	Election with Bribed Voter Uncertainty: Hardness and Approximation Algorithm	Lin Chen, Lei Xu, Shouhuai Xu, Zhimin Gao, Weidong Shi	In this paper, we introduce a novel variant of the bribery problem, “Election with Bribed Voter Uncertainty” or BVU for short, accommodating the uncertainty that the vote of a bribed voter may or may not be counted.
317	Human Motion Prediction via Learning Local Structure Representations and Temporal Dependencies	Xiao Guo, Jongmoo Choi	In this paper, we argue local representations on different body components should be learned separately and, based on such idea, propose a network, Skeleton Network (SkelNet), for long-term human motion prediction.
318	Lipper: Synthesizing Thy Speech Using Multi-View Lipreading	Yaman Kumar, Rohit Jain, Khwaja Mohd. Salik, Rajiv Ratn Shah, Yifang Yin, Roger Zimmermann	Thus, in this paper we propose a multi-view lipreading to audio system, namely Lipper, which models it as a regression task.
319	Goal-Oriented Dialogue Policy Learning from Failures	Keting Lu, Shiqi Zhang, Xiaoping Chen	In this work, we develop two complex HER methods providing different tradeoffs between complexity and performance, and, for the first time, enabled HER-based dialogue policy learning.
320	Be Inaccurate but Don’t Be Indecisive: How Error Distribution Can Affect User Experience	Rafael R. Padovani, Lucas N. Ferreira, Levi H. S. Lelis	In this paper we study, in the context of background music selection for tabletop games, how the error distribution of an intelligent system affects the user’s perceived experience.
321	Consensual Affine Transformations for Partial Valuation Aggregation	Hermann Schichl, Meinolf Sellmann	We propose several variants of a new aggregation framework that takes this into account by computing consensual affine transformations of each expert’s scores to reach a globally balanced view.
322	CycleEmotionGAN: Emotional Semantic Consistency Preserved CycleGAN for Adapting Image Emotions	Sicheng Zhao, Chuang Lin, Pengfei Xu, Sendong Zhao, Yuchen Guo, Ravi Krishna, Guiguang Ding, Kurt Keutzer	In this paper, we investigate the unsupervised domain adaptation (UDA) problem in image emotion classification.
323	Preference-Aware Task Assignment in Spatial Crowdsourcing	Yan Zhao, Jinfu Xia, Guanfeng Liu, Han Su, Defu Lian, Shuo Shang, Kai Zheng	In this paper, we propose a novel preference-aware spatial task assignment system based on workers’ temporal preferences, which consists of two components: History-based Context-aware Tensor Decomposition (HCTD) for workers’ temporal preferences modeling and preference-aware task assignment.
324	Satisfiability in Strategy Logic Can Be Easier than Model Checking	Erman Acar, Massimo Benerecetti, Fabio Mogavero	In this paper, we investigate the connection between the two problems for a non-trivial fragment of Strategy Logic (SL, for short).
325	Unbounded Orchestrations of Transducers for Manufacturing	Natasha Alechina, Tomáš Brázdil, Giuseppe De Giacomo, Paolo Felli, Brian Logan, Moshe Y. Vardi	In this paper, we consider the more general problem of whether a controller can be synthesized given sufficient resources.
326	Relaxing and Restraining Queries for OBDA	Medina Andreşel, Yazmín Ibáñez-García, Magdalena Ortiz, Mantas šimkus	We propose a set of rewriting rules to relax and restrain conjunctive queries (CQs) over datasets mediated by an ontology written in a dialect of DL-Lite with complex role inclusions (CRIs).
327	Certifying the True Error: Machine Learning in Coq with Verified Generalization Guarantees	Alexander Bagnall, Gordon Stewart	We present MLCERT, a novel system for doing practical mechanized proof of the generalization of learning procedures, bounding expected error in terms of training or test error.
328	Extension Removal in Abstract Argumentation – An Axiomatic Approach	Ringo Baumann, Gerhard Brewka	Here we study the inverse problem, namely the extension removal problem: is it possible – and if so how – to modify a given argumentation framework in such a way that certain undesired extensions are no longer generated?
329	Abstracting Causal Models	Sander Beckers, Joseph Y. Halpern	We consider a sequence of successively more restrictive definitions of abstraction for causal models, starting with a notion introduced by Rubenstein et al. (2017) called exact transformation that applies to probabilistic causal models, moving to a notion of uniform transformation that applies to deterministic causal models and does not allow differences to be hidden by the “right” choice of distribution, and then to abstraction, where the interventions of interest are determined by the map from low-level states to high-level states, and strong abstraction, which takes more seriously all potential interventions in a model, not just the allowed interventions.
330	Weighted Abstract Dialectical Frameworks through the Lens of Approximation Fixpoint Theory	Bart Bogaerts	In this paper, we propose a different view on wADFs: we develop semantics for wADFs based on approximation fixpoint theory, an abstract algebraic theory designed to capture semantics of various non-monotonic reasoning formalisms.
331	Enhancing Lazy Grounding with Lazy Normalization in Answer-Set Programming	Jori Bomanson, Tomi Janhunen, Antonius Weinzierl	In this work, we introduce a framework to handle aggregates by normalizing them on demand during lazy grounding, hence relieving the restrictions of lazy grounding significantly.
332	Learning Features and Abstract Actions for Computing Generalized Plans	Blai Bonet, Guillem Francès, Hector Geffner	In this work, we address this limitation by showing how to learn them automatically.
333	Ontology-Mediated Query Answering over Log-Linear Probabilistic Data	Stefan Borgwardt, İsmail İlkan Ceylan, Thomas Lukasiewicz	We propose a new data model that integrates the paradigm of ontology-mediated query answering with probabilistic databases, employing a log-linear probability model.
334	Querying Attributed DL-Lite Ontologies Using Provenance Semirings	Camille Bourgaux, Ana Ozaki	Querying Attributed DL-Lite Ontologies Using Provenance Semirings
335	Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models	Andreas Bunte, Benno Stein, Oliver Niggemann	This paper introduces a novel approach to Model-Based Diagnosis (MBD) for hybrid technical systems.
336	From Horn-SRIQ to Datalog: A Data-Independent Transformation That Preserves Assertion Entailment	David Carral, Larry González, Patrick Koopmann	We go one step further and present one such data-independent rewriting technique for Horn-SRIQ⊓, the extension of Horn-SHIQ that supports role chain axioms, an expressive feature prominently used in many real-world ontologies.
337	Identification of Causal Effects in the Presence of Selection Bias	Juan D. Correa, Jin Tian, Elias Bareinboim	In this paper, we consider the problem of identifiability of causal effects when both confounding and selection biases are simultaneously present.
338	Argumentation for Explainable Scheduling	Kristijonas čyras, Dimitrios Letsios, Ruth Misener, Francesca Toni	Specifically, we define argumentative and natural language explanations for why a schedule is (not) feasible, (not) efficient or (not) satisfying fixed user decisions, based on models of the fundamental makespan scheduling problem in terms of abstract argumentation frameworks (AFs).
339	Approximate Stream Reasoning with Metric Temporal Logic under Uncertainty	Daniel de Leng, Fredrik Heintz	The main contribution of this paper is therefore an extension of the progression procedure that efficiently keeps track of all consistent hypotheses.
340	ABox Abduction via Forgetting in ALC	Warren Del-Pinto, Renate A. Schmidt	Two approaches to redundancy elimination are explored in practice: full and approximate.
341	Qualitative Spatial Logic over 2D Euclidean Spaces Is Not Finitely Axiomatisable	Heshan Du, Natasha Alechina	We answer this question negatively by showing that the axiomatisations presented in (Du et al. 2013; Du and Alechina 2016) are not complete for 2D Euclidean spaces and, moreover, the logics are not finitely axiomatisable.
342	Validation of Growing Knowledge Graphs by Abductive Text Evidences	Jianfeng Du, Jeff Z. Pan, Sylvia Wang, Kunxun Qi, Yuming Shen, Yu Deng	This paper proposes a validation mechanism for newly added triples in a growing knowledge graph.
343	On Structured Argumentation with Conditional Preferences	Phan Minh Dung, Phan Minh Thang, Tran Cao Son	We introduce the notions of preference attack relations as sets of attacks between preference arguments and the rebuts or undercuts among arguments as well as of preference attack relation assignments which map knowledge bases to preference attack relations.
344	Complexity of Abstract Argumentation under a Claim-Centric View	Wolfgang Dvořák, Stefan Woltran	As we outline in this paper, this is not only a slight deviation but can lead to different complexity results.
345	Strong Equivalence for Epistemic Logic Programs Made Easy	Wolfgang Faber, Michael Morak, Stefan Woltran	In this paper, we consider a simpler, more direct characterization that is directly applicable to the language used in state-of-the-art ELP solvers.
346	Disjunctive Normal Form for Multi-Agent Modal Logics Based on Logical Separability	Liangda Fang, Kewen Wang, Zhe Wang, Ximing Wen	To demonstrate the usefulness of our approach, we apply SDNF in multi-agent epistemic planning.
347	Counting Complexity for Reasoning in Abstract Argumentation	Johannes K. Fichte, Markus Hecher, Arne Meier	In this paper, we consider counting and projected model counting of extensions in abstract argumentation for various semantics.
348	A Sequential Set Generation Method for Predicting Set-Valued Outputs	Tian Gao, Jie Chen, Vijil Chenthamarakshan, Michael Witbrock	In this paper, we propose a unified framework—sequential set generation (SSG)—that can handle output sets of labels and sequences.
349	Forgetting in Modular Answer Set Programming	Ricardo Gonçalves, Tomi Janhunen, Matthias Knorr, João Leite, Stefan Woltran	In this paper, we present a novel class of forgetting operators and show that such operators can always be successfully applied in Modular ASP to forget all kinds of atoms – input, output and hidden – overcoming the impossibility results that exist for general ASP.
350	Partial Awareness	Joseph Y. Halpern, Evan Piermont	We develop a modal logic to capture partial awareness.
351	Modular Materialisation of Datalog Programs	Pan Hu, Boris Motik, Ian Horrocks	To integrate such algorithms into a general reasoning approach that can handle arbitrary rules, we propose a modular framework for computing and maintaining a materialisation.
352	Bi-Kronecker Functional Decision Diagrams: A Novel Canonical Representation of Boolean Functions	Xuanxiang Huang, Kehang Fang, Liangda Fang, Qingliang Chen, Zhao-Rong Lai, Linfeng Wei	In this paper, we present a novel data structure for compact representation and effective manipulations of Boolean functions, called Bi-Kronecker Functional Decision Diagrams (BKFDDs).
353	Minimum Intervention Cover of a Causal Graph	Saravanan Kandasamy, Arnab Bhattacharyya, Vasant G. Honavar	We provide an algorithm that, given a causal graph G, determines MIC(G), a minimum intervention cover of G, i.e., a minimum set of interventions that suffices for identifying every causal effect that is identifiable in a causal model characterized by G.
354	Knowledge Refinement via Rule Selection	Phokion G. Kolaitis, Lucian Popa, Kun Qian	In this paper, we carry out a systematic complexity-theoretic investigation of the following rule selection problem: given a set of rules specified by Horn formulas, and a pair of an input database and an output database, find a subset of the rules that minimizes the total error, that is, the number of false positive and false negative errors arising from the selected rules.
355	LENA: Locality-Expanded Neural Embedding for Knowledge Base Completion	Fanshuang Kong, Richong Zhang, Yongyi Mao, Ting Deng	In this work, we observe that existing embedding models all have their loss functions decomposed into atomic loss functions, each on a triple or an postulated edge in the knowledge graph.
356	Ontology-Based Query Answering for Probabilistic Temporal Data	Patrick Koopmann	We present a framework that allows to represent temporal probabilistic data, and introduce a query language with which complex temporal and probabilistic patterns can be described.
357	Combining Deep Learning and Qualitative Spatial Reasoning to Learn Complex Structures from Sparse Examples with Noise	Nikhil Krishnaswamy, Scott Friedman, James Pustejovsky	We present a novel approach to introducing new spatial structures to an AI agent, combining deep learning over qualitative spatial relations with various heuristic search algorithms.
358	Representing and Learning Grammars in Answer Set Programming	Mark Law, Alessandra Russo, Elisa Bertino, Krysia Broda, Jorge Lobo	In this paper we introduce an extension of context-free grammars called answer set grammars (ASGs).
359	Multi-Context System for Optimization Problems	Tiep Le, Tran Cao Son, Enrico Pontelli	This paper proposes Multi-context System for Optimization Problems (MCS-OP) by introducing conditional costassignment bridge rules to Multi-context Systems (MCS).
360	Reasoning over Assumption-Based Argumentation Frameworks via Direct Answer Set Programming Encodings	Tuomo Lehtonen, Johannes P. Wallner, Matti Järvisalo	Focusing on assumption-based argumentation (ABA) as a central structured formalism to AI argumentation, we propose a new approach to reasoning in ABA with and without preferences.
361	SAT-Based Explicit LTLf Satisfiability Checking	Jianwen Li, Kristin Y. Rozier, Geguang Pu, Yueling Zhang, Moshe Y. Vardi	We present a SAT-based framework for LTLf (Linear Temporal Logic on Finite Traces) satisfiability checking.
362	Implanting Rational Knowledge into Distributed Representation at Morpheme Level	Zi Lin, Yang Liu	In this paper, after constructing the Chinese lexical and semantic ontology based on word-formation, we propose a novel approach to implanting the structured rational knowledge into distributed representation at morpheme level, naturally avoiding heavy disambiguation in the corpus.
363	Complexity of Inconsistency-Tolerant Query Answering in Datalog+/– under Cardinality-Based Repairs	Thomas Lukasiewicz, Enrico Malizia, Andrius Vaicenavičius	In this paper, we give a precise picture of the computational complexity of inconsistencytolerant (Boolean conjunctive) query answering in a wide range of Datalog± languages under the cardinality-based versions of the above three repair semantics.
364	SDRL: Interpretable and Data-Efficient Deep Reinforcement Learning Leveraging Symbolic Planning	Daoming Lyu, Fangkai Yang, Bo Liu, Steven Gustafson	In this paper, we introduce symbolic planning into DRL and propose a framework of Symbolic Deep Reinforcement Learning (SDRL) that can handle both high-dimensional sensory inputs and symbolic planning.
365	Less but Better: Generalization Enhancement of Ordinal Embedding via Distributional Margin	Ke Ma, Qianqian Xu, Zhiyong Yang, Xiaochun Cao	To address the issue of insufficient training samples, we propose a margin distribution learning paradigm for ordinal embedding, entitled Distributional Margin based Ordinal Embedding (DMOE).
366	Group Decision Diagram (GDD): A Compact Representation for Permutations	Takanori Maehara, Yuma Inoue	In this study, we develop a new data structure, called group decision diagram (GDD), to maintain a set of permutations.
367	On Limited Conjunctions and Partial Features in Parameter-Tractable Feature Logics	Stephanie McIntyre, Alexander Borgida, David Toman, Grant Weddell	Standard reasoning problems are complete for EXPTIME in common feature-based description logics—ones in which all roles are restricted to being functions.
368	Declarative Question Answering over Knowledge Bases Containing Natural Language Text with Answer Set Programming	Arindam Mitra, Peter Clark, Oyvind Tafjord, Chitta Baral	To address this we propose an approach that does logical reasoning over premises written in natural language text. To test our approach we develop a corpus based on the life cycle questions and showed that Our system achieves up to 18% performance gain when compared to standard MCQ solvers.
369	Blameworthiness in Strategic Games	Pavel Naumov, Jia Tao	Blameworthiness in Strategic Games
370	Belief Change and Non-Monotonic Reasoning Sans Compactness	Jandson S. Ribeiro, Abhaya Nayak, Renata Wassermann	In this paper we investigate the impact of such relaxation on non-monotonic logics instead.
371	ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning	Maarten Sap, Ronan Le Bras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith, Yejin Choi	We present ATOMIC, an atlas of everyday commonsense reasoning, organized through 877k textual descriptions of inferential knowledge.
372	Efficient Concept Induction for Description Logics	Md Kamruzzaman Sarker, Pascal Hitzler	In this paper we present a new algorithm for this problem which drastically reduces the number of reasoner invokations needed.
373	An Open-World Extension to Knowledge Graph Completion Models	Haseeb Shah, Johannes Villmow, Adrian Ulges, Ulrich Schwanecke, Faisal Shafait	We present a novel extension to embedding-based knowledge graph completion models which enables them to perform open-world link prediction, i.e. to predict facts for entities unseen in training based on their textual description.
374	Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME	Farhad Shakerin, Gopal Gupta	We present a heuristic based algorithm to induce nonmonotonic logic programs that will explain the behavior of XGBoost trained classifiers.
375	End-to-End Structure-Aware Convolutional Networks for Knowledge Base Completion	Chao Shang, Yun Tang, Jing Huang, Jinbo Bi, Xiaodong He, Bowen Zhou	In this work, we propose a novel end-to-end StructureAware Convolutional Network (SACN) that takes the benefit of GCN and ConvE together.
376	Amalgamating Knowledge towards Comprehensive Classification	Chengchao Shen, Xinchao Wang, Jie Song, Li Sun, Mingli Song	We propose in this paper to study a new model-reusing task, which we term as knowledge amalgamation.
377	Iterated Belief Base Revision: A Dynamic Epistemic Logic Approach	Marlo Souza, álvaro Moreira, Renata Vieira	AGM’s belief revision is one of the main paradigms in the study of belief change operations.
378	Safe Partial Diagnosis from Normal Observations	Roni Stern, Brendan Juba	We explore theoretically how observing the system when it is in a normal state can provide information about the system that is sufficient to learn a partial system model that allows automated diagnosis.
379	Reasoning over Streaming Data in Metric Temporal Datalog	Przemysław Andrzej Wałęga, Mark Kaminski, Bernardo Cuenca Grau	We propose a sound and complete stream reasoning algorithm that is applicable to a fragment datalogMTLFP of datalogMTL, in which propagation of derived information towards past time points is precluded.
380	TransGate: Knowledge Graph Embedding with Shared Gate Structure	Jun Yuan, Neng Gao, Ji Xiang	In this paper, we follow the thought of parameter sharing to simultaneously learn more expressive features, reduce parameters and avoid complex feature engineering.
381	Recursively Learning Causal Structures Using Regression-Based Conditional Independence Test	Hao Zhang, Shuigeng Zhou, Chuanxu Yan, Jihong Guan, Xin Wang	This paper addresses two important issues in causality inference.
382	Tracking Logical Difference in Large-Scale Ontologies: A Forgetting-Based Approach	Yizheng Zhao, Ghadah Alghamdi, Renate A. Schmidt, Hao Feng, Giorgos Stoilos, Damir Juric, Mohammad Khodadadi	To overcome drawbacks of existing forgetting/uniform interpolation tools we introduce a new forgetting method designed for the task of computing the logical difference between different versions of large-scale ontologies.
383	On Completing Sparse Knowledge Base with Transitive Relation Embedding	Zili Zhou, Shaowu Liu, Guandong Xu, Wu Zhang	This paper addresses this issue by proposing a new model exploiting the entity-independent transitive relation patterns, namely Transitive Relation Embedding (TRE).
384	State Abstraction as Compression in Apprenticeship Learning	David Abel, Dilip Arumugam, Kavosh Asadi, Yuu Jinnai, Michael L. Littman, Lawson L.S. Wong	In this work, we offer the first formalism and analysis of the trade-off between compression and performance made in the context of state abstraction for Apprenticeship Learning.
385	An Exponential Tail Bound for the Deleted Estimate	Karim Abou–Moustafa, Csaba Szepesvári	In this paper, we address the gap between these two regimes of results.
386	Model Learning for Look-Ahead Exploration in Continuous Control	Arpit Agarwal, Katharina Muelling, Katerina Fragkiadaki	We propose an exploration method that incorporates lookahead search over basic learnt skills and their dynamics, and use it for reinforcement learning (RL) of manipulation policies.
387	Character-Level Language Modeling with Deeper Self-Attention	Rami Al-Rfou, Dokook Choe, Noah Constant, Mandy Guo, Llion Jones	In this paper, we show that a deep (64-layer) transformer model (Vaswani et al. 2017) with fixed context outperforms RNN variants by a large margin, achieving state of the art on two popular benchmarks: 1.13 bits per character on text8 and 1.06 on enwik8.
388	Attacking Data Transforming Learners at Training Time	Scott Alfeld, Ara Vartanian, Lucas Newman-Johnson, Benjamin I.P. Rubinstein	We develop a general-purpose “plug and play” framework for gradient-based attacks based on matrix differentials, focusing on ordinary least-squares linear regression.
389	Hyperprior Induced Unsupervised Disentanglement of Latent Representations	Abdul Fatir Ansari, Harold Soh	We address the problem of unsupervised disentanglement of latent representations learnt via deep generative models.
390	Adversarial Label Learning	Chidubem Arachie, Bert Huang	We propose a weakly supervised method—adversarial label learning—that trains classifiers to perform well against an adversary that chooses labels for training data.
391	Robust Negative Sampling for Network Embedding	Mohammadreza Armandpour, Patrick Ding, Jianhua Huang, Xia Hu	In this paper, we provide theoretical arguments that reveal how NS can fail to properly estimate the SGA objective, and why it is not a suitable candidate for the network embedding problem as a distinct objective.
392	Random Feature Maps for the Itemset Kernel	Kyohei Atarashi, Subhransu Maji, Satoshi Oyama	We present random feature maps for the itemset kernel, which uses feature combinations, and includes the ANOVA kernel, the all-subsets kernel, and the standard dot product.
393	High Dimensional Clustering with r-nets	Georgia Avarikioti, Alain Ryser, Yuyi Wang, Roger Wattenhofer	In this paper, we consider a well-known structure, so-called r-nets, which rigorously captures the properties of clustering.
394	Mode Variational LSTM Robust to Unseen Modes of Variation: Application to Facial Expression Recognition	Wissam J. Baddar, Yong Man Ro	In this work, we investigate the effect of mode variations on the encoded spatio-temporal features using LSTMs.
395	Enhanced Random Forest Algorithms for Partially Monotone Ordinal Classification	Christopher Bartley, Wei Liu, Mark Reynolds	The proposed approaches are shown to reduce the bias induced by monotonisation and thereby improve accuracy.
396	Online Learning from Data Streams with Varying Feature Spaces	Ege Beyazit, Jeevithan Alagurajah, Xindong Wu	In this paper, we propose a novel online learning algorithm OLVF to learn from data with arbitrarily varying feature spaces.
397	CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks	Akhilan Boopathy, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel	This motivates us to propose a general and efficient framework, CNN-Cert, that is capable of certifying robustness on general convolutional neural networks.
398	Deep Convolutional Sum-Product Networks	Cory J. Butz, Jhonatan S. Oliveira, André E. dos Santos, André L. Teixeira	We give conditions under which convolutional neural networks (CNNs) define valid sum-product networks (SPNs).
399	FRAME Revisited: An Interpretation View Based on Particle Evolution	Xu Cai, Yang Wu, Guanbin Li, Ziliang Chen, Liang Lin	In this paper, we provide a new theoretical insight to analyze FRAME, from a perspective of particle physics ascribing the weird phenomenon to KL-vanishing issue.
400	Dynamic Learning of Sequential Choice Bandit Problem under Marketing Fatigue	Junyu Cao, Wei Sun	We propose a novel sequential choice model to capture multiple interactions taking place between the platform and its user: Upon receiving a message, a user decides on one of the three actions: accept the message, skip and receive the next message, or abandon the platform.
401	Adversarial Learning of Semantic Relevance in Text to Image Synthesis	Miriam Cha, Youngjune L. Gwon, H. T. Kung	We describe a new approach that improves the training of generative adversarial nets (GANs) for synthesizing diverse images from a text input.
402	Towards Non-Saturating Recurrent Units for Modelling Long-Term Dependencies	Sarath Chandar, Chinnadhurai Sankar, Eugene Vorontsov, Samira Ebrahimi Kahou, Yoshua Bengio	We propose a new recurrent architecture (Non-saturating Recurrent Unit; NRU) that relies on a memory mechanism but forgoes both saturating activation functions and saturating gates, in order to further alleviate vanishing gradients.
403	Disjoint Label Space Transfer Learning with Common Factorised Space	Xiaobin Chang, Yongxin Yang, Tao Xiang, Timothy M. Hospedales	In this paper, a unified approach is presented to transfer learning that addresses several source and target domain labelspace and annotation assumptions with a single model.
404	Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation	Chao Chen, Zhihong Chen, Boyuan Jiang, Xinyu Jin	To alleviate this issue, we propose to joint domain alignment and discriminative feature learning, which could benefit both domain alignment and final classification.
405	Two-Stage Label Embedding via Neural Factorization Machine for Multi-Label Classification	Chen Chen, Haobo Wang, Weiwei Liu, Xingyuan Zhao, Tianlei Hu, Gang Chen	To tackle this issue, we propose a novel Two-Stage Label Embedding (TSLE) paradigm that involves Neural Factorization Machine (NFM) to jointly project features and labels into a latent space.
406	Large-Scale Interactive Recommendation with Tree-Structured Policy Gradient	Haokun Chen, Xinyi Dai, Han Cai, Weinan Zhang, Xuejian Wang, Ruiming Tang, Yuzhou Zhang, Yong Yu	To avoid such inconsistency and achieve high efficiency and recommendation effectiveness, in this paper, we propose a Tree-structured Policy Gradient Recommendation (TPGR) framework, where a balanced hierarchical clustering tree is built over the items and picking an item is formulated as seeking a path from the root to a certain leaf of the tree.
407	Distributionally Robust Semi-Supervised Learning for People-Centric Sensing	Kaixuan Chen, Lina Yao, Dalin Zhang, Xiaojun Chang, Guodong Long, Sen Wang	To address this problem, we propose a generic distributionally robust model for semi-supervised learning on distributionally shifted data.
408	Deep Neural Network Quantization via Layer-Wise Optimization Using Limited Training Data	Shangyu Chen, Wenya Wang, Sinno Jialin Pan	In this paper, we propose a novel layer-wise quantization method for deep neural networks, which only requires limited training data (1% of original dataset).
409	EA-CG: An Approximate Second-Order Method for Training Fully-Connected Neural Networks	Sheng-Wei Chen, Chun-Nan Chou, Edward Y. Chang	For training fully-connected neural networks (FCNNs), we propose a practical approximate second-order method including: 1) an approximation of the Hessian matrix and 2) a conjugate gradient (CG) based method.
410	Data-Adaptive Metric Learning with Scale Alignment	Shuo Chen, Chen Gong, Jian Yang, Ying Tai, Le Hui, Jun Li	To address this issue, this paper proposes a novel method dubbed “Data-Adaptive Metric Learning” (DAML), which constructs a data-adaptive projection matrix for each data pair by selectively combining a set of learned candidate matrices.
411	A Layer Decomposition-Recomposition Framework for Neuron Pruning towards Accurate Lightweight Networks	Weijie Chen, Yuan Zhang, Di Xie, Shiliang Pu	To this end, we propose a novel Layer DecompositionRecomposition Framework (LDRF) for neuron pruning, by which each layer’s output information is recovered in an embedding space and then propagated to reconstruct the following pruned layers with useful information preserved.
412	Embedding Uncertain Knowledge Graphs	Xuelu Chen, Muhao Chen, Weijia Shi, Yizhou Sun, Carlo Zaniolo	Embedding models for deterministic Knowledge Graphs (KG) have been extensively studied, with the purpose of capturing latent semantic relations between entities and incorporating the structured knowledge they contain into machine learning.
413	Tensor Decomposition for Multilayer Networks Clustering	Zitai Chen, Chuan Chen, Zibin Zheng, Yi Zhu	To address this issue, we propose Centroid-based Multilayer Network Clustering (CMNC), a novel approach which can divide irrelevant relationships into different network groups and uncover the cluster structure in each group simultaneously.
414	Image Block Augmentation for One-Shot Learning	Zitian Chen, Yanwei Fu, Kaiyu Chen, Yu-Gang Jiang	Previous one-shot learning works investigate the metalearning or metric-based algorithms; in contrast, this paper proposes a Self-Training Jigsaw Augmentation (Self-Jig) method for one-shot learning.
415	End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks	Richard Cheng, Gábor Orosz, Richard M. Murray, Joel W. Burdick	To address this issue, we propose a controller architecture that combines (1) a model-free RL-based controller with (2) model-based controllers utilizing control barrier functions (CBFs) and (3) online learning of the unknown system dynamics, in order to ensure safety during learning.
416	Utilizing Class Information for Deep Network Representation Shaping	Daeyoung Choi, Wonjong Rhee	Motivated by the idea, we design two class-wise regularizers that explicitly utilize class information: class-wise Covariance Regularizer (cw-CR) and classwise Variance Regularizer (cw-VR).
417	Diverse Exploration via Conjugate Policies for Policy Gradient Methods	Andrew Cohen, Xingye Qiao, Lei Yu, Elliot Way, Xiangrong Tong	As a solution, we propose diverse exploration (DE) via conjugate policies.
418	Spatially Invariant Unsupervised Object Detection with Convolutional Neural Networks	Eric Crawford, Joelle Pineau	In the current work, we develop a neural network architecture that effectively addresses this large-image, many-object setting.
419	Efficient Online Learning for Mapping Kernels on Linguistic Structures	Giovanni Da San Martino, Alessandro Sperduti, Fabio Aiolli, Alessandro Moschitti	In this paper, we analyze how to speed up the prediction when the kernel function is an instance of the Mapping Kernels, a general framework for specifying kernels for structured data which extends the popular convolution kernel framework.
420	Learning Segmentation Masks with the Independence Prior	Songmin Dai, Xiaoqiang Li, Lu Wang, Pin Wu, Weiqin Tong, Yimin Chen	To achieve this, we propose a novel framework that exploits a new proposed prior called the independence prior based on Generative Adversarial Networks (GANs).
421	Inverse Abstraction of Neural Networks Using Symbolic Interpolation	Sumanth Dathathri, Sicun Gao, Richard M. Murray	We introduce new methods for computing compact symbolic abstractions of pre-images by computing their overapproximations and underapproximations through all layers.
422	Balanced Linear Contextual Bandits	Maria Dimakopoulou, Zhengyuan Zhou, Susan Athey, Guido Imbens	We develop algorithms for contextual bandits with linear payoffs that integrate balancing methods from the causal inference literature in their estimation to make it less prone to problems of estimation bias.
423	Linear Kernel Tests via Empirical Likelihood for High-Dimensional Data	Lizhong Ding, Zhi Liu, Yu Li, Shizhong Liao, Yong Liu, Peng Yang, Ge Yu, Ling Shao, Xin Gao	We propose a framework for analyzing and comparing distributions without imposing any parametric assumptions via empirical likelihood methods.
424	Approximate Kernel Selection with Strong Approximate Consistency	Lizhong Ding, Yong Liu, Shizhong Liao, Yu Li, Peng Yang, Yijie Pan, Chao Huang, Ling Shao, Xin Gao	In this paper, we propose a novel Nyström approximate kernel selection algorithm by customizing a criterion-driven adaptive sampling distribution for the Nyström approximation, which adaptively reduces the error between the approximate and accurate criteria.
425	On-Line Adaptative Curriculum Learning for GANs	Thang Doan, João Monteiro, Isabela Albuquerque, Bogdan Mazoure, Audrey Durand, Joelle Pineau, R. Devon Hjelm	In this paper, we build on existing work in the area by proposing a novel framework for training the generator against an ensemble of discriminator networks, which can be seen as a one-student/multiple-teachers setting.
426	Multistream Classification with Relative Density Ratio Estimation	Bo Dong, Yang Gao, Swarup Chandra, Latifur Khan	In this paper, we focus on utilizing an alternative bias correction technique, called relative density-ratio estimation, which is known to be computationally faster.
427	Single-Label Multi-Class Image Classification by Deep Logistic Regression	Qi Dong, Xiatian Zhu, Shaogang Gong	In this work, we analyse thoroughly the standard learning objective functions for multiclass classification CNNs: softmax regression (SR) for singlelabel scenario and logistic regression (LR) for multi-label scenario.
428	How to Combine Tree-Search Methods in Reinforcement Learning	Yonathan Efroni, Gal Dalal, Bruno Scherrer, Shie Mannor	Here, we question the potency of this approach.
429	Human-Like Delicate Region Erasing Strategy for Weakly Supervised Detection	Qing En, Lijuan Duan, Zhaoxiang Zhang, Xiang Bai, Yundong Zhang	We explore a principle method to address the weakly supervised detection problem.
430	Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation	Lijie Fan, Wenbing Huang, Chuang Gan, Junzhou Huang, Boqing Gong	In this paper, for the sake of both furthering this exploration and our own interest in a realistic application, we study imageto-video translation and particularly focus on the videos of facial expressions.
431	Partial Multi-Label Learning via Credible Label Elicitation	Jun-Peng Fang, Min-Ling Zhang	In light of this major difficulty, a novel two-stage PML approach is proposed which works by eliciting credible labels from the candidate label set for model induction.
432	Improved Knowledge Graph Embedding Using Background Taxonomic Information	Bahare Fatemi, Siamak Ravanbakhsh, David Poole	To enable learning about domains, embedding models, such as tensor factorization models, can be used to make predictions of new triples.
433	Unsupervised Feature Selection by Pareto Optimization	Chao Feng, Chao Qian, Ke Tang	In this paper, we consider its natural formulation, column subset selection (CSS), which is to minimize the reconstruction error of a data matrix by selecting a subset of features.
434	Partial Label Learning with Self-Guided Retraining	Lei Feng, Bo An	Specifically, we propose a unified formulation with proper constraints to train the desired model and perform pseudo-labeling jointly.
435	Collaboration Based Multi-Label Learning	Lei Feng, Bo An, Shuo He	In this paper, we suggest that for each individual label, the final prediction involves the collaboration between its own prediction and the predictions of other labels.
436	Hypergraph Neural Networks	Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, Yue Gao	In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure.
437	Transductive Bounds for the Multi-Class Majority Vote Classifier	Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini	In this paper, we propose a transductive bound over the risk of the majority vote classifier learned with partially labeled data for the multi-class classification.
438	The Goldilocks Zone: Towards Better Understanding of Neural Network Loss Landscapes	Stanislav Fort, Adam Scherlis	We explore the loss landscape of fully-connected and convolutional neural networks using random, low-dimensional hyperplanes and hyperspheres.
439	Combined Reinforcement Learning via Abstract Representations	Vincent Francois-Lavet, Yoshua Bengio, Doina Precup, Joelle Pineau	In this paper we propose a new way of explicitly bridging both approaches via a shared low-dimensional learned encoding of the environment, meant to capture summarizing abstractions.
440	Efficient Data Point Pruning for One-Class SVM	Yasuhiro Fujiwara, Sekitoshi Kanai, Junya Arai, Yasutoshi Ida, Naonori Ueda	This paper proposes Quix as an efficient training algorithm for one-class SVM.
441	Fully Convolutional Network with Multi-Step Reinforcement Learning for Image Processing	Ryosuke Furuta, Naoto Inoue, Toshihiko Yamasaki	We apply the proposed method to three image processing tasks: image denoising, image restoration, and local color enhancement.
442	Bayesian Posterior Approximation via Greedy Particle Optimization	Futoshi Futami, Zhenghang Cui, Issei Sato, Masashi Sugiyama	In this paper, we propose a novel method named maximum mean discrepancy minimization by the Frank-Wolfe algorithm (MMD-FW), which minimizes MMD in a greedy way by the FW algorithm.
443	Towards Reliable Learning for High Stakes Applications	Jinyang Gao, Junjie Yao, Yingxia Shao	In this paper, we focus on delivering reliable learning results for high stakes applications such as self-driving, financial investment and clinical diagnosis, where the accuracy of predictions is considered as a more crucial requirement than giving predictions for all query samples.
444	Explainable Recommendation through Attentive Multi-View Learning	Jingyue Gao, Xiting Wang, Yasha Wang, Xing Xie	In this paper, we propose to alleviate the trade-off between accuracy and explainability by developing an explainable deep model that combines the advantages of deep learning-based models and existing explainable methods.
445	Wasserstein Soft Label Propagation on Hypergraphs: Algorithm and Generalization Error Bounds	Tingran Gao, Shahab Asoodeh, Yi Huang, James Evans	Inspired by recent interests of developing machine learning and data mining algorithms on hypergraphs, we investigate in this paper the semi-supervised learning algorithm of propagating ”soft labels” (e.g. probability distributions, class membership scores) over hypergraphs, by means of optimal transportation.
446	Incomplete Label Multi-Task Deep Learning for Spatio-Temporal Event Subtype Forecasting	Yuyang Gao, Liang Zhao, Lingfei Wu, Yanfang Ye, Hui Xiong, Chaowei Yang	Optimizing the proposed model amounts to a new nonconvex and strongly-coupled problem, we propose a new algorithm based on Alternating Direction Method of Multipliers (ADMM) that can decompose the complex problem into subproblems that can be solved efficiently.
447	Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift	Carles Gelada, Marc G. Bellemare	In this paper we revisit the method of off-policy corrections for reinforcement learning (COP-TD) pioneered by Hallak et al. (2017).
448	Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting	Xu Geng, Yaguang Li, Leye Wang, Lingyu Zhang, Qiang Yang, Jieping Ye, Yan Liu	In this paper, we propose the spatiotemporal multi-graph convolution network (ST-MGCN), a novel deep learning model for ride-hailing demand forecasting.
449	Counting and Sampling from Markov Equivalent DAGs Using Clique Trees	AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang	In this paper, we propose a new technique for counting the number of DAGs in a Markov equivalence class.
450	Eliminating Latent Discrimination: Train Then Mask	Soheil Ghili, Ehsan Kazemi, Amin Karbasi	In this paper, we define a new operational fairness criteria, inspired by the well-understood notion of omitted variable-bias in statistics and econometrics.
451	Interpretation of Neural Networks Is Fragile	Amirata Ghorbani, Abubakar Abid, James Zou	In this paper, we demonstrate how to generate adversarial perturbations that produce perceptively indistinguishable inputs that are assigned the same predicted label, yet have very different interpretations.
452	Using Benson’s Algorithm for Regularization Parameter Tracking	Joachim Giesen, Sören Laue, Andreas Löhne, Christopher Schneider	A practically more feasible approach is covering the parameter domain with approximate solutions of the loss minimization problem for some prescribed approximation accuracy.
453	Scalable and Efficient Pairwise Learning to Achieve Statistical Accuracy	Bin Gu, Zhouyuan Huo, Heng Huang	Based on the relationship, we propose a scalable and efficient adaptive doubly stochastic gradient algorithm (AdaDSG) for generalized regularized pairwise learning problems.
454	AFS: An Attention-Based Mechanism for Supervised Feature Selection	Ning Gui, Danni Ge, Ziyin Hu	This paper introduces a novel neural network-based feature selection architecture, dubbed Attention-based Feature Selec-tion (AFS).
455	MixUp as Locally Linear Out-of-Manifold Regularization	Hongyu Guo, Yongyi Mao, Richong Zhang	In this paper, we develop an understanding for MixUp as a form of “out-of-manifold regularization”, which imposes certain “local linearity” constraints on the model’s input space beyond the data manifold.
456	Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input	Junliang Guo, Xu Tan, Di He, Tao Qin, Linli Xu, Tie-Yan Liu	In this paper, we propose two methods to enhance the decoder inputs so as to improve NAT models.
457	Smooth Deep Image Generator from Noises	Tianyu Guo, Chang Xu, Boxin Shi, Chao Xu, Dacheng Tao	This paper analyzes the perturbation on the input of the generator and its influence on the generated images.
458	Hybrid Reinforcement Learning with Expert State Sequences	Xiaoxiao Guo, Shiyu Chang, Mo Yu, Gerald Tesauro, Murray Campbell	In this paper, we consider a more realistic and difficult scenario where a reinforcement learning agent only has access to the state sequences of an expert, while the expert actions are unobserved.
459	Distributional Semantics Meets Multi-Label Learning	Vivek Gupta, Rahul Wadbude, Nagarajan Natarajan, Harish Karnick, Prateek Jain, Piyush Rai	We present a label embedding based approach to large-scale multi-label learning, drawing inspiration from ideas rooted in distributional semantics, specifically the Skip Gram Negative Sampling (SGNS) approach, widely used to learn word embeddings.
460	Temporal Anomaly Detection: Calibrating the Surprise	Eyal Gutflaish, Aryeh Kontorovich, Sivan Sabato, Ofer Biller, Oded Sofer	We propose a hybrid approach to temporal anomaly detection in access data of users to databases — or more generally, any kind of subject-object co-occurrence data.
461	Efficient and Scalable Multi-Task Regression on Massive Number of Tasks	Xiao He, Francesco Alesiani, Ammar Shaker	Here, we propose a novel algorithm, named Convex Clustering Multi-Task regression Learning (CCMTL), which integrates with convex clustering on the k-nearest neighbor graph of the prediction models.
462	Knowledge Distillation with Adversarial Samples Supporting Decision Boundary	Byeongho Heo, Minsik Lee, Sangdoo Yun, Jin Young Choi	In this paper, we provide a new perspective based on a decision boundary, which is one of the most important component of a classifier.
463	Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons	Byeongho Heo, Minsik Lee, Sangdoo Yun, Jin Young Choi	In this paper, we propose a knowledge transfer method via distillation of activation boundaries formed by hidden neurons.
464	The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering	Sibylle Hess, Wouter Duivesteijn, Philipp Honysz, Katharina Morik	In this paper, we propose SPECTACL: a method combining the advantages of both approaches, while solving the two mentioned drawbacks.
465	Multi-Task Deep Reinforcement Learning with PopArt	Matteo Hessel, Hubert Soyer, Lasse Espeholt, Wojciech Czarnecki, Simon Schmitt, Hado van Hasselt	In this work, we study the problem of learning to master not one but multiple sequentialdecision tasks at once.
466	Interaction-Aware Factorization Machines for Recommender Systems	Fuxing Hong, Dongbo Huang, Ge Chen	In this work, we propose a novel model named Interaction-aware Factorization Machine (IFM) by introducing Interaction-Aware Mechanism (IAM), which comprises the feature aspect and the field aspect, to learn flexible interactions on two levels.
467	Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing	Hanzhang Hu, Debadeepta Dey, Martial Hebert, J. Andrew Bagnell	This work considers the trade-off between accuracy and testtime computational cost of deep neural networks (DNNs) via anytime predictions from auxiliary predictions.
468	Learning to Adaptively Scale Recurrent Neural Networks	Hao Hu, Liqiang Wang, Guo-Jun Qi	In this paper, we propose Adaptively Scaled Recurrent Neural Networks (ASRNN), a simple but efficient way to handle this problem.
469	HERS: Modeling Influential Contexts with Heterogeneous Relations for Sparse and Cold-Start Recommendation	Liang Hu, Songlei Jian, Longbing Cao, Zhiping Gu, Qingkui Chen, Artak Amirbekyan	Accordingly, we propose a Heterogeneous relations-Embedded Recommender System (HERS) based on ICAUs to model and interpret the underlying motivation of user-item interactions by considering user-user and item-item influences.
470	One-Pass Incomplete Multi-View Clustering	Menglei Hu, Songcan Chen	To address this problem, in this paper, we propose an One-Pass Incomplete Multi-view Clustering framework (OPIMC).
471	Multi-Fidelity Automatic Hyper-Parameter Tuning via Transfer Series Expansion	Yi-Qi Hu, Yang Yu, Wei-Wei Tu, Qiang Yang, Yuqiang Chen, Wenyuan Dai	To alleviate this limitation, in this paper, we propose a derivative-free optimization framework for AutoML using multi-fidelity evaluations.
472	Efficient Quantization for Neural Networks with Binary Weights and Low Bitwidth Activations	Kun Huang, Bingbing Ni, Xiaokang Yang	In this paper, we design a new activation function dubbed CReLU from the quantization perspective and further complement this design with appropriate initialization method and training procedure.
473	Efficient Identification of Approximate Best Configuration of Training in Large Datasets	Silu Huang, Chi Wang, Bolin Ding, Surajit Chaudhuri	To guarantee small accuracy loss, we develop a solution using confidence interval (CI)-based progressive sampling and pruning strategy.
474	Bootstrap Estimated Uncertainty of the Environment Model for Model-Based Reinforcement Learning	Wenzhen Huang, Junge Zhang, Kaiqi Huang	We propose a bootstrapped model-based RL method which bootstraps the modules in each depth of the planning tree.
475	Large-Scale Heterogeneous Feature Embedding	Xiao Huang, Qingquan Song, Fan Yang, Xia Hu	To bridge the gap, we propose a scalable framework FeatWalk, which can model and incorporate instance similarities in terms of different types of features into a unified embedding representation.
476	Manifold-Valued Image Generation with Wasserstein Generative Adversarial Nets	Zhiwu Huang, Jiqing Wu, Luc Van Gool	On the three datasets, we experimentally demonstrate the proposed manifold-aware WGAN model can generate more plausible manifold-valued images than its competitors.
477	Inter-Class Angular Loss for Convolutional Neural Networks	Le Hui, Xiang Li, Chen Gong, Meng Fang, Joey Tianyi Zhou, Jian Yang	Based on this observation, we propose a novel loss function dubbed “Inter-Class Angular Loss” (ICAL), which explicitly models the class correlation and can be directly applied to many existing deep networks.
478	Tensorial Change Analysis Using Probabilistic Tensor Regression	Tsuyoshi Idé	This paper proposes a new method for change detection and analysis using tensor regression.
479	Complex Moment-Based Supervised Eigenmap for Dimensionality Reduction	Akira Imakura, Momo Matsuda, Xiucai Ye, Tetsuya Sakurai	Herein, to overcome the deficiency of the information loss, we propose a novel complex moment-based supervised eigenmap including multiple eigenvectors for dimensionality reduction.
480	Estimating the Causal Effect from Partially Observed Time Series	Akane Iseki, Yusuke Mukuta, Yoshitaka Ushiku, Tatsuya Harada	Treating this problem as a semi-supervised learning problem, we propose a novel semi-supervised extension of probabilistic Partial CCA called semi-Bayesian Partial CCA.
481	TAPAS: Train-Less Accuracy Predictor for Architecture Search	R. Istrate, F. Scheidegger, G. Mariani, D. Nikolopoulos, C. Bekas, A. C. I. Malossi	We propose a new deep neural network accuracy predictor, that estimates in fractions of a second classification performance for unseen input datasets, without training.
482	Neural Collective Graphical Models for Estimating Spatio-Temporal Population Flow from Aggregated Data	Tomoharu Iwata, Hitoshi Shimizu	We propose a probabilistic model for estimating population flow, which is defined as populations of the transition between areas over time, given aggregated spatio-temporal population data.
483	Meta-Descent for Online, Continual Prediction	Andrew Jacobsen, Matthew Schlegel, Cameron Linke, Thomas Degris, Adam White, Martha White	This paper investigates different vector step-size adaptation approaches for non-stationary online, continual prediction problems.
484	Model-Free IRL Using Maximum Likelihood Estimation	Vinamra Jain, Prashant Doshi, Bikramjit Banerjee	In this paper, we present a model-free approach to IRL, which casts IRL in the maximum likelihood framework.
485	Classification with Costly Features Using Deep Reinforcement Learning	Jaromír Janisch, Tomáš Pevný, Viliam Lisý	We study a classification problem where each feature can be acquired for a cost and the goal is to optimize a trade-off between the expected classification error and the feature cost.
486	Tile2Vec: Unsupervised Representation Learning for Spatially Distributed Data	Neal Jean, Sherrie Wang, Anshul Samar, George Azzari, David Lobell, Stefano Ermon	To fill this gap, we introduce Tile2Vec, an unsupervised representation learning algorithm that extends the distributional hypothesis from natural language — words appearing in similar contexts tend to have similar meanings — to spatially distributed data.
487	Multi-Dimensional Classification via kNN Feature Augmentation	Bin-Bin Jia, Min-Ling Zhang	In this paper, a first attempt towards feature manipulation for MDC is proposed which enriches the original feature space with kNNaugmented features.
488	Joint Semi-Supervised Feature Selection and Classification through Bayesian Approach	Bingbing Jiang, Xingyu Wu, Kui Yu, Huanhuan Chen	In this paper, we propose a joint semi-supervised feature selection and classification algorithm (JSFS) which adopts a Bayesian approach to automatically select the relevant features and simultaneously learn a classifier.
489	Fast Incremental SVDD Learning Algorithm with the Gaussian Kernel	Hansi Jiang, Haoyu Wang, Wenhao Hu, Deovrat Kakde, Arin Chaudhuri	We propose an incremental learning algorithm for SVDD that uses the Gaussian kernel.
490	Non-Asymptotic Uniform Rates of Consistency for k-NN Regression	Heinrich Jiang	We derive high-probability finite-sample uniform rates of consistency for k-NN regression that are optimal up to logarithmic factors under mild assumptions.
491	Gaussian-Induced Convolution for Graphs	Jiatao Jiang, Zhen Cui, Chunyan Xu, Jian Yang	In this work, we propose a Gaussianinduced convolution (GIC) framework to conduct local convolution filtering on irregular graphs.
492	SCFont: Structure-Guided Chinese Font Generation via Deep Stacked Networks	Yue Jiang, Zhouhui Lian, Yingmin Tang, Jianguo Xiao	To address those problems, this paper proposes a structure-guided Chinese font generation system, SCFont, by using deep stacked networks.
493	Estimating the Days to Success of Campaigns in Crowdfunding: A Deep Survival Perspective	Binbin Jin, Hongke Zhao, Enhong Chen, Qi Liu, Yong Ge	In this paper, we notice the implicit factor of distribution of backing behaviors has a positive impact on estimating the success time of the campaign.
494	DoPAMINE: Double-Sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling	Sunghwan Joo, Sungmin Cha, Taesup Moon	We propose DoPAMINE, a new neural network based multiplicative noise despeckling algorithm.
495	Precision-Recall versus Accuracy and the Role of Large Data Sets	Brendan Juba, Hai S. Le	In this work, we consider the measures of classifier performance in terms of precision and recall, a measure that is widely suggested as more appropriate to the classification of imbalanced data.
496	Dimension-Free Error Bounds from Random Projections	Ata Kabán	We introduce an auxiliary function class that operates on reduced dimensional inputs, and a new complexity term, as the distortion of the loss under random projections.
497	Similarity Learning via Kernel Preserving Embedding	Zhao Kang, Yiwei Lu, Yuanzhang Su, Changsheng Li, Zenglin Xu	In this paper, we argue that it is beneficial to preserve the overall relations when we extract similarity information.
498	Guided Dropout	Rohit Keshari, Richa Singh, Mayank Vatsa	In this research, we propose “guided dropout” for training deep neural network which drop nodes by measuring the strength of each node.
499	Mixture of Expert/Imitator Networks: Scalable Semi-Supervised Learning Framework	Shun Kiyono, Jun Suzuki, Kentaro Inui	In this paper, we propose a novel scalable method of SSL for text classification tasks.
500	Exploiting Class Learnability in Noisy Data	Matthew Klawonn, Eric Heim, James Hendler	In this work, we aim to explore the classes in a given data set, and guide supervised training to spend time on a class proportional to its learnability.
501	Active Generative Adversarial Network for Image Classification	Quan Kong, Bin Tong, Martin Klinkigt, Yuki Watanabe, Naoto Akira, Tomokazu Murakami	In this paper, we propose a novel model that is able to obtain labels for data in a cheaper manner without the need to query an oracle.
502	On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filters	Mark Kozdoba, Jakub Marecek, Tigran Tchrakian, Shie Mannor	Based on this insight, we devise an on-line algorithm for improper learning of a linear dynamical system (LDS), which considers only a few most recent observations.
503	Unsupervised Domain Adaptation by Matching Distributions Based on the Maximum Mean Discrepancy via Unilateral Transformations	Atsutoshi Kumagai, Tomoharu Iwata	We propose a simple yet effective method for unsupervised domain adaptation.
504	Multi-Source Neural Variational Inference	Richard Kurle, Stephan Günnemann, Patrick van der Smagt	In this paper we formulate a variational autoencoder based multi-source learning framework in which each encoder is conditioned on a different information source.
505	Unsupervised Domain Adaptation Based on Source-Guided Discrepancy	Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama	To mitigate these problems, this paper proposes a novel discrepancy measure called source-guided discrepancy (S-disc), which exploits labels in the source domain unlike the existing ones.
506	TransConv: Relationship Embedding in Social Networks	Yi-Yu Lai, Jennifer Neville, Dan Goldwasser	In this paper, we propose TransConv, a novel approach that incorporates textual interactions between pair of users to improve representation learning of both users and relationships.
507	Accurate and Interpretable Factorization Machines	Liang Lan, Yu Geng	In this paper, we present a novel method named Subspace Encoding Factorization Machines (SEFM) to overcome these two limitations by using non-parametric subspace feature mapping.
508	Gradient-Based Inference for Networks with Output Constraints	Jay Yoon Lee, Sanket Vaibhav Mehta, Michael Wick, Jean-Baptiste Tristan, Jaime Carbonell	In this paper, we present an inference method for neural networks that enforces deterministic constraints on outputs without performing rule-based post-processing or expensive discrete search.
509	Understanding Learned Models by Identifying Important Features at the Right Resolution	Kyubin Lee, Akshay Sood, Mark Craven	We present a model-agnostic approach to this task that makes the following specific contributions.
510	Structural Causal Bandits with Non-Manipulable Variables	Sanghack Lee, Elias Bareinboim	In this paper, we study a relaxed version of the structural causal bandit problem when not all variables are manipulable.
511	Communication-Efficient Stochastic Gradient MCMC for Neural Networks	Chunyuan Li, Changyou Chen, Yunchen Pu, Ricardo Henao, Lawrence Carin	We propose accelerating SG-MCMC under the masterworker framework: workers asynchronously and in parallel share responsibility for gradient computations, while the master collects the final samples.
512	Lifted Proximal Operator Machines	Jia Li, Cong Fang, Zhouchen Lin	We propose a new optimization method for training feedforward neural networks.
513	From Zero-Shot Learning to Cold-Start Recommendation	Jingjing Li, Mengmeng Jing, Ke Lu, Lei Zhu, Yang Yang, Zi Huang	Specifically, we propose a Lowrank Linear Auto-Encoder (LLAE), which challenges three cruxes, i.e., domain shift, spurious correlations and computing efficiency, in this paper.
514	X-DMM: Fast and Scalable Model Based Text Clustering	Linwei Li, Liangchen Guo, Zhenying He, Yinan Jing, X. Sean Wang	We introduce a Metropolis-Hastings sampling algorithm, which further reduces the sampling time complexity from O(K∗U) to O(U) in the nearly-to-convergence training stages.
515	Sign-Full Random Projections	Ping Li	In this paper, we study a series of estimators for “sign-full” random projections.
516	Robust Multi-Agent Reinforcement Learning via Minimax Deep Deterministic Policy Gradient	Shihui Li, Yi Wu, Xinyue Cui, Honghua Dong, Fei Fang, Stuart Russell	In this paper, we focus on the problem of training robust DRL agents with continuous actions in the multi-agent learning setting so that the trained agents can still generalize when its opponents’ policies alter.
517	Spectral Clustering in Heterogeneous Information Networks	Xiang Li, Ben Kao, Zhaochun Ren, Dawei Yin	We formulate the similarity matrix construction as an optimization problem and propose the SClump algorithm for solving the problem.
518	Learning Adaptive Random Features	Yanjun Li, Kai Zhang, Jun Wang, Sanjiv Kumar	In this paper, we propose to compute more adaptive random Fourier features with optimized spectral samples (wj’s) and feature weights (pj’s).
519	Towards Automated Semi-Supervised Learning	Yu-Feng Li, Hai Wang, Tong Wei, Wei-Wei Tu	In this paper, we propose to present an automated learning system for SSL (AUTO-SSL).
520	Learning Disentangled Representation with Pairwise Independence	Zejian Li, Yongchuan Tang, Wei Li, Yongxing He	Accordingly, we propose a new method based on a pairwise independence assumption to learn the disentangled representation.
521	Exploiting Coarse-to-Fine Task Transfer for Aspect-Level Sentiment Classification	Zheng Li, Ying Wei, Yu Zhang, Xiang Zhang, Xin Li	In this paper, we exploit a new direction named coarse-to-fine task transfer, which aims to leverage knowledge learned from a rich-resource source domain of the coarse-grained AC task, which is more easily accessible, to improve the learning in a low-resource target domain of the fine-grained AT task.
522	SepNE: Bringing Separability to Network Embedding	Ziyao Li, Liang Zhang, Guojie Song	In this paper, we formalize the problem of separated matrix factorization, based on which we elaborate a novel objective function that preserves both local and global information.
523	Collaborative, Dynamic and Diversified User Profiling	Shangsong Liang	In this paper, we study the problem of dynamic user profiling in the context of streams of short texts.
524	Learning Logistic Circuits	Yitao Liang, Guy Van den Broeck	This paper proposes a new classification model called logistic circuits.
525	CircConv: A Structured Convolution with Low Complexity	Siyu Liao, Bo Yuan	To overcome these limitations, this paper proposes to impose the circulant structure to the construction of convolutional layers, and hence leads to circulant convolutional layers (CircConvs) and circulant CNNs.
526	Evolutionary Manytasking Optimization Based on Symbiosis in Biocoenosis	Rung-Tzuo Liaw, Chuan-Kang Ting	Beyond three tasks, this paper proposes a novel framework, called the symbiosis in biocoenosis optimization (SBO), to address evolutionary many-tasking optimization.
527	Non-Compensatory Psychological Models for Recommender Systems	Chen Lin, Xiaolin Shen, Si Chen, Muhua Zhu, Yanghua Xiao	Our main contribution in this paper is to study the unexplored area of utilizing non-compensatory rules in recommendation models.
528	Which Factorization Machine Modeling Is Better: A Theoretical Answer with Optimal Guarantee	Ming Lin, Shuang Qiu, Jieping Ye, Xiaomin Song, Qi Qian, Liang Sun, Shenghuo Zhu, Rong Jin	In this work, we aim to tighten this bound towards optimal and generalize the analysis to sub-gaussian distribution.
529	MFPCA: Multiscale Functional Principal Component Analysis	Zhenhua Lin, Hongtu Zhu	The aim of this paper is to propose a novel multiscale functional principal component analysis (MFPCA) approach to address such heteroscedastic issue.
530	Learning Plackett-Luce Mixtures from Partial Preferences	Ao Liu, Zhibing Zhao, Chao Liao, Pinyan Lu, Lirong Xia	We propose an EM-based framework for learning Plackett-Luce model and its mixtures from partial orders.
531	Near-Neighbor Methods in Random Preference Completion	Ao Liu, Qiong Wu, Zhenming Liu, Lirong Xia	Our goal is to identify near neighbors of an arbitrary agent in the latent space for prediction.
532	Scale Invariant Fully Convolutional Network: Detecting Hands Efficiently	Dan Liu, Dawei Du, Libo Zhang, Tiejian Luo, Yanjun Wu, Feiyue Huang, Siwei Lyu	In this paper, we propose a new Scale Invariant Fully Convolutional Network (SIFCN) trained in an end-to-end fashion to detect hands efficiently.
533	Trust Region Evolution Strategies	Guoqing Liu, Li Zhao, Feidiao Yang, Jiang Bian, Tao Qin, Nenghai Yu, Tie-Yan Liu	In this paper, with the purpose of more efficient using of sampled data, we propose a novel iterative procedure that optimizes a surrogate objective function, enabling to reuse data sample for multiple epochs of updates.
534	Learning Multi-Task Communication with Message Passing for Sequence Learning	Pengfei Liu, Jie Fu, Yue Dong, Xipeng Qiu, Jackie Chi Kit Cheung	We adopt the idea from message-passing graph neural networks, and propose a general graph multi-task learning framework in which different tasks can communicate with each other in an effective and interpretable way.
535	A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI	Risheng Liu, Yuxi Zhang, Shichao Cheng, Xin Fan, Zhongxuan Luo	In this work, we develop a new paradigm to integrate designed numerical solvers and the data-driven architectures for CS-MRI.
536	A Bandit Approach to Maximum Inner Product Search	Rui Liu, Tianyi Wu, Barzan Mozafari	In this paper, we propose the first approximate algorithm for MIPS that does not require any preprocessing, and allows users to control and bound the suboptimality of the results.
537	The Utility of Sparse Representations for Control in Reinforcement Learning	Vincent Liu, Raksha Kumaraswamy, Lei Le, Martha White	We identify a simple but effective way to obtain sparse representations, not afforded by previously proposed strategies, making it more practical for further investigation into sparse representations for reinforcement learning.
538	Efficient and Effective Incomplete Multi-View Clustering	Xinwang Liu, Xinzhong Zhu, Miaomiao Li, Chang Tang, En Zhu, Jianping Yin, Wen Gao	In this paper, we propose an Efficient and Effective Incomplete Multi-view Clustering (EE-IMVC) algorithm to address these issues.
539	Ranking-Based Deep Cross-Modal Hashing	Xuanwu Liu, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Yazhou Ren, Maozu Guo	In this paper, we propose a ranking-based deep cross-modal hashing approach (RDCMH).
540	Adaptive Sparse Confidence-Weighted Learning for Online Feature Selection	Yanbin Liu, Yan Yan, Ling Chen, Yahong Han, Yi Yang	In this paper, we propose a new online feature selection algorithm for streaming data.
541	Active Sampling for Open-Set Classification without Initial Annotation	Zhao-Yang Liu, Sheng-Jun Huang	In this paper, we focus on a more challenging case where the data examples collected for known classes are all unlabeled.
542	GeniePath: Graph Neural Networks with Adaptive Receptive Paths	Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, Le Song, Yuan Qi	We present, GeniePath, a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data.
543	Guiding the One-to-One Mapping in CycleGAN via Optimal Transport	Guansong Lu, Zhiming Zhou, Yuxuan Song, Kan Ren, Yong Yu	In this paper, we experimentally find that, under some circumstances, the one-to-one mapping learned by CycleGAN is just a random one within the large feasible solution space.
544	Super Sparse Convolutional Neural Networks	Yao Lu, Guangming Lu, Bob Zhang, Yuanrong Xu, Jinxing Li	To construct small mobile networks without performance loss and address the over-fitting issues caused by the less abundant training datasets, this paper proposes a novel super sparse convolutional (SSC) kernel, and its corresponding network is called SSC-Net.
545	Block Belief Propagation for Parameter Learning in Markov Random Fields	You Lu, Zhiyuan Liu, Bert Huang	In this paper, we propose block belief propagation learning (BBPL), which uses block-coordinate updates of approximate marginals to compute approximate gradients, removing the need to compute inference on the entire graphical model.
546	Relation Structure-Aware Heterogeneous Information Network Embedding	Yuanfu Lu, Chuan Shi, Linmei Hu, Zhiyuan Liu	In this paper, we take the structural characteristics of heterogeneous relations into consideration and propose a novel Relation structure-aware Heterogeneous Information Network Embedding model (RHINE).
547	Scaling-Up Split-Merge MCMC with Locality Sensitive Sampling (LSS)	Chen Luo, Anshumali Shrivastava	We leverage some unique properties of weighted MinHash, which is a popular LSH, to design a novel class of split-merge proposals which are significantly more informative than random sampling but at the same time efficient to compute.
548	Orthogonality-Promoting Dictionary Learning via Bayesian Inference	Lei Luo, Jie Xu, Cheng Deng, Heng Huang	To address this problem, this paper presents a novel robust dictionary learning framework via Bayesian inference.
549	Robust Metric Learning on Grassmann Manifolds with Generalization Guarantees	Lei Luo, Jie Xu, Cheng Deng, Heng Huang	To address this issue, we propose a new robust formulation of metric learning.
550	Bias-Variance Trade-Off in Hierarchical Probabilistic Models Using Higher-Order Feature Interactions	Simon Luo, Mahito Sugiyama	In our study, we propose an efficient inference algorithm for the log-linear formulation of the higher-order Boltzmann machine using a combination of Gibbs sampling and annealed importance sampling.
551	Distributed PageRank Computation: An Improved Theoretical Study	Siqiang Luo	In this paper, we present improved distributed algorithms for computing PageRank.
552	A Comparative Analysis of Expected and Distributional Reinforcement Learning	Clare Lyle, Marc G. Bellemare, Pablo Samuel Castro	In this paper we begin the investigation into this fundamental question by analyzing the differences in the tabular, linear approximation, and non-linear approximation settings.
553	State-Augmentation Transformations for Risk-Sensitive Reinforcement Learning	Shuai Ma, Jia Yuan Yu	We propose three successively more general state-augmentation transformations (SATs), which preserve the reward sequences as well as the reward distributions and the optimal policy in risk-sensitive reinforcement learning.
554	LabelForest: Non-Parametric Semi-Supervised Learning for Activity Recognition	Yuchao Ma, Hassan Ghasemzadeh	To address these challenges, we introduce LabelForest 1, a novel non-parametric semi-supervised learning framework for activity recognition.
555	Complex Unitary Recurrent Neural Networks Using Scaled Cayley Transform	Kehelwala D. G. Maduranga, Kyle E. Helfrich, Qiang Ye	In this paper, we develop a unitary RNN architecture based on a complex scaled Cayley transform.
556	The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure	Saeed Mahloujifar, Dimitrios I. Diochnos, Mohammad Mahmoody	In this work, through a theoretical study, we investigate the adversarial risk and robustness of classifiers and draw a connection to the well-known phenomenon of “concentration of measure” in metric measure spaces.
557	A Distillation Approach to Data Efficient Individual Treatment Effect Estimation	Maggie Makar, Adith Swaminathan, Emre Kıcıman	In this work, we present Data Efficient Individual Treatment Effect Estimation (DEITEE), a method which exploits the idea that adjusting for confounding, and hence collecting information about confounders, is not necessary at test time.
558	DyS: A Framework for Mixture Models in Quantification	André Maletzke, Denis dos Reis, Everton Cherman, Gustavo Batista	In this paper, we generalize MM with a base framework called DyS: Distribution y-Similarity.
559	Towards Better Interpretability in Deep Q-Networks	Raghuram Mandyam Annasamy, Katia Sycara	In this paper we propose an interpretable neural network architecture for Q-learning which provides a global explanation of the model’s behavior using key-value memories, attention and reconstructible embeddings.
560	Cost-Sensitive Learning to Rank	Ryan McBride, Ke Wang, Zhouyang Ren, Wenyuan Li	We formulate the Cost-Sensitive Learning to Rank problem of learning to prioritize limited resources to mitigate the most costly outcomes.
561	A Two-Stream Mutual Attention Network for Semi-Supervised Biomedical Segmentation with Noisy Labels	Shaobo Min, Xuejin Chen, Zheng-Jun Zha, Feng Wu, Yongdong Zhang	In this paper, we propose a Two-Stream Mutual Attention Network (TSMAN) that weakens the influence of back-propagated gradients caused by incorrect labels, thereby rendering the network robust to unclean data.
562	A Probabilistic Derivation of LASSO and L12-Norm Feature Selections	Di Ming, Chris Ding, Feiping Nie	A Probabilistic Derivation of LASSO and L12-Norm Feature Selections
563	Cogra: Concept-Drift-Aware Stochastic Gradient Descent for Time-Series Forecasting	Kohei Miyaguchi, Hiroshi Kajino	In this paper, we present a concept-drift-aware stochastic gradient descent (Cogra), equipped with more theoretically-sound mean estimator called sequential mean tracker (SMT).
564	Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks	Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, Martin Grohe	Based on this, we propose a generalization of GNNs, so-called k-dimensional GNNs (k-GNNs), which can take higher-order graph structures at multiple scales into account.
565	ClusterGAN: Latent Space Clustering in Generative Adversarial Networks	Sudipto Mukherjee, Himanshu Asnani, Eugene Lin, Sreeram Kannan	In this paper, we propose ClusterGAN as a new mechanism for clustering using GANs.
566	Subspace Selection via DR-Submodular Maximization on Lattices	So Nakashima, Takanori Maehara	In this study, we focus on the fact that the set of subspaces forms a lattice, then formulate the problems as optimization problems on lattices. Using these results, we propose new solvable feature selection problems (generalized principal component analysis and quantum maximum cut problem), and improve the approximation ratio of the sparse dictionary selection problem in certain instances.
567	Number Sequence Prediction Problems for Evaluating Computational Powers of Neural Networks	Hyoungwook Nam, Segwang Kim, Kyomin Jung	Inspired by number series tests to measure human intelligence, we suggest number sequence prediction tasks to assess neural network models’ computational powers for solving algorithmic problems.
568	Efficient Counterfactual Learning from Bandit Feedback	Yusuke Narita, Shota Yasui, Kohei Yata	For log data generated by contextual bandit algorithms, we consider offline estimators for the expected reward from a counterfactual policy.
569	Multigrid Backprojection Super–Resolution and Deep Filter Visualization	Pablo Navarrete Michelini, Hanwen Liu, Dan Zhu	We introduce a novel deep–learning architecture for image upscaling by large factors (e.g. 4×, 8×) based on examples of pristine high–resolution images.
570	Biologically Motivated Algorithms for Propagating Local Target Representations	Alexander G. Ororbia, Ankur Mali	In this paper, we propose a learning algorithm called error-driven Local Representation Alignment (LRA-E), which has strong connections to predictive coding, a theory that offers a mechanistic way of describing neurocomputational machinery.
571	Determinantal Reinforcement Learning	Takayuki Osogami, Rudy Raymond	We propose the approach of using the determinant of a positive semidefinite matrix to approximate the action-value function in reinforcement learning, where we learn the matrix in a way that it represents the relevance and diversity of the actions.
572	Non-Parametric Transformation Networks for Learning General Invariances from Data	Dipan K. Pal, Marios Savvides	In this paper, we introduce a new class of deep convolutional architectures called Non-Parametric Transformation Networks (NPTNs) which can learn general invariances and symmetries directly from data.
573	Policy Optimization with Model-Based Explorations	Feiyang Pan, Qingpeng Cai, An-Xiang Zeng, Chun-Xiang Pan, Qing Da, Hualin He, Qing He, Pingzhong Tang	In this paper, we present a new technique to address the tradeoff between exploration and exploitation, which regards the difference between model-free and model-based estimations as a measure of exploration value.
574	Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition	Yu Pan, Jing Xu, Maolin Wang, Jinmian Ye, Fei Wang, Kun Bai, Zenglin Xu	To address this challenge, we propose a novel compact LSTM model, named as TR-LSTM, by utilizing the low-rank tensor ring decomposition (TRD) to reformulate the input-to-hidden transformation.
575	On Reinforcement Learning for Full-Length Game of StarCraft	Zhen-Jia Pang, Ruo-Ze Liu, Zhou-Yu Meng, Yi Zhang, Yang Yu, Tong Lu	In this paper, we investigate a set of techniques of reinforcement learning for the full-length game of StarCraft II.
576	Adversarial Dropout for Recurrent Neural Networks	Sungrae Park, Kyungwoo Song, Mingi Ji, Wonsung Lee, Il-Chul Moon	Specifically, the guided dropout used in this research is called as adversarial dropout, which adversarially disconnects neurons that are dominantly used to predict correct targets over time.
577	Trainable Undersampling for Class-Imbalance Learning	Minlong Peng, Qi Zhang, Xiaoyu Xing, Tao Gui, Xuanjing Huang, Yu-Gang Jiang, Keyu Ding, Zhigang Chen	In this work, we propose a meta-learning method built on the undersampling to address this issue.
578	Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets	M. Peréz-Ortiz, P. Tiňo, R. Mantiuk, C. Hervás-Martínez	This paper studies the effect of generating synthetic data by convex combination of patterns and the use of these as unsupervised information in a semi-supervised learning framework with support vector machines, avoiding thus the need to label synthetic examples.
579	Interpretable Preference Learning: A Game Theoretic Framework for Large Margin On-Line Feature and Rule Learning	Mirko Polato, Fabio Aiolli	In this work, game theory notions are injected into a preference learning framework.
580	Learning to Solve NP-Complete Problems: A Graph Neural Network for Decision TSP	Marcelo Prates, Pedro H. C. Avelar, Henrique Lemos, Luis C. Lamb, Moshe Y. Vardi	In this paper, we show that GNNs can learn to solve, with very little supervision, the decision variant of the Traveling Salesperson Problem (TSP), a highly relevant NP-Complete problem.
581	Robust Optimization over Multiple Domains	Qi Qian, Shenghuo Zhu, Jiasheng Tang, Rong Jin, Baigui Sun, Hao Li	In this work, we study the problem of learning a single model for multiple domains.
582	Composite Binary Decomposition Networks	You Qiaoben, Zheng Wang, Jianguo Li, Yinpeng Dong, Yu-Gang Jiang, Jun Zhu	In this paper, we propose the composite binary decomposition networks (CBDNet), which first compose real-valued tensor of each layer with a limited number of binary tensors, and then decompose some conditioned binary tensors into two low-rank binary tensors, so that the number of parameters and operations are greatly reduced comparing to the original ones.
583	Nearest-Neighbour-Induced Isolation Similarity and Its Impact on Density-Based Clustering	Xiaoyu Qin, Kai Ming Ting, Ye Zhu, Vincent CS Lee	We identify shortcomings of using a tree method to implement Isolation Similarity; and propose a nearest neighbour method instead.
584	Training Complex Models with Multi-Task Weak Supervision	Alexander Ratner, Braden Hancock, Jared Dunnmon, Frederic Sala, Shreyash Pandey, Christopher Ré	We propose a framework for integrating and modeling such weak supervision sources by viewing them as labeling different related sub-tasks of a problem, which we refer to as the multi-task weak supervision setting.
585	Explicitly Imposing Constraints in Deep Networks via Conditional Gradients Gives Improved Generalization and Faster Convergence	Sathya N. Ravi, Tuan Dinh, Vishnu Suresh Lokhande, Vikas Singh	In this paper, we revisit a classical first order scheme from numerical optimization, Conditional Gradients (CG), that has, thus far had limited applicability in training deep models.
586	Regularized Evolution for Image Classifier Architecture Search	Esteban Real, Alok Aggarwal, Yanping Huang, Quoc V. Le	To do this, we modify the tournament selection evolutionary algorithm by introducing an age property to favor the younger genotypes.
587	On Fair Cost Sharing Games in Machine Learning	Ievgen Redko, Charlotte Laclau	In this paper, we take a closer look at a special class of games, known as fair cost sharing games, from a machine learning perspective.
588	Deep Recurrent Survival Analysis	Kan Ren, Jiarui Qin, Lei Zheng, Zhengyu Yang, Weinan Zhang, Lin Qiu, Yong Yu	In this paper, we propose a Deep Recurrent Survival Analysis model which combines deep learning for conditional probability prediction at finegrained level of the data, and survival analysis for tackling the censorship.
589	RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-Based Recommendation	Pengjie Ren, Zhumin Chen, Jing Li, Zhaochun Ren, Jun Ma, Maarten de Rijke	In this paper, we incorporate a repeat-explore mechanism into neural networks and propose a new model, called RepeatNet, with an encoder-decoder structure.
590	Devil in the Details: Towards Accurate Single and Multiple Human Parsing	Tao Ruan, Ting Liu, Zilong Huang, Yunchao Wei, Shikui Wei, Yao Zhao	In this paper, we identify several useful properties, including feature resolution, global context information and edge details, and perform rigorous analyses to reveal how to leverage them to benefit the human parsing task.
591	Latent Multi-Task Architecture Learning	Sebastian Ruder, Joachim Bingel, Isabelle Augenstein, Anders Søgaard	In this work we present an approach that learns a latent multi-task architecture that jointly addresses (a)–(c).
592	How Many Pairwise Preferences Do We Need to Rank a Graph Consistently?	Aadirupa Saha, Rakesh Shivanna, Chiranjib Bhattacharyya	We consider the problem of optimal recovery of true ranking of n items from a randomly chosen subset of their pairwise preferences.
593	Covariate Shift Adaptation on Learning from Positive and Unlabeled Data	Tomoya Sakai, Nobuyuki Shimizu	In this paper, we address the PU learning problem under the covariate shift.
594	Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks	Patrick Schwab, Djordje Miladinovic, Walter Karlen	Here, we present a new approach to estimating feature importance with neural networks based on the idea of distributing the features of interest among experts in an attentive mixture of experts (AME).
595	Congestion Graphs for Automated Time Predictions	Arik Senderovich, J. Christopher Beck, Avigdor Gal, Matthias Weidlich	In this work, we focus on time prediction in congested systems, where entities share scarce resources.
596	Unsupervised Learning with Contrastive Latent Variable Models	Kristen A. Severson, Soumya Ghosh, Kenney Ng	Here, we present a probabilistic model for dimensionality reduction to discover signal that is enriched in the target dataset relative to the background dataset.
597	Sparse Reject Option Classifier Using Successive Linear Programming	Kulin Shah, Naresh Manwani	In this paper, we propose an approach for learning sparse reject option classifiers using double ramp loss Ldr.
598	Geometric Hawkes Processes with Graph Convolutional Recurrent Neural Networks	Jin Shang, Mingxuan Sun	In this paper, we propose the Geometric Hawkes Process (GHP) model to better correlate individual processes, by integrating Hawkes processes and a graph convolutional recurrent neural network.
599	MEAL: Multi-Model Ensemble via Adversarial Learning	Zhiqiang Shen, Zhankui He, Xiangyang Xue	In this paper, we present a method for compressing large, complex trained ensembles into a single network, where knowledge from a variety of trained deep neural networks (DNNs) is distilled and transferred to a single DNN.
600	Multi-View Anomaly Detection: Neighborhood in Locality Matters	Xiang-Rong Sheng, De-Chuan Zhan, Su Lu, Yuan Jiang	To address these issues, we propose the nearest neighborbased MUlti-View Anomaly Detection (MUVAD) approach.
601	Virtual-Taobao: Virtualizing Real-World Online Retail Environment for Reinforcement Learning	Jing-Cheng Shi, Yang Yu, Qing Da, Shi-Yong Chen, An-Xiang Zeng	To improve the simulation precision, we propose GAN-SD (GAN for Simulating Distributions) for customer feature generation with better matched distribution; we propose MAIL (Multiagent Adversarial Imitation Learning) for generating better generalizable customer actions.
602	Automatic Code Review by Learning the Revision of Source Code	Shu-Ting Shi, Ming Li, David Lo, Ferdian Thung, Xuan Huo	In this paper, we propose a novel deep model named DACE for automatic code review.
603	Sublinear Time Numerical Linear Algebra for Structured Matrices	Xiaofei Shi, David P. Woodruff	For the important case of autoregression with the polynomial kernel and arbitrary target vector b ∈ Rn, we obtain even faster algorithms.
604	Label Embedding with Partial Heterogeneous Contexts	Yaxin Shi, Donna Xu, Yuangang Pan, Ivor W. Tsang, Shirui Pan	In this paper, we propose a general Partial Heterogeneous Context Label Embedding (PHCLE) framework to address these challenges.
605	Evaluating Recommender System Stability with Influence-Guided Fuzzing	David Shriver, Sebastian Elbaum, Matthew B. Dwyer, David S. Rosenblum	In this work, we present an approach based on inferred models of influence that underlie recommender systems to guide the generation of dataset modifications to assess a recommender’s stability.
606	Sensitivity Analysis of Deep Neural Networks	Hai Shu, Hongtu Zhu	We introduce a novel perturbation manifold and its associated influence measure to quantify the effects of various perturbations on DNN classifiers.
607	Transferable Curriculum for Weakly-Supervised Domain Adaptation	Yang Shu, Zhangjie Cao, Mingsheng Long, Jianmin Wang	In this paper, we try to address two entangled challenges of weaklysupervised domain adaptation: sample noises of the source domain and distribution shift across domains. As such, for a particular target task, we simply collect the source domain with coarse labeling or corrupted data.
608	Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents	Aditya Siddhant, Anuj Goyal, Angeliki Metallinou	In this paper, we explore techniques to efficiently transfer the knowledge from these unlabeled utterances to improve model performance on Spoken Language Understanding (SLU) tasks.
609	Safe Policy Improvement with Baseline Bootstrapping in Factored Environments	Thiago D. Simão, Matthijs T. J. Spaan	We present a novel safe reinforcement learning algorithm that exploits the factored dynamics of the environment to become less conservative.
610	Composable Modular Reinforcement Learning	Christopher Simpkins, Charles Isbell	We solve this problem with a Q-learningbased command arbitration algorithm and demonstrate that it does not exhibit the same performance degradation as existing approaches to MRL, thereby supporting composability.
611	Hierarchical Context Enabled Recurrent Neural Network for Recommendation	Kyungwoo Song, Mingi Ji, Sungrae Park, Il-Chul Moon	Besides, we propose a hierarchical context-based gate structure to incorporate our interest drift assumption.
612	Diversity-Driven Extensible Hierarchical Reinforcement Learning	Yuhang Song, Jianyi Wang, Thomas Lukasiewicz, Zhenghua Xu, Mai Xu	Therefore, in this paper, we propose a diversitydriven extensible HRL (DEHRL), where an extensible and scalable framework is built and learned levelwise to realize HRL with multiple levels.
613	On the Persistence of Clustering Solutions and True Number of Clusters in a Dataset	Amber Srivastava, Mayank Baranwal, Srinivasa Salapaka	We show that the datasets where natural clusters are a priori known, the clustering solutions that identify the natural clusters are most persistent – in this way, this notion can be used to identify solutions with true number of clusters.
614	Soft Facial Landmark Detection by Label Distribution Learning	Kai Su, Xin Geng	Therefore, this paper proposes to associate a bivariate label distribution (BLD) to each landmark of an image.
615	Partial Multi-Label Learning by Low-Rank and Sparse Decomposition	Lijuan Sun, Songhe Feng, Tao Wang, Congyan Lang, Yi Jin	In this paper, in order to further improve the denoising capability of PML framework, we utilize the low-rank and sparse decomposition scheme and propose a novel Partial Multi-label Learning by Low-Rank and Sparse decomposition (PML-LRS) approach.
616	Multi-Precision Quantized Neural Networks via Encoding Decomposition of {-1,+1}	Qigong Sun, Fanhua Shang, Kang Yang, Xiufang Li, Yan Ren, Licheng Jiao	To address this issue, we propose a novel encoding scheme of using {−1, +1} to decompose quantized neural networks (QNNs) into multibranch binary networks, which can be efficiently implemented by bitwise operations (xnor and bitcount) to achieve model compression, computational acceleration and resource saving.
617	Non-Ergodic Convergence Analysis of Heavy-Ball Algorithms	Tao Sun, Penghang Yin, Dongsheng Li, Chun Huang, Lei Guan, Hao Jiang	In this paper, we revisit the convergence of the Heavy-ball method, and present improved convergence complexity results in the convex setting.
618	Network Structure and Transfer Behaviors Embedding via Deep Prediction Model	Xin Sun, Zenghui Song, Junyu Dong, Yongbo Yu, Claudia Plant, Christian Böhm	In this work, we propose a deep embedding framework to preserve the transfer possibilities among the network nodes.
619	Learning Vine Copula Models for Synthetic Data Generation	Yi Sun, Alfredo Cuesta-Infante, Kalyan Veeramachaneni	In this work, we formulate a vine structure learning problem with both vector and reinforcement learning representation.
620	Matrix Completion for Graph-Based Deep Semi-Supervised Learning	Fariborz Taherkhani, Hadi Kazemi, Nasser M. Nasrabadi	In this paper, we introduce a new iterative Graph-based Semi-Supervised Learning (GSSL) method to train a CNN-based classifier using a large amount of unlabeled data and a small amount of labeled data.
621	Variational Autoencoder with Implicit Optimal Priors	Hiroshi Takahashi, Tomoharu Iwata, Yuki Yamanaka, Masanori Yamada, Satoshi Yagi	With the proposed method, we introduce the density ratio trick to estimate this KL divergence without modeling the aggregated posterior explicitly.
622	Character n-Gram Embeddings to Improve RNN Language Models	Sho Takase, Jun Suzuki, Masaaki Nagata	This paper proposes a novel Recurrent Neural Network (RNN) language model that takes advantage of character information.
623	Coreset Stochastic Variance-Reduced Gradient with Application to Optimal Margin Distribution Machine	Zhi-Hao Tan, Teng Zhang, Wei Wang	In this paper, we provide perhaps the first coreset-based kernel-accelerating optimization method that has a linear convergence rate, which is much faster than existing approaches.
624	Refining Coarse-Grained Spatial Data Using Auxiliary Spatial Data Sets with Various Granularities	Yusuke Tanaka, Tomoharu Iwata, Toshiyuki Tanaka, Takeshi Kurashima, Maya Okawa, Hiroyuki Toda	We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets.
625	Cross-View Local Structure Preserved Diversity and Consensus Learning for Multi-View Unsupervised Feature Selection	Chang Tang, Xinzhong Zhu, Xinwang Liu, Lizhe Wang	In this paper, we propose a cross-view local structure preserved diversity and consensus semantic learning model for MV-UFS, termed CRV-DCL briefly, to address these issues.
626	An Integral Tag Recommendation Model for Textual Content	Shijie Tang, Yuan Yao, Suwei Zhang, Feng Xu, Tianxiao Gu, Hanghang Tong, Xiaohui Yan, Jian Lu	In this paper, we identify three pillars that impact the accuracy of tag recommendation: (1) sequential text modeling meaning that the intrinsic sequential ordering as well as different areas of text might have an important implication on the corresponding tag(s) , (2) tag correlation meaning that the tags for a certain piece of textual content are often semantically correlated with each other, and (3) content-tag overlapping meaning that the vocabularies of content and tags are overlapped.
627	Self-Paced Active Learning: Query the Right Thing at the Right Time	Ying-Peng Tang, Sheng-Jun Huang	In this paper, we propose a self-paced active learning approach to simultaneously consider the potential value and easiness of an instance, and try to train the model with least cost by querying the right thing at the right time.
628	A Radical-Aware Attention-Based Model for Chinese Text Classification	Hanqing Tao, Shiwei Tong, Hongke Zhao, Tong Xu, Binbin Jin, Qi Liu	For better representing the Chinese text and then implementing Chinese text classification, we propose a novel Radicalaware Attention-based Four-Granularity (RAFG) model to take full advantages of Chinese characters, words, characterlevel radicals, word-level radicals simultaneously.
629	Optimization of Hierarchical Regression Model with Application to Optimizing Multi-Response Regression K-ary Trees	Pooya Tavallali, Peyman Tavallali, Mukesh Singhal	This paper presents a meta-algorithm capable of minimizing the regression loss function, thus, improving the accuracy of any given hierarchical model, such as k-ary regression trees.
630	Holographic Factorization Machines for Recommendation	Yi Tay, Shuai Zhang, Anh Tuan Luu, Siu Cheung Hui, Lina Yao, Tran Dang Quang Vinh	This paper proposes Holographic Factorization Machines (HFM), a new novel method of enhancing the representation capability of FMs without increasing its parameter size.
631	Clipped Matrix Completion: A Remedy for Ceiling Effects	Takeshi Teshima, Miao Xu, Issei Sato, Masashi Sugiyama	We consider the problem of recovering a low-rank matrix from its clipped observations.
632	A Non–Convex Optimization Approach to Correlation Clustering	Erik Thiel, Morteza Haghir Chehreghani, Devdatt Dubhashi	We develop a non-convex optimization approach to correlation clustering using the Frank-Wolfe (FW) framework.
633	Learning Competitive and Discriminative Reconstructions for Anomaly Detection	Kai Tian, Shuigeng Zhou, Jianping Fan, Jihong Guan	In this paper, we take the discriminative information implied in unlabeled data into consideration and propose a new method for anomaly detection that can learn the labels of unlabelled data directly.
634	Natural Option Critic	Saket Tiwari, Philip S. Thomas	In this paper we show how the option-critic architecture can be extended to estimate the natural gradient (Amari 1998) of the expected discounted return.
635	Learning Triggers for Heterogeneous Treatment Effects	Christopher Tran, Elena Zheleva	In this paper we define and study a variant of this problem in which an individuallevel threshold in treatment needs to be reached, in order to trigger an effect.
636	Improving GAN with Neighbors Embedding and Gradient Matching	Ngoc-Trung Tran, Tuan-Anh Bui, Ngai-Man Cheung	We propose two new techniques for training Generative Adversarial Networks (GANs) in the unsupervised setting.
637	Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data	Amin Vahedian, Xun Zhou, Ling Tong, W. Nick Street, Yanhua Li	We propose a two-stage framework (DILSA), where a deep learning model combined with survival analysis is developed to predict the probability of a dispersal event and its demand volume.
638	Automatic Bayesian Density Analysis	Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera	As a result, supervision from statisticians is usually needed to find the right model for the data.
639	Robust Anomaly Detection in Videos Using Multilevel Representations	Hung Vu, Tu Dinh Nguyen, Trung Le, Wei Luo, Dinh Phung	This paper introduces a framework of robust anomaly detection using multilevel representations of both intensity and motion data.
640	CAMO: A Collaborative Ranking Method for Content Based Recommendation	Chengwei Wang, Tengfei Zhou, Chen Chen, Tianlei Hu, Gang Chen	To fill the gaps, we propose a recommender named CAMO 1.
641	Video Inpainting by Jointly Learning Temporal Structure and Spatial Details	Chuan Wang, Haibin Huang, Xiaoguang Han, Jue Wang	We present a new data-driven video inpainting method for recovering missing regions of video frames.
642	Bounding Uncertainty for Active Batch Selection	Hanmo Wang, Runwu Zhou, Yi-Dong Shen	In this paper, we discover that the two approaches both have shortcomings in the initial stage of BMAL.
643	Adversarial Binary Collaborative Filtering for Implicit Feedback	Haoyu Wang, Nan Shao, Defu Lian	To this end, we propose two novel methods to relax the binarization based on the error function and Gumbel trick so that the generative model can be optimized by many popular solvers, such as SGD and ADMM.
644	Theoretical Analysis of Label Distribution Learning	Jing Wang, Xin Geng	In this paper, we rethink LDL from theoretical aspects, towards analyzing learnability of LDL.
645	Orderly Subspace Clustering	Jing Wang, Atsushi Suzuki, Linchuan Xu, Feng Tian, Liang Yang, Kenji Yamanishi	We propose an orderly subspace clustering approach with a novel regularization term.
646	SVM-Based Deep Stacking Networks	Jingyuan Wang, Kai Feng, Junjie Wu	In this paper, we propose a novel SVM-based Deep Stacking Network (SVM-DSN), which uses the DSN architecture to organize linear SVM classifiers for deep learning.
647	An Efficient Approach to Informative Feature Extraction from Multimodal Data	Lichen Wang, Jiaxiang Wu, Shao-Lun Huang, Lizhong Zheng, Xiangxiang Xu, Lin Zhang, Junzhou Huang	To address this problem, this paper proposes Soft-HGR, a novel framework to extract informative features from multiple data modalities.
648	Scalable Distributed DL Training: Batching Communication and Computation	Shaoqi Wang, Aidi Pi, Xiaobo Zhou	In this paper, we propose and design iBatch, a novel communication approach that batches parameter communication and forward computation to overlap them with each other.
649	HyperAdam: A Learnable Task-Adaptive Adam for Network Training	Shipeng Wang, Jian Sun, Zongben Xu	In this paper, a new optimizer, dubbed as HyperAdam, is proposed that combines the idea of “learning to optimize” and traditional Adam optimizer.
650	A Sharper Generalization Bound for Divide-and-Conquer Ridge Regression	Shusen Wang	There have been constantfactor bounds in the prior works, their sample complexities have a quadratic dependence on d, which does not match the setting of most real-world problems.
651	Robustness Can Be Cheap: A Highly Efficient Approach to Discover Outliers under High Outlier Ratios	Siqi Wang, En Zhu, Xiping Hu, Xinwang Liu, Qiang Liu, Jianping Yin, Fei Wang	This paper proposes a Low-rank based Efficient Outlier Detection (LEOD) framework to achieve favorable robustness against high outlier ratios with much cheaper computations.
652	SCNN: A General Distribution Based Statistical Convolutional Neural Network with Application to Video Object Detection	Tianchen Wang, Jinjun Xiong, Xiaowei Xu, Yiyu Shi	In this paper, we propose a novel statistical convolutional neural network (SCNN), which extends existing CNN architectures but operates directly on correlated distributions rather than deterministic numbers.
653	Explainable Reasoning over Knowledge Graphs for Recommendation	Xiang Wang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, Tat-Seng Chua	In this paper, we contribute a new model named Knowledgeaware Path Recurrent Network (KPRN) to exploit knowledge graph for recommendation.
654	Hyperbolic Heterogeneous Information Network Embedding	Xiao Wang, Yiding Zhang, Chuan Shi	In this paper, we make the first effort toward HIN embedding in hyperbolic spaces.
655	Transferable Attention for Domain Adaptation	Ximei Wang, Liang Li, Weirui Ye, Mingsheng Long, Jianmin Wang	To this end, we present Transferable Attention for Domain Adaptation (TADA), focusing our adaptation model on transferable regions or images.
656	Multiple Independent Subspace Clusterings	Xing Wang, Jun Wang, Carlotta Domeniconi, Guoxian Yu, Guoqiang Xiao, Maozu Guo	To this end, we provide a two-stage approach called MISC (Multiple Independent Subspace Clusterings).
657	Deep Metric Learning by Online Soft Mining and Class-Aware Attention	Xinshao Wang, Yang Hua, Elyor Kodirov, Guosheng Hu, Neil M. Robertson	In this work, we identify two critical limitations of the sample mining methods, and provide solutions for both of them.
658	Universal Approximation Property and Equivalence of Stochastic Computing-Based Neural Networks and Binary Neural Networks	Yanzhi Wang, Zheng Zhan, Liang Zhao, Jian Tang, Siyue Wang, Jiayu Li, Bo Yuan, Wujie Wen, Xue Lin	In order to address these concerns, in this paper we prove that the “ideal” SCNNs and BNNs satisfy the universal approximation property with probability 1 (due to the stochastic behavior), which is a new angle from the original approximation property.
659	Non-Autoregressive Machine Translation with Auxiliary Regularization	Yiren Wang, Fei Tian, Di He, Tao Qin, ChengXiang Zhai, Tie-Yan Liu	In this paper, we propose to address these two problems by improving the quality of decoder hidden representations via two auxiliary regularization terms in the training process of an NAT model.
660	Learning Compact Model for Large-Scale Multi-Label Data	Tong Wei, Yu-Feng Li	This paper proposes a POP (joint label and feature Parameter OPtimization) method.
661	Unified Embedding Alignment with Missing Views Inferring for Incomplete Multi-View Clustering	Jie Wen, Zheng Zhang, Yong Xu, Bob Zhang, Lunke Fei, Hong Liu	To address these issues, this paper proposes a Unified Embedding Alignment Framework (UEAF) for robust incomplete multi-view clustering.
662	Exploiting Local Feature Patterns for Unsupervised Domain Adaptation	Jun Wen, Risheng Liu, Nenggan Zheng, Qian Zheng, Zhefeng Gong, Junsong Yuan	In this paper, we present a method for learning domain-invariant local feature patterns and jointly aligning holistic and local feature statistics.
663	RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series	Qingsong Wen, Jingkun Gao, Xiaomin Song, Liang Sun, Huan Xu, Shenghuo Zhu	In the paper, we propose a novel and generic time series decomposition algorithm to address these challenges.
664	Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation	Florian Wenzel, Théo Galy-Fajou, Christan Donner, Marius Kloft, Manfred Opper	We propose a scalable stochastic variational approach to GP classification building on Pólya-Gamma data augmentation and inducing points.
665	How Does Knowledge of the AUC Constrain the Set of Possible Ground-Truth Labelings?	Jacob Whitehill	For binary classification tasks in particular, one of the most common accuracy metrics is the Area Under the ROC Curve (AUC), and in this paper we explore the mathematical structure of how the AUC is computed from an n-vector of real-valued “guesses” with respect to the ground-truth labels.
666	Uncovering Specific-Shape Graph Anomalies in Attributed Graphs	Nannan Wu, Wenjun Wang, Feng Chen, Jianxin Li, Bo Li, Jinpeng Huai	This paper proposes a nonlinear approach to specific-shape graph anomaly detection.
667	Point Cloud Processing via Recurrent Set Encoding	Pengxiang Wu, Chao Chen, Jingru Yi, Dimitris Metaxas	We present a new permutation-invariant network for 3D point cloud processing.
668	Improving Domain-Specific Classification by Collaborative Learning with Adaptation Networks	Si Wu, Jian Zhong, Wenming Cao, Rui Li, Zhiwen Yu, Hau-San Wong	In order to improve the performance in inferring target labels, we propose a targetspecific network which is capable of learning collaboratively with a domain adaptation network, instead of directly minimizing domain discrepancy.
669	Modelling of Bi-Directional Spatio-Temporal Dependence and Users’ Dynamic Preferences for Missing POI Check-In Identification	Dongbo Xi, Fuzhen Zhuang, Yanchi Liu, Jingjing Gu, Hui Xiong, Qing He	To this end, in this paper, we develop a model, named Bi-STDDP, which can integrate bi-directional spatio-temporal dependence and users’ dynamic preferences, to identify the missing POI check-in where a user has visited at a specific time.
670	Tied Transformers: Neural Machine Translation with Shared Encoder and Decoder	Yingce Xia, Tianyu He, Xu Tan, Fei Tian, Di He, Tao Qin	We empirically verify our framework for both supervised NMT and unsupervised NMT: we achieve a 35.52 BLEU score on IWSLT 2014 German to English translation, 28.98/29.89 BLEU scores on WMT 2014 English to German translation without/with monolingual data, and a 22.05 BLEU score on WMT 2016 unsupervised German to English translation.
671	Bayesian Deep Collaborative Matrix Factorization	Teng Xiao, Shangsong Liang, Weizhou Shen, Zaiqiao Meng	In this paper, we propose a Bayesian Deep Collaborative Matrix Factorization (BDCMF) algorithm for collaborative filtering (CF).
672	RS3CIS: Robust Single-Step Spectral Clustering with Intrinsic Subspace	Yun Xiao, Pengzhen Ren, Zhihui Li, Xiaojiang Chen, Xin Wang, Dingyi Fang	In order to deal with this challenge, a new Robust Single-Step Spectral Clustering with Intrinsic Subspace (RS3CIS) method is proposed in this paper.
673	Understanding Persuasion Cascades in Online Product Rating Systems	Hong Xie, Yongkun Li, John C.S. Lui	This paper investigates: (1) How does the persuasion cascade influence the product quality estimation accuracy?
674	Learning Dynamic Generator Model by Alternating Back-Propagation through Time	Jianwen Xie, Ruiqi Gao, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu	This paper studies the dynamic generator model for spatialtemporal processes such as dynamic textures and action sequences in video data.
675	Multi-View Multi-Instance Multi-Label Learning Based on Collaborative Matrix Factorization	Yuying Xing, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Zili Zhang, Maozu Guo	\ In this paper, we propose a collaborative matrix factorization based solution called M3Lcmf.
676	SpHMC: Spectral Hamiltonian Monte Carlo	Haoyi Xiong, Kafeng Wang, Jiang Bian, Zhanxing Zhu, Cheng-Zhong Xu, Zhishan Guo, Jun Huan	Stochastic Gradient Hamiltonian Monte Carlo (SGHMC) methods have been widely used to sample from certain probability distributions, incorporating (kernel) density derivatives and/or given datasets.
677	Modeling Local Dependence in Natural Language with Multi-Channel Recurrent Neural Networks	Chang Xu, Weiran Huang, Hongwei Wang, Gang Wang, Tie-Yan Liu	In this paper, we propose an improved variant of RNN, Multi-Channel RNN (MC-RNN), to dynamically capture and leverage local semantic structure information.
678	Hierarchical Classification Based on Label Distribution Learning	Changdong Xu, Xin Geng	This paper proposes a novel method to deal with the small training set issue.
679	Embedding-Based Complex Feature Value Coupling Learning for Detecting Outliers in Non-IID Categorical Data	Hongzuo Xu, Yongjun Wang, Zhiyue Wu, Yijie Wang	This paper introduces a novel unsupervised embedding-based complex value coupling learning framework EMAC and its instance SCAN to address these issues.
680	Dueling Bandits with Qualitative Feedback	Liyuan Xu, Junya Honda, Masashi Sugiyama	In this paper1, we propose such direct algorithms for the QDB problem.
681	Partial Label Learning via Label Enhancement	Ning Xu, Jiaqi Lv, Xin Geng	In this paper, a new partial label learning strategy named PL-LE is proposed to learn from partial label examples via label enhancement.
682	Data-Distortion Guided Self-Distillation for Deep Neural Networks	Ting-Bing Xu, Cheng-Lin Liu	In this paper, we design a more elegant self-distillation mechanism to transfer knowledge between different distorted versions of same training data without the reliance on accompanying models.
683	Task-Driven Common Representation Learning via Bridge Neural Network	Yao Xu, Xueshuang Xiang, Meiyu Huang	This paper introduces a novel deep learning based method, named bridge neural network (BNN) to dig the potential relationship between two given data sources task by task.
684	Self-Ensembling Attention Networks: Addressing Domain Shift for Semantic Segmentation	Yonghao Xu, Bo Du, Lefei Zhang, Qian Zhang, Guoli Wang, Liangpei Zhang	Besides, since different regions in the image usually correspond to different levels of domain gap, we introduce the attention mechanism into the proposed framework to generate attention-aware features, which are further utilized to guide the calculation of consistency loss in the target domain.
685	Active Learning of Multi-Class Classification Models from Ordered Class Sets	Yanbing Xue, Milos Hauskrecht	In this paper, we study the problem of learning multi-class classification models from a limited set of labeled examples obtained from human annotator.
686	Frame and Feature-Context Video Super-Resolution	Bo Yan, Chuming Lin, Weimin Tan	In this paper, we propose a fully end-to-end trainable frame and feature-context video super-resolution (FFCVSR) network that consists of two key sub-networks: local network and context network, where the first one explicitly utilizes a sequence of consecutive LR frames to generate local feature and local SR frame, and the other combines the outputs of local network and the previously estimated HR frames and features to super-resolve the subsequent frame.
687	Oversampling for Imbalanced Data via Optimal Transport	Yuguang Yan, Mingkui Tan, Yanwu Xu, Jiezhang Cao, Michael Ng, Huaqing Min, Qingyao Wu	In this paper, relying on optimal transport (Villani 2008), we propose an oversampling method by exploiting global geometric information of data to make synthetic samples follow a similar distribution to that of minority class samples.
688	Cross-Domain Visual Representations via Unsupervised Graph Alignment	Baoyao Yang, Pong C. Yuen	To avoid the misclassification/misidentification due to the difference in distribution structures, this paper proposes a novel unsupervised graph alignment method that aligns both data representations and distribution structures across the source and target domains.
689	Weighted Oblique Decision Trees	Bin-Bin Yang, Song-Qing Shen, Wei Gao	This work presents the Weighted Oblique Decision Tree (WODT) based on continuous optimization with random initialization.
690	Training Deep Neural Networks in Generations: A More Tolerant Teacher Educates Better Students	Chenglin Yang, Lingxi Xie, Siyuan Qiao, Alan L. Yuille	We focus on the problem of training a deep neural network in generations.
691	Confidence Weighted Multitask Learning	Peng Yang, Peilin Zhao, Jiayu Zhou, Xin Gao	To remedy this issue, we propose a confidence weighted multitask learning algorithm, which maintains a Gaussian distribution over each task model to guide online learning process.
692	Unsupervised Fake News Detection on Social Media: A Generative Approach	Shuo Yang, Kai Shu, Suhang Wang, Renjie Gu, Fan Wu, Huan Liu	To solve the inference problem, we propose an efficient collapsed Gibbs sampling approach to infer the truths of news and the users’ credibility without any labelled data.
693	Deep Robust Unsupervised Multi-Modal Network	Yang Yang, Yi-Feng Wu, De-Chuan Zhan, Zhi-Bin Liu, Yuan Jiang	In this paper, we propose a novel Deep Robust Unsupervised Multi-modal Network structure (DRUMN) for solving this real problem within a unified framework.
694	Learning Personalized Attribute Preference via Multi-Task AUC Optimization	Zhiyong Yang, Qianqian Xu, Xiaochun Cao, Qingming Huang	Theoretically, we propose a novel closed-form solution for one of our non-convex subproblem, which leads to provable convergence behaviors.
695	Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction	Huaxiu Yao, Xianfeng Tang, Hua Wei, Guanjie Zheng, Zhenhui Li	In this paper, we make two important observations: (1) the spatial dependencies between locations are dynamic; and (2) the temporal dependency follows daily and weekly pattern but it is not strictly periodic for its dynamic temporal shifting.
696	Balanced Sparsity for Efficient DNN Inference on GPU	Zhuliang Yao, Shijie Cao, Wencong Xiao, Chen Zhang, Lanshun Nie	In this paper, we propose a novel fine-grained sparsity approach, Balanced Sparsity, to achieve high model accuracy with commercial hardwares efficiently.
697	Iterative Classroom Teaching	Teresa Yeo, Parameswaran Kamalaruban, Adish Singla, Arpit Merchant, Thibault Asselborn, Louis Faucon, Pierre Dillenbourg, Volkan Cevher	We consider the machine teaching problem in a classroom-like setting wherein the teacher has to deliver the same examples to a diverse group of students.
698	Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning	Hao Yu, Sen Yang, Shenghuo Zhu	This paper provides a thorough and rigorous theoretical study on why model averaging can work as well as parallel mini-batch SGD with significantly less communication overhead.
699	Network Recasting: A Universal Method for Network Architecture Transformation	Joonsang Yu, Sungbum Kang, Kiyoung Choi	This paper proposes network recasting as a general method for network architecture transformation.
700	Multi-Order Attentive Ranking Model for Sequential Recommendation	Lu Yu, Chuxu Zhang, Shangsong Liang, Xiangliang Zhang	In this paper, we propose a Multi-order Attentive Ranking Model (MARank) to unify both individual- and union-level item interaction into preference inference model from multiple views.
701	Interpreting Deep Models for Text Analysis via Optimization and Regularization Methods	Hao Yuan, Yongjun Chen, Xia Hu, Shuiwang Ji	In this work, we propose an approach to investigate the meaning of hidden neurons of the convolutional neural network (CNN) models.
702	f-Similarity Preservation Loss for Soft Labels: A Demonstration on Cross-Corpus Speech Emotion Recognition	Biqiao Zhang, Yuqing Kong, Georg Essl, Emily Mower Provost	In this paper, we propose a Deep Metric Learning (DML) approach that supports soft labels.
703	Partially Observable Multi-Sensor Sequential Change Detection: A Combinatorial Multi-Armed Bandit Approach	Chen Zhang, Steven C.H. Hoi	In this paper, we present the first online learning study to tackle POMSCD in a systemic and rigorous way.
704	Active Mini-Batch Sampling Using Repulsive Point Processes	Cheng Zhang, Cengiz öztireli, Stephan Mandt, Giampiero Salvi	In particular, we propose and investigate Poisson Disk sampling—frequently encountered in the computer graphics community—for this task.
705	Learning Set Functions with Limited Complementarity	Hanrui Zhang	We study PMAC-learning of real-valued set functions with limited complementarity.
706	RecurJac: An Efficient Recursive Algorithm for Bounding Jacobian Matrix of Neural Networks and Its Applications	Huan Zhang, Pengchuan Zhang, Cho-Jui Hsieh	In this paper, we propose a recursive algorithm, RecurJac, to compute both upper and lower bounds for each element in the Jacobian matrix of a neural network with respect to network’s input, and the network can contain a wide range of activation functions.
707	A Powerful Global Test Statistic for Functional Statistical Inference	Jingwen Zhang, Joseph Ibrahim, Tengfei Li, Hongtu Zhu	We propose a functional projection regression model and an associated global test statistic to aggregate relatively weak signals across the domain of functional data, while reducing the dimension.
708	Interactive Attention Transfer Network for Cross-Domain Sentiment Classification	Kai Zhang, Hefu Zhang, Qi Liu, Hongke Zhao, Hengshu Zhu, Enhong Chen	In order to better solve this problem, we propose an Interactive Attention Transfer Network (IATN) for crossdomain sentiment classification.
709	Learning to Communicate and Solve Visual Blocks-World Tasks	Qi Zhang, Richard Lewis, Satinder Singh, Edmund Durfee	Our contributions are (a) the introduction of a task domain for studying emergent communication that is both challenging and affords useful analyses of the emergent protocols; (b) an empirical comparison of the interpolation and extrapolation performance of training via supervised, (contextual) Bandit, and reinforcement learning; and (c) evidence for the emergence of interesting linguistic properties in the RL agent protocol that are distinct from the other two.
710	ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search	Shangtong Zhang, Hengshuai Yao	In this paper, we propose an actor ensemble algorithm, named ACE, for continuous control with a deterministic policy in reinforcement learning.
711	QUOTA: The Quantile Option Architecture for Reinforcement Learning	Shangtong Zhang, Hengshuai Yao	In this paper, we propose the Quantile Option Architecture (QUOTA) for exploration based on recent advances in distributional reinforcement learning (RL).
712	Hashtag Recommendation for Photo Sharing Services	Suwei Zhang, Yuan Yao, Feng Xu, Hanghang Tong, Xiaohui Yan, Jian Lu	In this paper, we propose an integral and effective hashtag recommendation approach for photo sharing services.
713	Find Me if You Can: Deep Software Clone Detection by Exploiting the Contest between the Plagiarist and the Detector	Yan-Ya Zhang, Ming Li	In this paper, we propose a novel clone detection approach, namely ACD, to mimic the adversarial process between the plagiarist and the detector, which enables us to not only build strong a clone detector but also model the behavior of the plagiarists.
714	CAFE: Adaptive VDI Workload Prediction with Multi-Grained Features	Yao Zhang, Wen-Ping Fan, Xuan Wu, Hua Chen, Bin-Yang Li, Min-Ling Zhang	In this paper, a novel proactive resource management approach is proposed which aims to predict VDI pool workload adaptively by utilizing CoArse to Fine historical dEscriptive (CAFE) features.
715	Bayesian Graph Convolutional Neural Networks for Semi-Supervised Classification	Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Ustebay	In this paper we adopt a Bayesian approach, viewing the observed graph as a realization from a parametric family of random graphs.
716	Learning (from) Deep Hierarchical Structure among Features	Yu Zhang, Lei Han	In this paper, we propose a Deep Hierarchical Structure (DHS) method to handle the hierarchical structure of an arbitrary height with a convex objective function.
717	Learning Uniform Semantic Features for Natural Language and Programming Language Globally, Locally and Sequentially	Yudong Zhang, Wenhao Zheng, Ming Li	In this paper, we build a novel and general-purpose feature learning framework called UniEmbed, to uniformly learn comprehensive semantic representation for both natural language and programming language.
718	SADIH: Semantic-Aware DIscrete Hashing	Zheng Zhang, Guo-sen Xie, Yang Li, Sheng Li, Zi Huang	To overcome these challenges, we propose a unified Semantic-Aware DIscrete Hashing (SADIH) framework, which aims to directly embed the transformed semantic information into the asymmetric similarity approximation and discriminative hashing function learning.
719	Submodular Optimization over Streams with Inhomogeneous Decays	Junzhou Zhao, Shuo Shang, Pinghui Wang, John C.S. Lui, Xiangliang Zhang	This work formulates the SSO-ID problem and presents three algorithms: BASIC-STREAMING is a basic streaming algorithm that achieves an (1/2 − ɛ) approximation factor; HISTAPPROX improves the efficiency significantly and achieves an (1/3 − ɛ) approximation factor; HISTSTREAMING is a streaming version of HISTAPPROX and uses heuristics to further improve the efficiency.
720	The Adversarial Attack and Detection under the Fisher Information Metric	Chenxiao Zhao, P. Thomas Fletcher, Mixue Yu, Yaxin Peng, Guixu Zhang, Chaomin Shen	In this paper, using information geometry, we provide a reasonable explanation for the vulnerability of deep learning models.
721	Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation	Pengpeng Zhao, Haifeng Zhu, Yanchi Liu, Jiajie Xu, Zhixu Li, Fuzhen Zhuang, Victor S. Sheng, Xiaofang Zhou	To this end, in this paper, we propose a new Spatio-Temporal Gated Network (STGN) by enhancing long-short term memory network, where spatio-temporal gates are introduced to capture the spatio-temporal relationships between successive checkins.
722	InfoVAE: Balancing Learning and Inference in Variational Autoencoders	Shengjia Zhao, Jiaming Song, Stefano Ermon	We again identify the cause in existing training criteria and propose a new class of objectives (Info-VAE) that mitigate these problems.
723	Self-Adversarially Learned Bayesian Sampling	Yang Zhao, Jianyi Zhang, Changyou Chen	In this paper, we propose a novel self-adversarial learning framework that automatically learns a conditional generator to mimic the behavior of a Markov kernel (transition kernel).
724	Biomedical Image Segmentation via Representative Annotation	Hao Zheng, Lin Yang, Jianxu Chen, Jun Han, Yizhe Zhang, Peixian Liang, Zhuo Zhao, Chaoli Wang, Danny Z. Chen	In this paper, we propose representative annotation (RA), a new deep learning framework for reducing annotation effort in biomedical image segmentation.
725	A New Ensemble Learning Framework for 3D Biomedical Image Segmentation	Hao Zheng, Yizhe Zhang, Lin Yang, Peixian Liang, Zhuo Zhao, Chaoli Wang, Danny Z. Chen	In this paper, we propose a new ensemble learning framework for 3D biomedical image segmentation that combines the merits of 2D and 3D models.
726	Understanding VAEs in Fisher-Shannon Plane	Huangjie Zheng, Jiangchao Yao, Ya Zhang, Ivor W. Tsang, Jia Wang	In this paper, we investigate VAEs in the Fisher-Shannon plane, and demonstrate that the representation learning and the log-likelihood estimation are intrinsically related to these two information quantities.
727	Capacity Control of ReLU Neural Networks by Basis-Path Norm	Shuxin Zheng, Qi Meng, Huishuai Zhang, Wei Chen, Nenghai Yu, Tie-Yan Liu	Motivated by this, we propose a new norm Basis-path Norm based on a group of linearly independent paths to measure the capacity of neural networks more accurately.
728	Self-Supervised Mixture-of-Experts by Uncertainty Estimation	Zhuobin Zheng, Chun Yuan, Xinrui Zhu, Zhihui Lin, Yangyang Cheng, Cheng Shi, Jiahui Ye	In this paper, we propose SelfSupervised Mixture-of-Experts (SUM), an effective algorithm driven by predictive uncertainty estimation for multitask RL.
729	Deep Interest Evolution Network for Click-Through Rate Prediction	Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, Kun Gai	In this paper, we propose a novel model, named Deep Interest Evolution Network (DIEN), for CTR prediction.
730	An Efficient Compressive Convolutional Network for Unified Object Detection and Image Compression	Xichuan Zhou, Lang Xu, Shujun Liu, Yingcheng Lin, Lei Zhang, Cheng Zhuo	This paper addresses the challenge of designing efficient framework for real-time object detection and image compression.
731	Communication-Optimal Distributed Dynamic Graph Clustering	Chun Jiang Zhu, Tan Zhu, Kam-Yiu Lam, Song Han, Jinbo Bi	We propose communication-efficient algorithms for two well-established communication models namely the message passing and the blackboard models.
732	A Domain Generalization Perspective on Listwise Context Modeling	Lin Zhu, Yihong Chen, Bowen He	In this paper, we propose a domain generalization strategy to tackle this problem.
733	DAN: Deep Attention Neural Network for News Recommendation	Qiannan Zhu, Xiaofei Zhou, Zeliang Song, Jianlong Tan, Li Guo	In this paper, taking full advantages of convolution neural network (CNN), recurrent neural network (RNN) and attention mechanism, we propose a deep attention neural network DAN for news recommendation.
734	Residual Invertible Spatio-Temporal Network for Video Super-Resolution	Xiaobin Zhu, Zhuangzi Li, Xiao-Yu Zhang, Changsheng Li, Yaqi Liu, Ziyu Xue	In this paper, we propose a novel end-to-end architecture, called Residual Invertible Spatio-Temporal Network (RISTN) for video super-resolution.
735	Aligning Domain-Specific Distribution and Classifier for Cross-Domain Classification from Multiple Sources	Yongchun Zhu, Fuzhen Zhuang, Deqing Wang	To solve these problems, we propose a new framework with two alignment stages for MUDA which not only respectively aligns the distributions of each pair of source and target domains in multiple specific feature spaces, but also aligns the outputs of classifiers by utilizing the domainspecific decision boundaries.
736	Consensus Adversarial Domain Adaptation	Han Zou, Yuxun Zhou, Jianfei Yang, Huihan Liu, Hari Prasanna Das, Costas J. Spanos	We propose a novel domain adaptation framework, namely Consensus Adversarial Domain Adaptation (CADA), that gives freedom to both target encoder and source encoder to embed data from both domains into a common domaininvariant feature space until they achieve consensus during adversarial learning.
737	Verification of RNN-Based Neural Agent-Environment Systems	Michael E. Akintunde, Andreea Kevorchian, Alessio Lomuscio, Edoardo Pirovano	We introduce agent-environment systems where the agent is stateful and executing a ReLU recurrent neural network.
738	Bayesian Execution Skill Estimation	Christopher Archibald, Delma Nieves-Rivera	In this paper, we address the problem of estimating the execution skill of an agent given observations of that agent acting in a domain. We previously introduced this problem and demonstrated that estimating an agent’s execution skill is possible under certain conditions.
739	Consensus in Opinion Formation Processes in Fully Evolving Environments	Vincenzo Auletta, Angelo Fanelli, Diodato Ferraioli	Motivated by the observation that innate beliefs, stubbornness levels and even social relations can co-evolve together with the expressed opinions, we present a new model of opinion formation where the dynamics runs in a co-evolving environment.
740	An Abstraction-Based Method for Verifying Strategic Properties in Multi-Agent Systems with Imperfect Information	Francesco Belardinelli, Alessio Lomuscio, Vadim Malvone	To this end, we develop a three-valued semantics for concurrent game structures upon which we define an abstraction method.
741	A Generic Approach to Accelerating Belief Propagation Based Incomplete Algorithms for DCOPs via a Branch-and-Bound Technique	Ziyu Chen, Xingqiong Jiang, Yanchen Deng, Dingding Chen, Zhongshi He	In this paper, we present a generic and easy-touse method based on a branch-and-bound technique to solve the issue, called Function Decomposing and State Pruning (FDSP).
742	Distributed Community Detection via Metastability of the 2-Choices Dynamics	Emilio Cruciani, Emanuele Natale, Giacomo Scornavacca	We investigate the behavior of a simple majority dynamics on networks of agents whose interaction topology exhibits a community structure.
743	Successor Features Based Multi-Agent RL for Event-Based Decentralized MDPs	Tarun Gupta, Akshat Kumar, Praveen Paruchuri	Decentralized MDPs (Dec-MDPs) provide a rigorous framework for collaborative multi-agent sequential decisionmaking under uncertainty.
744	IPOMDP-Net: A Deep Neural Network for Partially Observable Multi-Agent Planning Using Interactive POMDPs	Yanlin Han, Piotr Gmytrasiewicz	This paper introduces the IPOMDP-net, a neural network architecture for multi-agent planning under partial observability.
745	General Robustness Evaluation of Incentive Mechanism against Bounded Rationality Using Continuum-Armed Bandits	Zehong Hu, Jie Zhang, Zhao Li	In this paper, we propose a general empirical framework for robustness evaluation.
746	Message-Dropout: An Efficient Training Method for Multi-Agent Deep Reinforcement Learning	Woojun Kim, Myungsik Cho, Youngchul Sung	In this paper, we propose a new learning technique named message-dropout to improve the performance for multi-agent deep reinforcement learning under two application scenarios: 1) classical multi-agent reinforcement learning with direct message communication among agents and 2) centralized training with decentralized execution.
747	Symmetry-Breaking Constraints for Grid-Based Multi-Agent Path Finding	Jiaoyang Li, Daniel Harabor, Peter J. Stuckey, Hang Ma, Sven Koenig	We describe a new way of reasoning about symmetric collisions for Multi-Agent Path Finding (MAPF) on 4-neighbor grids.
748	Multi-Agent Discussion Mechanism for Natural Language Generation	Xu Li, Mingming Sun, Ping Li	We introduce the discussion mechanism into the multiagent communicating encoder-decoder architecture for Natural Language Generation (NLG) tasks and prove that by applying the discussion mechanism, the communication between agents becomes more effective.
749	Large Scale Learning of Agent Rationality in Two-Player Zero-Sum Games	Chun Kai Ling, Fei Fang, J. Zico Kolter	In this paper, we address these limitations and propose a framework that is applicable for more practical settings.
750	Leveraging Observations in Bandits: Between Risks and Benefits	Andrei Lupu, Audrey Durand, Doina Precup	In this paper, we study this problem in the context of bandits.
751	TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents	Yuexin Ma, Xinge Zhu, Sibo Zhang, Ruigang Yang, Wenping Wang, Dinesh Manocha	To solve this problem, we propose a long short-term memory-based (LSTM-based) realtime traffic prediction algorithm, TrafficPredict. In order to evaluate its performance, we collected trajectory datasets in a large city consisting of varying conditions and traffic densities.
752	Learning to Teach in Cooperative Multiagent Reinforcement Learning	Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Christopher Amato, Murray Campbell, Jonathan P. How	In contrast to existing works, this paper presents the first general framework and algorithm for intelligent agents to learn to teach in a multiagent environment.
753	Overcoming Blind Spots in the Real World: Leveraging Complementary Abilities for Joint Execution	Ramya Ramakrishnan, Ece Kamar, Besmira Nushi, Debadeepta Dey, Julie Shah, Eric Horvitz	Our approach for blind spot discovery combines experiences collected in simulation with limited human demonstrations.
754	Evolution of Collective Fairness in Hybrid Populations of Humans and Agents	Fernando P. Santos, Jorge M. Pacheco, Ana Paiva, Francisco C. Santos	We observe that people become increasingly fair if groups adopt stricter decision rules, i.e., if more individuals are required to accept a proposal for it to be accepted by the group.
755	Multi-Winner Contests for Strategic Diffusion in Social Networks	Wen Shen, Yang Feng, Cristina V. Lopes	Here, we introduce a novel multi-winner contests (MWC) mechanism for strategic diffusion in social networks.
756	Theory of Minds: Understanding Behavior in Groups through Inverse Planning	Michael Shum, Max Kleiman-Weiner, Michael L. Littman, Joshua B. Tenenbaum	Towards the goal of building machine-learning algorithms with human-like social intelligence, we develop a generative model of multiagent action understanding based on a novel representation for these latent relationships called Composable Team Hierarchies (CTH).
757	Multiagent Decision Making For Maritime Traffic Management	Arambam James Singh, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau	We address the problem of maritime traffic management in busy waterways to increase the safety of navigation by reducing congestion. We develop a maritime traffic simulator based on historical traffic data that incorporates realistic domain constraints such as uncertain and asynchronous movement of vessels.
758	Probabilistic Alternating-Time <em>µ</em>-Calculus	Fu Song, Yedi Zhang, Taolue Chen, Yu Tang, Zhiwu Xu	We propose a probabilistic extension of Alternating µ-Calculus (AMC), named PAMC, for reasoning about strategic abilities of agents in stochastic multi-agent systems.
759	Enriching Word Embeddings with a Regressor Instead of Labeled Corpora	Mohamed Abdalla, Magnus Sahlgren, Graeme Hirst	We propose a novel method for enriching word-embeddings without the need of a labeled corpus.
760	Online Embedding Compression for Text Classification Using Low Rank Matrix Factorization	Anish Acharya, Rahul Goel, Angeliki Metallinou, Inderjit Dhillon	We propose a compression method that leverages low rank matrix factorization during training, to compress the word embedding layer which represents the size bottleneck for most NLP models.
761	Antonym-Synonym Classification Based on New Sub-Space Embeddings	Muhammad Asif Ali, Yifang Sun, Xiaoling Zhou, Wei Wang, Xiang Zhao	We propose a novel approach entirely based on pre-trained embeddings.
762	AutoSense Model for Word Sense Induction	Reinald Kim Amplayo, Seung-won Hwang, Min Song	Thus, we aim to eliminate these requirements and solve the sense granularity problem by proposing AutoSense, a latent variable model based on two observations: (1) senses are represented as a distribution over topics, and (2) senses generate pairings between the target word and its neighboring word.
763	Re-Evaluating ADEM: A Deeper Look at Scoring Dialogue Responses	Ananya B. Sai, Mithun Das Gupta, Mitesh M. Khapra, Mukundhan Srinivasan	We take a systematic look at the scoring function proposed by ADEM and connect it to linear system theory to predict the shortcomings evident in the system.
764	Automated Rule Base Completion as Bayesian Concept Induction	Zied Bouraoui, Steven Schockaert	We present experimental results that demonstrate the effectiveness of our method.
765	GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition	Hui Chen, Zijia Lin, Guiguang Ding, Jianguang Lou, Yusen Zhang, Borje Karlsson	In this paper, we propose a simple but effective CNN-based network for NER, i.e., gated relation network (GRN), which is more capable than common CNNs in capturing long-term context.
766	Incorporating Structured Commonsense Knowledge in Story Completion	Jiaao Chen, Jianshu Chen, Zhou Yu	We present a neural story ending selection model that integrates three types of information: narrative sequence, sentiment evolution and commonsense knowledge.
767	Deep Short Text Classification with Knowledge Powered Attention	Jindong Chen, Yizhou Hu, Jingping Liu, Yanghua Xiao, Haiyun Jiang	In this paper, we retrieve knowledge from external knowledge source to enhance the semantic representation of short texts.
768	Transfer Learning for Sequence Labeling Using Source Model and Target Data	Lingzhen Chen, Alessandro Moschitti	In this paper, we propose an approach for transferring the knowledge of a neural model for sequence labeling, learned from the source domain, to a new model trained on a target domain, where new label categories appear.
769	Title-Guided Encoding for Keyphrase Generation	Wang Chen, Yifan Gao, Jiani Zhang, Irwin King, Michael R. Lyu	To solve this problem, we introduce a new model called Title-Guided Network (TG-Net) for automatic keyphrase generation task based on the encoderdecoder architecture with two new features: (i) the title is additionally employed as a query-like input, and (ii) a titleguided encoder gathers the relevant information from the title to each word in the document.
770	Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions	Zhipeng Chen, Yiming Cui, Wentao Ma, Shijin Wang, Guoping Hu	In this paper, we propose a novel approach called Convolutional Spatial Attention (CSA) model which can better handle the MRC with multiple-choice questions.
771	Implicit Argument Prediction as Reading Comprehension	Pengxiang Cheng, Katrin Erk	We present a new model for implicit argument prediction that draws on reading comprehension, casting the predicate-argument tuple with the missing argument as a query.
772	Recurrent Stacking of Layers for Compact Neural Machine Translation Models	Raj Dabre, Atsushi Fujita	In this paper, we propose to share parameters across all layers thereby leading to a recurrently stacked sequence-to-sequence model.
773	Joint Extraction of Entities and Overlapping Relations Using Position-Attentive Sequence Labeling	Dai Dai, Xinyan Xiao, Yajuan Lyu, Shan Dou, Qiaoqiao She, Haifeng Wang	In this paper, we present a novel unified joint extraction model which directly tags entity and relation labels according to a query word position p, i.e., detecting entity at p, and identifying entities at other positions that have relationship with the former.
774	What Is One Grain of Sand in the Desert? Analyzing Individual Neurons in Deep NLP Models	Fahim Dalvi, Nadir Durrani, Hassan Sajjad, Yonatan Belinkov, Anthony Bau, James Glass	We propose two methods: Linguistic Correlation Analysis, based on a supervised method to extract the most relevant neurons with respect to an extrinsic task, and Cross-model Correlation Analysis, an unsupervised method to extract salient neurons w.r.t. the model itself.
775	Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering	Yang Deng, Yuexiang Xie, Yaliang Li, Min Yang, Nan Du, Wei Fan, Kai Lei, Ying Shen	In this paper, we tackle answer selection and KBQA tasks simultaneously via multi-task learning (MTL), motivated by the following motivations.
776	Training Temporal Word Embeddings with a Compass	Valerio Di Carlo, Federico Bianchi, Matteo Palmonari	In this paper, we propose a new heuristic to train temporal word embeddings based on the Word2vec model.
777	A Pattern-Based Approach to Recognizing Time Expressions	Wentao Ding, Guanji Gao, Linfeng Shi, Yuzhong Qu	In this paper, we propose a pattern-based approach, called PTime, which automatically generates and selects patterns for recognizing time expressions.
778	From Independent Prediction to Reordered Prediction: Integrating Relative Position and Global Label Information to Emotion Cause Identification	Zixiang Ding, Huihui He, Mengran Zhang, Rui Xia	To integrate such information, we propose a model based on the neural network architecture to encode the three elements (i.e., text content, relative position and global label), in an unified and end-to-end fashion.
779	Adapting Translation Models for Transcript Disfluency Detection	Qianqian Dong, Feng Wang, Zhen Yang, Wei Chen, Shuang Xu, Bo Xu	We propose a general training framework for adapting NMT models to TDD task rapidly.
780	Explicit Interaction Model towards Text Classification	Cunxiao Du, Zhaozheng Chen, Fuli Feng, Lei Zhu, Tian Gan, Liqiang Nie	To address this problem, we introduce the interaction mechanism to incorporate word-level matching signals into the text classification task.
781	“Bilingual Expert” Can Find Translation Errors	Kai Fan, Jiayi Wang, Bo Li, Fengming Zhou, Boxing Chen, Luo Si	In order to address the issue of translation quality estimation (QE) without reference, we propose a general framework for automatic evaluation of the translation output for the QE task in the Conference on Statistical Machine Translation (WMT).
782	EA Reader: Enhance Attentive Reader for Cloze-Style Question Answering via Multi-Space Context Fusion	Chengzhen Fu, Yan Zhang	In this paper, we design a novel module to produce the query-aware context vector, named Multi-Space based Context Fusion (MSCF), with the following considerations: (1) interactions are applied across multiple latent semantic spaces; (2) attention is measured at bit level, not at token level.
783	Generating Multiple Diverse Responses for Short-Text Conversation	Jun Gao, Wei Bi, Xiaojiang Liu, Junhui Li, Shuming Shi	In this paper, we propose a novel response generation model, which considers a set of responses jointly and generates multiple diverse responses simultaneously.
784	Structured Two-Stream Attention Network for Video Question Answering	Lianli Gao, Pengpeng Zeng, Jingkuan Song, Yuan-Fang Li, Wu Liu, Tao Mei, Heng Tao Shen	In this paper, we specifically tackle the problem of video QA by proposing a Structured Two-stream Attention network, namely STA, to answer a free-form or open-ended natural language question about the content of a given video.
785	Abstractive Text Summarization by Incorporating Reader Comments	Shen Gao, Xiuying Chen, Piji Li, Zhaochun Ren, Lidong Bing, Dongyan Zhao, Rui Yan	To tackle this problem, we propose the task of reader-aware abstractive summary generation, which utilizes the reader comments to help the model produce better summary about the main aspect. We release our large-scale dataset for further research1.
786	Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification	Tianyu Gao, Xu Han, Zhiyuan Liu, Maosong Sun	In this paper, we propose hybrid attention-based prototypical networks for the problem of noisy few-shot RC.
787	Predicting and Analyzing Language Specificity in Social Media Posts	Yifan Gao, Yang Zhong, Daniel Preoţiuc-Pietro, Junyi Jessy Li	Using this dataset, we train a supervised regression model that accurately estimates specificity in social media posts, reaching a mean absolute error of 0.3578 (for ratings on a scale of 1-5) and 0.73 Pearson correlation, significantly improving over baselines and previous sentence specificity prediction systems. We collect a dataset of over 7,000 tweets annotated with specificity on a fine-grained scale.
788	Generating Distractors for Reading Comprehension Questions from Real Examinations	Yifan Gao, Lidong Bing, Piji Li, Irwin King, Michael R. Lyu	We propose a hierarchical encoderdecoder framework with static and dynamic attention mechanisms to tackle this task.
789	Kernelized Hashcode Representations for Relation Extraction	Sahil Garg, Aram Galstyan, Greg Ver Steeg, Irina Rish, Guillermo Cecchi, Shuyang Gao	Here we propose to use random subspaces of KLSH codes for efficiently constructing an explicit representation of NLP structures suitable for general classification methods.
790	MNCN: A Multilingual Ngram-Based Convolutional Network for Aspect Category Detection in Online Reviews	Erfan Ghadery, Sajad Movahedi, Heshaam Faili, Azadeh Shakery	In this paper, we propose a multilingual method to perform aspect category detection on reviews in different languages, which makes use of a deep convolutional neural network with multilingual word embeddings.
791	Sentence-Wise Smooth Regularization for Sequence to Sequence Learning	Chengyue Gong, Xu Tan, Di He, Tao Qin	In this paper, we propose a sentence-wise regularization method which aims to output smooth prediction probabilities for all the tokens in the target sequence.
792	Switch-LSTMs for Multi-Criteria Chinese Word Segmentation	Jingjing Gong, Xinchi Chen, Tao Gui, Xipeng Qiu	In this paper, we propose a flexible multi-criteria learning for Chinese word segmentation.
793	Deep Cascade Multi-Task Learning for Slot Filling in Online Shopping Assistant	Yu Gong, Xusheng Luo, Yu Zhu, Wenwu Ou, Zhao Li, Muhua Zhu, Kenny Q. Zhu, Lu Duan, Xi Chen	In this work, inspired by the unique structure of E-commerce knowledge base, we propose a novel multi-task model with cascade and residual connections, which jointly learns segment tagging, named entity tagging and slot filling.
794	Story Ending Generation with Incremental Encoding and Commonsense Knowledge	Jian Guan, Yansen Wang, Minlie Huang	In this paper, we devise a novel model for story ending generation.
795	Long Short-Term Memory with Dynamic Skip Connections	Tao Gui, Qi Zhang, Lujun Zhao, Yaosong Lin, Minlong Peng, Jingjing Gong, Xuanjing Huang	In this work, we tried to alleviate this problem by introducing a dynamic skip connection, which can learn to directly connect two dependent words.
796	Gaussian Transformer: A Lightweight Approach for Natural Language Inference	Maosheng Guo, Yu Zhang, Ting Liu	Natural Language Inference (NLI) is an active research area, where numerous approaches based on recurrent neural networks (RNNs), convolutional neural networks (CNNs), and self-attention networks (SANs) has been proposed.
797	GIRNet: Interleaved Multi-Task Recurrent State Sequence Models	Divam Gupta, Tanmoy Chakraborty, Soumen Chakrabarti	We propose GIRNet, a unified position-sensitive multi-task recurrent neural network (RNN) architecture for such applications.
798	Document Informed Neural Autoregressive Topic Models with Distributional Prior	Pankaj Gupta, Yatin Chaudhary, Florian Buettner, Hinrich Schütze	We address two challenges in topic models: (1) Context information around words helps in determining their actual meaning, e.g., “networks” used in the contexts artificial neural networks vs. biological neuron networks.
799	Neural Relation Extraction within and across Sentence Boundaries	Pankaj Gupta, Subburam Rajaram, Hinrich Schütze, Thomas Runkler	In this paper, we propose a novel architecture for this task: inter-sentential dependency-based neural networks (iDepNN).
800	PARABANK: Monolingual Bitext Generation and Sentential Paraphrasing via Lexically-Constrained Neural Machine Translation	J. Edward Hu, Rachel Rudinger, Matt Post, Benjamin Van Durme	We present PARABANK, a large-scale English paraphrase dataset that surpasses prior work in both quantity and quality.
801	Read + Verify: Machine Reading Comprehension with Unanswerable Questions	Minghao Hu, Furu Wei, Yuxing Peng, Zhen Huang, Nan Yang, Dongsheng Li	Read + Verify: Machine Reading Comprehension with Unanswerable Questions
802	Recurrent Poisson Process Unit for Speech Recognition	Hengguan Huang, Hao Wang, Brian Mak	In this paper, we propose a recurrent Poisson process (RPP) which can be seen as a collection of Poisson processes at a series of time intervals in which the intensity evolves according to the RNN hidden states that encode the history of the acoustic signal.
803	Dictionary-Guided Editing Networks for Paraphrase Generation	Shaohan Huang, Yu Wu, Furu Wei, Zhongzhi Luan	We propose a novel approach to modeling the process with dictionary-guided editing networks which effectively conduct rewriting on the source sentence to generate paraphrase sentences.
804	Unsupervised Controllable Text Formalization	Parag Jain, Abhijit Mishra, Amar Prakash Azad, Karthik Sankaranarayanan	We propose a novel framework for controllable natural language transformation.
805	Word Embedding as Maximum A Posteriori Estimation	Shoaib Jameel, Zihao Fu, Bei Shi, Wai Lam, Steven Schockaert	In this paper, we propose to generalize this approach to word embedding by considering parametrized variants of the GloVe model and incorporating priors on these parameters.
806	Understanding Actors and Evaluating Personae with Gaussian Embeddings	Hannah Kim, Denys Katerenchuk, Daniel Billet, Jun Huan, Haesun Park, Boyang Li	For an actor model, we present a technique for embedding actors, movies, character roles, genres, and descriptive keywords as Gaussian distributions and translation vectors, where the Gaussian variance corresponds to actors’ versatility.
807	Predicting the Argumenthood of English Prepositional Phrases	Najoung Kim, Kyle Rawlins, Benjamin Van Durme, Paul Smolensky	We propose two PP argumenthood prediction tasks branching from these two motivations: (1) binary argumentadjunct classification of PPs in VerbNet, and (2) gradient argumenthood prediction using human judgments as gold standard, and report results from prediction models that use pretrained word embeddings and other linguistically informed features.
808	Semantic Sentence Matching with Densely-Connected Recurrent and Co-Attentive Information	Seonhoon Kim, Inho Kang, Nojun Kwak	Inspired by DenseNet, a densely connected convolutional network, we propose a densely-connected co-attentive recurrent neural network, each layer of which uses concatenated information of attentive features as well as hidden features of all the preceding recurrent layers.
809	Dynamic Compositionality in Recursive Neural Networks with Structure-Aware Tag Representations	Taeuk Kim, Jihun Choi, Daniel Edmiston, Sanghwan Bae, Sang-goo Lee	We present a novel RvNN architecture that can provide dynamic compositionality by considering comprehensive syntactic information derived from both the structure and linguistic tags.
810	Improving Neural Question Generation Using Answer Separation	Yanghoon Kim, Hwanhee Lee, Joongbo Shin, Kyomin Jung	In this paper, we propose answer-separated seq2seq, which better utilizes the information from both the passage and the target answer.
811	Domain Agnostic Real-Valued Specificity Prediction	Wei-Jen Ko, Greg Durrett, Junyi Jessy Li	The goal of this work is to generalize specificity prediction to domains where no labeled data is available and output more nuanced realvalued specificity ratings.We present an unsupervised domain adaptation system for sentence specificity prediction, specifically designed to output real-valued estimates from binary training labels.
812	Neural Machine Translation with Adequacy-Oriented Learning	Xiang Kong, Zhaopeng Tu, Shuming Shi, Eduard Hovy, Tong Zhang	In this work, we propose an adequacyoriented learning mechanism for NMT by casting translation as a stochastic policy in Reinforcement Learning (RL), where the reward is estimated by explicitly measuring translation adequacy.
813	Fast and Simple Mixture of Softmaxes with BPE and Hybrid-LightRNN for Language Generation	Xiang Kong, Qizhe Xie, Zihang Dai, Eduard Hovy	In this work, we set out to unleash the power of MoS in practical applications by investigating improved word coding schemes, which could effectively reduce the vocabulary size and hence relieve the memory and computation burden.
814	Lattice CNNs for Matching Based Chinese Question Answering	Yuxuan Lai, Yansong Feng, Xiaohan Yu, Zheng Wang, Kun Xu, Dongyan Zhao	In this paper, we propose a novel lattice based CNN model (LCNs) to utilize multi-granularity information inherent in the word lattice while maintaining strong ability to deal with the introduced noisy information for matching based question answering in Chinese.
815	Zero-Shot Adaptive Transfer for Conversational Language Understanding	Sungjin Lee, Rahul Jha	To tackle this, we introduce a novel Zero-Shot Adaptive Transfer method for slot tagging that utilizes the slot description for transferring reusable concepts across domains, and enjoys efficient training without any explicit concept alignments.
816	A Human-Like Semantic Cognition Network for Aspect-Level Sentiment Classification	Zeyang Lei, Yujiu Yang, Min Yang, Wei Zhao, Jun Guo, Yi Liu	In this paper, we propose a novel Human-like Semantic Cognition Network (HSCN) for aspect-level sentiment classification, motivated by the principles of human beings’ reading cognitive process (pre-reading, active reading, post-reading).
817	Dependency Grammar Induction with a Neural Variational Transition-Based Parser	Bowen Li, Jianpeng Cheng, Yang Liu, Frank Keller	In this work, we propose a neural transition-based parser for dependency grammar induction, whose inference procedure utilizes rich neural features with O(n) time complexity.
818	Knowledge-Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation	Christy Y. Li, Xiaodan Liang, Zhiting Hu, Eric P. Xing	Generating long and semantic-coherent reports to describe medical images poses great challenges towards bridging visual and linguistic modalities, incorporating medical domain knowledge, and generating realistic and accurate descriptions.
819	What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning	Irene Li, Alexander R. Fabbri, Robert R. Tung, Dragomir R. Radev	We introduce LectureBank, a dataset containing 1,352 English lecture files collected from university courses which are each classified according to an existing taxonomy as well as 208 manually-labeled prerequisite relation topics, which is publicly available 1.
820	Differentiated Distribution Recovery for Neural Text Generation	Jianing Li, Yanyan Lan, Jiafeng Guo, Jun Xu, Xueqi Cheng	In this paper, we propose to achieve differentiated distribution recovery, DDR for short.
821	Towards Personalized Review Summarization via User-Aware Sequence Network	Junjie Li, Haoran Li, Chengqing Zong	We propose a novel personalized review summarization model named User-aware Sequence Network (USN) to consider the aforementioned users’ characteristics when generating summaries, which contains a user-aware encoder and a useraware decoder.
822	Insufficient Data Can Also Rock! Learning to Converse Using Smaller Data with Augmentation	Juntao Li, Lisong Qiu, Bo Tang, Dongmin Chen, Dongyan Zhao, Rui Yan	In this paper, we use data augmentation techniques to improve the performance of neural dialogue models on the condition of insufficient data.
823	Neural Speech Synthesis with Transformer Network	Naihan Li, Shujie Liu, Yanqing Liu, Sheng Zhao, Ming Liu	Inspired by the success of Transformer network in neural machine translation (NMT), in this paper, we introduce and adapt the multi-head attention mechanism to replace the RNN structures and also the original attention mechanism in Tacotron2.
824	A Unified Model for Opinion Target Extraction and Target Sentiment Prediction	Xin Li, Lidong Bing, Piji Li, Wai Lam	This paper aims to solve the complete task of target-based sentiment analysis in an end-to-end fashion, and presents a novel unified model which applies a unified tagging scheme.
825	Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning	Ziming Li, Julia Kiseleva, Maarten de Rijke	We evaluate the performance of the resulting model with automatic metrics and human evaluations in two annotation settings.
826	Dependency or Span, End-to-End Uniform Semantic Role Labeling	Zuchao Li, Shexia He, Hai Zhao, Yiqing Zhang, Zhuosheng Zhang, Xi Zhou, Xiang Zhou	This paper presents an end-to-end model for both dependency and span SRL with a unified argument representation to deal with two different types of argument annotations in a uniform fashion.
827	A Generalized Idiom Usage Recognition Model Based on Semantic Compatibility	Changsheng Liu, Rebecca Hwa	We propose a novel semantic compatibility model by adapting the training of a Continuous Bag-of-Words (CBOW) model for the purpose of idiom usage recognition.
828	Leveraging Web Semantic Knowledge in Word Representation Learning	Haoyan Liu, Lei Fang, Jian-Guang Lou, Zhoujun Li	In this paper, we propose to leverage semantic knowledge automatically mined from web structured data to enhance WRL.
829	Exploiting the Ground-Truth: An Adversarial Imitation Based Knowledge Distillation Approach for Event Detection	Jian Liu, Yubo Chen, Kang Liu	In this paper, we propose an adversarial imitation based knowledge distillation approach, for the first time, to tackle the challenge of acquiring knowledge from rawsentences for event detection.
830	Contextualized Non-Local Neural Networks for Sequence Learning	Pengfei Liu, Shuaichen Chang, Xuanjing Huang, Jian Tang, Jackie Chi Kit Cheung	In this paper, we propose an approach that combines and draws on the complementary strengths of these two methods.
831	FANDA: A Novel Approach to Perform Follow-Up Query Analysis	Qian Liu, Bei Chen, Jian-Guang Lou, Ge Jin, Dongmei Zhang	In this paper, we address a typical contextual understanding problem, termed as follow-up query analysis. Our work summarizes typical follow-up query scenarios and provides a new FollowUp dataset with 1000 query triples on 120 tables.
832	Unsupervised Post-Processing of Word Vectors via Conceptor Negation	Tianlin Liu, Lyle Ungar, João Sedoc	In this paper, we introduce a novel word vector post-processing technique based on matrix conceptors (Jaeger 2014), a family of regularized identity maps.
833	Hierarchical Encoder with Auxiliary Supervision for Neural Table-to-Text Generation: Learning Better Representation for Tables	Tianyu Liu, Fuli Luo, Qiaolin Xia, Shuming Ma, Baobao Chang, Zhifang Sui	We achieve the state-of-the-art performance on both automatic and human evaluation metrics.
834	Learning Personalized End-to-End Goal-Oriented Dialog	Liangchen Luo, Wenhao Huang, Qi Zeng, Zaiqing Nie, Xu Sun	In this paper, we present a personalized end-to-end model in an attempt to leverage personalization in goal-oriented dialogs.
835	SAM-Net: Integrating Event-Level and Chain-Level Attentions to Predict What Happens Next	Shangwen Lv, Wanhui Qian, Longtao Huang, Jizhong Han, Songlin Hu	We utilize the event-level attention to model the relations between subsequent events and individual events.
836	LiveBot: Generating Live Video Comments Based on Visual and Textual Contexts	Shuming Ma, Lei Cui, Damai Dai, Furu Wei, Xu Sun	In this work, we construct a large-scale live comment dataset with 2,361 videos and 895,929 live comments.
837	DialogueRNN: An Attentive RNN for Emotion Detection in Conversations	Navonil Majumder, Soujanya Poria, Devamanyu Hazarika, Rada Mihalcea, Alexander Gelbukh, Erik Cambria	In this paper, we describe a new method based on recurrent neural networks that keeps track of the individual party states throughout the conversation and uses this information for emotion classification.
838	Weakly-Supervised Hierarchical Text Classification	Yu Meng, Jiaming Shen, Chao Zhang, Jiawei Han	In this paper, we propose a weakly-supervised neural method for hierarchical text classification.
839	CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling	Ning Miao, Hao Zhou, Lili Mou, Rui Yan, Lei Li	In this paper, we propose CGMH, a novel approach using Metropolis-Hastings sampling for constrained sentence generation.
840	Spell Once, Summon Anywhere: A Two-Level Open-Vocabulary Language Model	Sebastian J. Mielke, Jason Eisner	The method we propose can be used to extend any closedvocabulary generative model, but in this paper we specifically consider the case of neural language modeling.
841	One for All: Neural Joint Modeling of Entities and Events	Trung Minh Nguyen, Thien Huu Nguyen	In this work, we propose a novel model to jointly perform predictions for entity mentions, event triggers and arguments based on the shared hidden representations from deep learning.
842	Combining Fact Extraction and Verification with Neural Semantic Matching Networks	Yixin Nie, Haonan Chen, Mohit Bansal	In this paper, we present a connected system consisting of three homogeneous neural semantic matching models that conduct document retrieval, sentence selection, and claim verification jointly for fact extraction and verification.
843	Analyzing Compositionality-Sensitivity of NLI Models	Yixin Nie, Yicheng Wang, Mohit Bansal	Therefore, we propose a compositionality-sensitivity testing setup that analyzes models on natural examples from existing datasets that cannot be solved via lexical features alone (i.e., on which a bag-of-words model gives a high probability to one wrong label), hence revealing the models’ actual compositionality awareness.
844	HAS-QA: Hierarchical Answer Spans Model for Open-Domain Question Answering	Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Lixin Su, Xueqi Cheng	Recently, some works have viewed this problem as a reading comprehension (RC) task, and directly applied successful RC models to it.
845	Paraphrase Diversification Using Counterfactual Debiasing	Sunghyun Park, Seung-won Hwang, Fuxiang Chen, Jaegul Choo, Jung-Woo Ha, Sunghun Kim, Jinyeong Yim	In this work, we consider style transfer as a means of imposing diversity, with a paraphrasing correctness constraint that the target sentence must remain a paraphrase of the original sentence.
846	Found in Translation: Learning Robust Joint Representations by Cyclic Translations between Modalities	Hai Pham, Paul Pu Liang, Thomas Manzini, Louis-Philippe Morency, Barnabás Póczos	In this paper, we propose a method to learn robust joint representations by translating between modalities.
847	Unseen Word Representation by Aligning Heterogeneous Lexical Semantic Spaces	Victor Prokhorov, Mohammad Taher Pilehvar, Dimitri Kartsaklis, Pietro Lio, Nigel Collier	In this paper we put forward a technique that exploits the knowledge encoded in lexical resources, such as WordNet, to induce embeddings for unseen words.
848	Data-to-Text Generation with Content Selection and Planning	Ratish Puduppully, Li Dong, Mirella Lapata	In this work, we present a neural network architecture which incorporates content selection and planning without sacrificing end-to-end training.
849	Jointly Learning to Label Sentences and Tokens	Marek Rei, Anders Søgaard	In this paper, we investigate how objectives at different granularities can be used to learn better language representations and we propose an architecture for jointly learning to label sentences and tokens.
850	Zero-Shot Neural Transfer for Cross-Lingual Entity Linking	Shruti Rijhwani, Jiateng Xie, Graham Neubig, Jaime Carbonell	Specifically, we propose pivot-based entity linking, which leverages information from a highresource “pivot” language to train character-level neural entity linking models that are transferred to the source lowresource language in a zero-shot manner.
851	COALA: A Neural Coverage-Based Approach for Long Answer Selection with Small Data	Andreas Rücklé, Nafise Sadat Moosavi, Iryna Gurevych	In this paper, we propose COALA, an answer selection approach that (a) selects appropriate long answers due to an effective comparison of all question-answer aspects, (b) has the ability to generalize from a small number of training examples, and (c) makes use of the information about syntactic roles of words.
852	Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function	Devendra Singh Sachan, Manzil Zaheer, Ruslan Salakhutdinov	In this paper, we study bidirectional LSTM network for the task of text classification using both supervised and semisupervised approaches.
853	A Hierarchical Multi-Task Approach for Learning Embeddings from Semantic Tasks	Victor Sanh, Thomas Wolf, Sebastian Ruder	In this work, we introduce a hierarchical model trained in a multi-task learning setup on a set of carefully selected semantic tasks.
854	On Resolving Ambiguous Anaphoric Expressions in Imperative Discourse	Vasanth Sarathy, Matthias Scheutz	In this paper, we introduce, with examples, a general class of situated anaphora resolution problems, propose a proof-of-concept system for disambiguating situated pronouns, and discuss some general types of reasoning that might be needed.
855	Learning Semantic Representations for Novel Words: Leveraging Both Form and Context	Timo Schick, Hinrich Schütze	In this paper, we propose an architecture that leverages both sources of information – surface-form and context – and show that it results in large increases in embedding quality.
856	Challenges in the Automatic Analysis of Students’ Diagnostic Reasoning	Claudia Schulz, Christian M. Meyer, Iryna Gurevych	We aim to enable the large-scale adoption of diagnostic reasoning analysis and feedback by automating the epistemic activity identification. We create the first corpus for this task, comprising diagnostic reasoning selfexplanations of students from two domains annotated with epistemic activities.
857	Analysis of Joint Multilingual Sentence Representations and Semantic K-Nearest Neighbor Graphs	Holger Schwenk, Douwe Kiela, Matthijs Douze	We provide a detailed analysis of both the multilingual sentence encoder for twenty-one European languages and the learned graph.
858	Learning to Embed Sentences Using Attentive Recursive Trees	Jiaxin Shi, Lei Hou, Juanzi Li, Zhiyuan Liu, Hanwang Zhang	To this end, we propose an Attentive Recursive Tree model (AR-Tree), where the words are dynamically located according to their importance in the task.
859	DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization	Jiaxin Shi, Chen Liang, Lei Hou, Juanzi Li, Zhiyuan Liu, Hanwang Zhang	We propose DeepChannel, a robust, data-efficient, and interpretable neural model for extractive document summarization.
860	A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues	Zhouxing Shi, Minlie Huang	This paper presents a deep sequential model for parsing discourse dependency structures of multi-party dialogues.
861	GlobalTrait: Personality Alignment of Multilingual Word Embeddings	Farhad Bin Siddique, Dario Bertero, Pascale Fung	We propose a multilingual model to recognize Big Five Personality traits from text data in four different languages: English, Spanish, Dutch and Italian.
862	Exploring Knowledge Graphs in an Interpretable Composite Approach for Text Entailment	Vivian S. Silva, André Freitas, Siegfried Handschuh	We propose a composite approach for recognizing text entailment which analyzes the entailment pair to decide whether it must be resolved syntactically or semantically.
863	Fast PMI-Based Word Embedding with Efficient Use of Unobserved Patterns	Behrouz Haji Soleimani, Stan Matwin	In this paper, we propose a new word embedding algorithm that works on a smoothed Positive Pointwise Mutual Information (PPMI) matrix which is obtained from the word-word co-occurrence counts.
864	Distantly Supervised Entity Relation Extraction with Adapted Manual Annotations	Changzhi Sun, Yuanbin Wu	To tackle the problem, we propose to adapt a small manually labelled dataset to the large automatically generated dataset.
865	Towards Sentence-Level Brain Decoding with Distributed Representations	Jingyuan Sun, Shaonan Wang, Jiajun Zhang, Chengqing Zong	In this paper, we build decoders to associate brain activities with sentence stimulus via distributed representations, the currently dominant sentence representation approach in natural language processing (NLP).
866	A Grammar-Based Structural CNN Decoder for Code Generation	Zeyu Sun, Qihao Zhu, Lili Mou, Yingfei Xiong, Ge Li, Lu Zhang	In this paper, we propose a grammar-based structural convolutional neural network (CNN) for code generation.
867	QUAREL: A Dataset and Models for Answering Questions about Qualitative Relationships	Oyvind Tafjord, Peter Clark, Matt Gardner, Wen-tau Yih, Ashish Sabharwal	We present QUAREL, a dataset of diverse story questions involving qualitative relationships that characterize these challenges, and techniques that begin to address them.
868	A Hierarchical Framework for Relation Extraction with Reinforcement Learning	Ryuichi Takanobu, Tianyang Zhang, Jiexi Liu, Minlie Huang	This paper presents a novel paradigm to deal with relation extraction by regarding the related entities as the arguments of a relation.
869	Jointly Extracting Multiple Triplets with Multilayer Translation Constraints	Zhen Tan, Xiang Zhao, Wei Wang, Weidong Xiao	To close the gap, we propose in this paper a joint neural extraction model for multitriplets, namely, TME, which is capable of adaptively discovering multiple triplets simultaneously in a sentence via ranking with translation mechanism.
870	Multi-Matching Network for Multiple Choice Reading Comprehension	Min Tang, Jiaran Cai, Hankz Hankui Zhuo	In this paper, we propose Multi-Matching Network (MMN) which models the semantic relationship among passage, question and candidate answers from multiple different paradigms of matching.
871	Generating Live Soccer-Match Commentary from Play Data	Yasufumi Taniguchi, Yukun Feng, Hiroya Takamura, Manabu Okumura	For these reasons, we propose an encoder for play event data, which is enhanced with a gate mechanism.
872	Near-Lossless Binarization of Word Embeddings	Julien Tissier, Christophe Gravier, Amaury Habrard	The method proposed in this paper transforms real-valued embeddings into binary embeddings while preserving semantic information, requiring only 128 or 256 bits for each vector.
873	CompareLDA: A Topic Model for Document Comparison	Maksim Tkachenko, Hady W. Lauw	In this work, we develop a topic model supervised by pairwise comparisons of documents.
874	A Natural Language Corpus of Common Grounding under Continuous and Partially-Observable Context	Takuma Udagawa, Akiko Aizawa	In this paper, we propose a minimal dialogue task which requires advanced skills of common grounding under continuous and partially-observable context. Based on this task formulation, we collected a largescale dataset of 6,760 dialogues which fulfills essential requirements of natural language corpora.
875	Improving Hypernymy Prediction via Taxonomy Enhanced Adversarial Learning	Chengyu Wang, Xiaofeng He, Aoying Zhou	In this paper, we introduce the Taxonomy Enhanced Adversarial Learning (TEAL) for hypernymy prediction.
876	What if We Simply Swap the Two Text Fragments? A Straightforward yet Effective Way to Test the Robustness of Methods to Confounding Signals in Nature Language Inference Tasks	Haohan Wang, Da Sun, Eric P. Xing	With the increasing popularity of NLI, many state-of-the-art predictive models have been proposed with impressive performances.
877	Template-Based Math Word Problem Solvers with Recursive Neural Networks	Lei Wang, Dongxiang Zhang, Jipeng Zhang, Xing Xu, Lianli Gao, Bing Tian Dai, Heng Tao Shen	In this paper, we propose a template-based solution based on recursive neural network for math expression construction.
878	Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding	Peifeng Wang, Jialong Han, Chenliang Li, Rong Pan	To this end, we summarize the desired properties that may lead to effective neighborhood aggregators.
879	Chinese NER with Height-Limited Constituent Parsing	Rui Wang, Xin Xin, Wei Chang, Kun Ming, Biao Li, Xin Fan	In this paper, we investigate how to improve Chinese named entity recognition (NER) by jointly modeling NER and constituent parsing, in the framework of neural conditional random fields (CRF).
880	A Multi-Agent Communication Framework for Question-Worthy Phrase Extraction and Question Generation	Siyuan Wang, Zhongyu Wei, Zhihao Fan, Yang Liu, Xuanjing Huang	We introduce a multi-agent communication framework, taking phrase extraction and question generation as two agents, and learn these two tasks simultaneously via message passing mechanism.
881	A Task in a Suit and a Tie: Paraphrase Generation with Semantic Augmentation	Su Wang, Rahul Gupta, Nancy Chang, Jason Baldridge	We show the effectiveness of transformers (Vaswani et al. 2017) for paraphrase generation and further improvements by incorporating PropBank labels via a multi-encoder.
882	Hierarchical Attention Networks for Sentence Ordering	Tianming Wang, Xiaojun Wan	In this paper, we propose a novel hierarchical attention network that captures word clues and dependencies between sentences to address this problem.
883	Transferable Interactive Memory Network for Domain Adaptation in Fine-Grained Opinion Extraction	Wenya Wang, Sinno Jialin Pan	In this work, we propose an interactive memory network that consists of local and global memory units.
884	Unsupervised Learning Helps Supervised Neural Word Segmentation	Xiaobin Wang, Deng Cai, Linlin Li, Guangwei Xu, Hai Zhao, Luo Si	By exploiting unlabeled data for further performance improvement for Chinese word segmentation, this work makes the first attempt at exploring adding unsupervised segmentation information into neural supervised segmenter.
885	Improving Natural Language Inference Using External Knowledge in the Science Questions Domain	Xiaoyan Wang, Pavan Kapanipathi, Ryan Musa, Mo Yu, Kartik Talamadupula, Ibrahim Abdelaziz, Maria Chang, Achille Fokoue, Bassem Makni, Nicholas Mattei, Michael Witbrock	To address this, we present a combination of techniques that harness external knowledge to improve performance on the NLI problem in the science questions domain.
886	Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors	Yansen Wang, Ying Shen, Zhun Liu, Paul Pu Liang, Amir Zadeh, Louis-Philippe Morency	To this end, we propose the Recurrent Attended Variation Embedding Network (RAVEN) that models the fine-grained structure of nonverbal subword sequences and dynamically shifts word representations based on nonverbal cues.
887	A Deep Reinforcement Learning Based Multi-Step Coarse to Fine Question Answering (MSCQA) System	Yu Wang, Hongxia Jin	In this paper, we present a multi-step coarse to fine question answering (MSCQA) system which can efficiently processes documents with different lengths by choosing appropriate actions.
888	When Do Words Matter? Understanding the Impact of Lexical Choice on Audience Perception Using Individual Treatment Effect Estimation	Zhao Wang, Aron Culotta	Studies across many disciplines have shown that lexical choice can affect audience perception.
889	Better Fine-Tuning via Instance Weighting for Text Classification	Zhi Wang, Wei Bi, Yan Wang, Xiaojiang Liu	In this paper, we propose an Instance Weighting based Finetuning (IW-Fit) method, which revises the fine-tuning stage to improve the final performance on the target domain.
890	A Topic-Aware Reinforced Model for Weakly Supervised Stance Detection	Penghui Wei, Wenji Mao, Guandan Chen	To address the above two issues, in this paper, we propose a Topic-Aware Reinforced Model (TARM) for weakly supervised stance detection.
891	Translating with Bilingual Topic Knowledge for Neural Machine Translation	Xiangpeng Wei, Yue Hu, Luxi Xing, Yipeng Wang, Li Gao	In this paper, we propose a novel bilingual topic enhanced NMT (BLTNMT) model to improve translation performance by incorporating bilingual topic knowledge into NMT.
892	Reverse-Engineering Satire, or “Paper on Computational Humor Accepted despite Making Serious Advances”	Robert West, Eric Horvitz	As such, it is a promising and important subject of study, with relevance for artificial intelligence and human– computer interaction. Starting from the observation that satirical news headlines tend to resemble serious news headlines, we build and analyze a corpus of satirical headlines paired with nearly identical but serious headlines.
893	Improving Distantly Supervised Relation Extraction with Neural Noise Converter and Conditional Optimal Selector	Shanchan Wu, Kai Fan, Qiong Zhang	In this paper, we propose a method with neural noise converter to alleviate the impact of noisy data, and a conditional optimal selector to make proper prediction.
894	Response Generation by Context-Aware Prototype Editing	Yu Wu, Furu Wei, Shaohan Huang, Yunli Wang, Zhoujun Li, Ming Zhou	We propose a new paradigm, prototypethen-edit for response generation, that first retrieves a prototype response from a pre-defined index and then edits the prototype response according to the differences between the prototype context and current context.
895	Switch-Based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning	Yuexin Wu, Xiujun Li, Jingjing Liu, Jianfeng Gao, Yiming Yang	To this end, we extend the recently proposed Deep Dyna-Q (DDQ) framework by integrating a switcher that automatically determines whether to use a real or simulated experience for Q-learning.
896	Graph Based Translation Memory for Neural Machine Translation	Mengzhou Xia, Guoping Huang, Lemao Liu, Shuming Shi	We propose an efficient approach to making use of the global information in a TM.
897	Syntax-Aware Neural Semantic Role Labeling	Qingrong Xia, Zhenghua Li, Min Zhang, Meishan Zhang, Guohong Fu, Rui Wang, Luo Si	In this work, we investigate several previous approaches for encoding syntactic trees, and make a thorough study on whether extra syntax-aware representations are beneficial for neural SRL models.
898	Adaptive Region Embedding for Text Classification	Liuyu Xiang, Xiaoming Jin, Lan Yi, Guiguang Ding	In this work, we propose the Adaptive Region Embedding to learn context representation to improve text classification.
899	Quantifying Uncertainties in Natural Language Processing Tasks	Yijun Xiao, William Yang Wang	In this paper, we propose novel methods to study the benefits of characterizing model and data uncertainties for natural language processing (NLP) tasks.
900	Distributed Representation of Words in Cause and Effect Spaces	Zhipeng Xie, Feiteng Mu	This paper focuses on building up distributed representation of words in cause and effect spaces, a task-specific word embedding technique for causality.
901	Modeling Coherence for Discourse Neural Machine Translation	Hao Xiong, Zhongjun He, Hua Wu, Haifeng Wang	In this paper, we propose to use discourse context and reward to refine the translation quality from the discourse perspective.
902	End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis	Lin Xu, Qixian Zhou, Ke Gong, Xiaodan Liang, Jianheng Tang, Liang Lin	In this work, we propose an End-to-End Knowledge-routed Relational Dialogue System (KR-DS) that seamlessly incorporates rich medical knowledge graph into the topic transition in dialogue management, and makes it cooperative with natural language understanding and natural language generation.
903	A Deep Cascade Model for Multi-Document Reading Comprehension	Ming Yan, Jiangnan Xia, Chen Wu, Bin Bi, Zhongzhou Zhao, Ji Zhang, Luo Si, Rui Wang, Wei Wang, Haiqing Chen	To address this problem, we develop a novel deep cascade learning model, which progressively evolves from the documentlevel and paragraph-level ranking of candidate texts to more precise answer extraction with machine reading comprehension.
904	Exploring Human-Like Reading Strategy for Abstractive Text Summarization	Min Yang, Qiang Qu, Wenting Tu, Ying Shen, Zhou Zhao, Xiaojun Chen	Motivated by the humanlike reading strategy that follows a hierarchical routine, we propose a novel Hybrid learning model for Abstractive Text Summarization (HATS).
905	Graph Convolutional Networks for Text Classification	Liang Yao, Chengsheng Mao, Yuan Luo	In this work, we propose to use graph convolutional networks for text classification.
906	Plan-and-Write: Towards Better Automatic Storytelling	Lili Yao, Nanyun Peng, Ralph Weischedel, Kevin Knight, Dongyan Zhao, Rui Yan	In this paper, we explore open-domain story generation that writes stories given a title (topic) as input.
907	ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks	Michihiro Yasunaga, Jungo Kasai, Rui Zhang, Alexander R. Fabbri, Irene Li, Dan Friedman, Dragomir R. Radev	We 1) develop and release the first large-scale manually-annotated corpus for scientific papers (on computational linguistics) by enabling faster annotation, and 2) propose summarization methods that integrate the authors’ original highlights (abstract) and the article’s actual impacts on the community (citations), to create comprehensive, hybrid summaries.
908	TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts	Michihiro Yasunaga, John D. Lafferty	Inspired by the topical correspondence between mathematical equations and word contexts observed in scientific texts, we propose a novel topic model that jointly generates mathematical equations and their surrounding text (TopicEq). To experiment with this model, we create a corpus of 400K equation-context pairs extracted from a range of scientific articles from arXiv, and fit the model using a variational autoencoder approach.
909	Data Augmentation for Spoken Language Understanding via Joint Variational Generation	Kang Min Yoo, Youhyun Shin, Sang-goo Lee	In this paper, we propose a novel generative architecture which leverages the generative power of latent variable models to jointly synthesize fully annotated utterances.
910	Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inference	Masashi Yoshikawa, Koji Mineshima, Hiroshi Noji, Daisuke Bekki	In this work, we show the processing time of a state-of-the-art logic-based RTE system can be significantly reduced by replacing its search-based axiom injection (abduction) mechanism by that based on Knowledge Base Completion (KBC).
911	Distant Supervision for Relation Extraction with Linear Attenuation Simulation and Non-IID Relevance Embedding	Changsen Yuan, Heyan Huang, Chong Feng, Xiao Liu, Xiaochi Wei	Towards this end, we propose a novel approach to address these problems in this paper.
912	Exploring Answer Stance Detection with Recurrent Conditional Attention	Jianhua Yuan, Yanyan Zhao, Jingfang Xu, Bing Qin	To address them, we introduce the Recurrent Conditional Attention (RCA) model which incorporates a conditional attention structure into the recurrent reading process.
913	Bidirectional Transition-Based Dependency Parsing	Yunzhe Yuan, Yong Jiang, Kewei Tu	In this paper, we propose a simple framework for bidirectional transitionbased parsing.
914	DRr-Net: Dynamic Re-Read Network for Sentence Semantic Matching	Kun Zhang, Guangyi Lv, Linyuan Wang, Le Wu, Enhong Chen, Fangzhao Wu, Xing Xie	To this end, we propose a Dynamic Re-read Network (DRr-Net) approach for sentence semantic matching, which is able to pay close attention to a small region of sentences at each step and re-read the important words for better sentence semantic understanding.
915	A Generalized Language Model in Tensor Space	Lipeng Zhang, Peng Zhang, Xindian Ma, Shuqin Gu, Zhan Su, Dawei Song	In this paper, we propose a language model named Tensor Space Language Model (TSLM), by utilizing tensor networks and tensor decomposition.
916	Generating Chinese Ci with Designated Metrical Structure	Richong Zhang, Xinyu Liu, Xinwei Chen, Zhiyuan Hu, Zhaoqing Xu, Yongyi Mao	In this work, we adapt the CVAE framework to automated Ci generation under metrical constraints.
917	A Neural Network Approach to Verb Phrase Ellipsis Resolution	Wei-Nan Zhang, Yue Zhang, Yuanxing Liu, Donglin Di, Ting Liu	In this paper, we explore the advantages of neural models on VPE resolution in both pipeline and end-to-end processes, comparing the differences between statistical and neural models.
918	Generating Character Descriptions for Automatic Summarization of Fiction	Weiwei Zhang, Jackie Chi Kit Cheung, Joel Oren	In this work, we take a bottomup approach to this problem by assuming that story authors are uniquely qualified to inform such decisions. We collect a dataset of one million fiction stories with accompanying author-written summaries from Wattpad, an online story sharing platform.
919	Multi-Labeled Relation Extraction with Attentive Capsule Network	Xinsong Zhang, Pengshuai Li, Weijia Jia, Hai Zhao	To tackle with the new issue, we propose a novel approach for multi-labeled relation extraction with capsule network which acts considerably better than current convolutional or recurrent net in identifying the highly overlapped relations within an individual sentence.
920	An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Loss	Peixiang Zhong, Di Wang, Chunyan Miao	In this paper, we propose an endto-end affect-rich open-domain neural conversational model that produces responses not only appropriate in syntax and semantics, but also with rich affect.
921	Plan-Length Bounds: Beyond 1-Way Dependency	Mohammad Abdulaziz	We consider the problem of compositionally computing upper bounds on lengths of plans.
922	Measurement Maximizing Adaptive Sampling with Risk Bounding Functions	Benjamin Ayton, Brian Williams, Richard Camilli	This paper considers an autonomous agent tasked with maximizing measurements from a Gaussian Process while subject to unbounded disturbances.
923	Bringing Order to Chaos – A Compact Representation of Partial Order in SAT-Based HTN Planning	Gregor Behnke, Daniel Höller, Susanne Biundo	In this paper, we introduce a novel encoding of HTN Planning as SAT.
924	Deep Reactive Policies for Planning in Stochastic Nonlinear Domains	Thiago P. Bueno, Leliane N. de Barros, Denis D. Mauá, Scott Sanner	In order to overcome those limitations, we introduce a framework for training DRPs in continuous stochastic spaces via gradient-based policy search.
925	Robustness Envelopes for Temporal Plans	Michael Cashmore, Alessandro Cimatti, Daniele Magazzeni, Andrea Micheli, Parisa Zehtabi	We propose an approach to compute the “robustness envelope” (i.e., alternative action durations or resource consumption rates) of a given STN plan, for which the plan remains valid.
926	Improving Domain-Independent Planning via Critical Section Macro-Operators	Lukáš Chrpa, Mauro Vallati	This paper presents a technique that, inspired by resource locking in critical sections in parallel computing, learns macros capturing activities in which a limited resource (e.g., a robotic hand) is used.
927	Efficient Temporal Planning Using Metastates	Amanda Coles, Andrew Coles, J. Christopher Beck	In this paper, we present an approach for reducing the state space explosion that arises due to having to keep many copies of the same ‘classically’ equal state – states that are classically equal are aggregated into metastates, and these are separated lazily only in the case of temporal inconsistency.
928	Efficiently Reasoning with Interval Constraints in Forward Search Planning	Amanda Coles, Andrew Coles, Moises Martinez, Emre Savas, Juan Manuel Delfa, Tomás de la Rosa, Yolanda E-Martín, Angel García-Olaya	In this paper we present techniques for reasoning natively with quantitative/qualitative interval constraints in statebased PDDL planners.
929	Learning Resource Allocation and Pricing for Cloud Profit Maximization	Bingqian Du, Chuan Wu, Zhiyi Huang	Existing studies establish detailed performance models of cloud resource usage, and propose offline or online algorithms to decide allocation and pricing.
930	Refining Abstraction Heuristics during Real-Time Planning	Rebecca Eifler, Maximilian Fickert, Jörg Hoffmann, Wheeler Ruml	In this paper, we explore the use of abstraction-based heuristics.
931	Operator Mutexes and Symmetries for Simplifying Planning Tasks	Daniel Fišer, álvaro Torralba, Alexander Shleyfman	In this paper, we introduce the notion of operator mutex, which is a set of operators that cannot all be part of the same (strongly) optimal plan.
932	Solving Multiagent Planning Problems with Concurrent Conditional Effects	Daniel Furelos-Blanco, Anders Jonsson	In this work we present a novel approach to solving concurrent multiagent planning problems in which several agents act in parallel.
933	Learning How to Ground a Plan – Partial Grounding in Classical Planning	Daniel Gnad, álvaro Torralba, Martín Domínguez, Carlos Areces, Facundo Bustos	To address this issue, we introduce a partial grounding approach that grounds only a projection of the task, when complete grounding is not feasible.
934	Generalized Planning via Abstraction: Arbitrary Numbers of Objects	León Illanes, Sheila A. McIlraith	We show how we can adapt fully observable nondeterministic planning techniques to produce generalized solutions that are easy to instantiate over particular problem instances.
935	Red-Black Heuristics for Planning Tasks with Conditional Effects	Michael Katz	Consequently, we investigate the possibility of handling conditional effects directly in the red-black planning heuristic function, extending the algorithm for computing red-black plans to the conditional effects setting.
936	Multi-Agent Path Finding for Large Agents	Jiaoyang Li, Pavel Surynek, Ariel Felner, Hang Ma, T. K. Satish Kumar, Sven Koenig	In this paper, we formalize and study LAMAPF, i.e., MAPF for large agents.
937	Moral Permissibility of Action Plans	Felix Lindner, Robert Mattmüller, Bernhard Nebel	We address this challenge by analyzing in how far it is possible to generalize existing approaches of machine ethics to automatic planning systems.
938	Searching with Consistent Prioritization for Multi-Agent Path Finding	Hang Ma, Daniel Harabor, Peter J. Stuckey, Jiaoyang Li, Sven Koenig	We study prioritized planning for Multi-Agent Path Finding (MAPF).
939	Lifelong Path Planning with Kinematic Constraints for Multi-Agent Pickup and Delivery	Hang Ma, Wolfgang Hönig, T. K. Satish Kumar, Nora Ayanian, Sven Koenig	We make TP even more efficient and effective by using a novel combinatorial search algorithm, called Safe Interval Path Planning with Reservation Table (SIPPwRT), for single-agent path planning.
940	Performance Guarantees for Homomorphisms beyond Markov Decision Processes	Sultan Javed Majeed, Marcus Hutter	Performance Guarantees for Homomorphisms beyond Markov Decision Processes
941	Sliding Window Temporal Graph Coloring	George B. Mertzios, Hendrik Molter, Viktor Zamaraev	In this paper we present a natural temporal extension of the classical graph coloring problem.
942	Temporal Planning with Temporal Metric Trajectory Constraints	Andrea Micheli, Enrico Scala	The paper details the semantics of our new formalism and presents a solving technique grounded in classical, heuristic forward search planning.
943	Automated Verification of Social Laws for Continuous Time Multi-Robot Systems	Ronen Nir, Erez Karpas	In this paper, we show how the robustness of a social law in a continuous time setting can be verified through compilation to temporal planning.
944	Acting and Planning Using Operational Models	Sunandita Patra, Malik Ghallab, Dana Nau, Paolo Traverso	We define and implement an integrated acting-and-planning system in which both planning and acting use the same operational models, which are written in a general-purpose hierarchical task-oriented language offering rich control structures.
945	Distribution-Based Semi-Supervised Learning for Activity Recognition	Hangwei Qian, Sinno Jialin Pan, Chunyan Miao	Therefore, in this paper, we propose a novel method, named Distribution-based Semi-Supervised Learning, to tackle the aforementioned limitations.
946	An Innovative Genetic Algorithm for the Quantum Circuit Compilation Problem	Riccardo Rasconi, Angelo Oddi	This paper investigates the performance of a genetic algorithm to optimize the realization (compilation) of nearest-neighbor compliant quantum circuits.
947	Deep Learning for Cost-Optimal Planning: Task-Dependent Planner Selection	Silvan Sievers, Michael Katz, Shirin Sohrabi, Horst Samulowitz, Patrick Ferber	In this work, we alleviate this barrier.
948	Rotational Diversity in Multi-Cycle Assignment Problems	Helge Spieker, Arnaud Gotlieb, Morten Mossige	We approach the multi-cycle assignment problem as a two-part problem: Profit maximization and rotation are combined into one objective value, and then solved as a General Assignment Problem.
949	Online Multi-Agent Pathfinding	Jiří švancara, Marek Vlk, Roni Stern, Dor Atzmon, Roman Barták	In this work, we study the online version of MAPF where new agents appear over time.
950	Active Preference Learning Based on Generalized Gini Functions: Application to the Multiagent Knapsack Problem	Nadjet Bourdache, Patrice Perny	We introduce and compare several incremental decision procedures interleaving an adaptive preference elicitation procedure with a combinatorial optimization algorithm to determine a GSF-optimal solution.
951	Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications	Daniel S. Brown, Scott Niekum	We formalize the problem of finding maximally informative demonstrations for IRL as a machine teaching problem where the goal is to find the minimum number of demonstrations needed to specify the reward equivalence class of the demonstrator.
952	Robustness Guarantees for Bayesian Inference with Gaussian Processes	Luca Cardelli, Marta Kwiatkowska, Luca Laurenti, Andrea Patane	In this paper we define a robustness measure for Bayesian inference against input perturbations, given by the probability that, for a test point and a compact set in the input space containing the test point, the prediction of the learning model will remain δ−close for all the points in the set, for δ > 0.
953	Probabilistic Logic Programming with Beta-Distributed Random Variables	Federico Cerutti, Lance Kaplan, Angelika Kimmig, Murat Şensoy	We enable aProbLog—a probabilistic logical programming approach—to reason in presence of uncertain probabilities represented as Beta-distributed random variables.
954	On Testing of Uniform Samplers	Sourav Chakraborty, Kuldeep S. Meel	The primary contribution of this paper is an affirmative answer to the above question when the given distribution is a uniform distribution: We design, to the best of our knowledge, the first algorithmic framework, Barbarik, to test whether the distribution generated is ε−close or η−far from the uniform distribution.
955	On the Hardness of Probabilistic Inference Relaxations	Supratik Chakraborty, Kuldeep S. Meel, Moshe Y. Vardi	In this paper, we show that contrary to common belief, several relaxations used for model counting and its applications (including probablistic inference) do not really lead to computational efficiency in a complexity theoretic sense.
956	Learning Diverse Bayesian Networks	Cong Chen, Changhe Yuan	This paper proposes a novel method for finding a set of diverse top Bayesian networks, called modes, such that each network is guaranteed to be optimal in a local neighborhood.
957	Path-Specific Counterfactual Fairness	Silvia Chiappa	We introduce a counterfactual approach to disregard effects along unfair pathways that does not incur in the same loss of individual-specific information as previous approaches.
958	Efficient Optimal Approximation of Discrete Random Variables for Estimation of Probabilities of Missing Deadlines	Liat Cohen, Gera Weiss	We present an efficient algorithm that, given a discrete random variable X and a number m, computes a random variable whose support is of size at most m and whose Kolmogorov distance from X is minimal.
959	Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs	Mayukh Das, Devendra Singh Dhami, Gautam Kunapuli, Kristian Kersting, Sriraam Natarajan	Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs
960	Exact and Approximate Weighted Model Integration with Probability Density Functions Using Knowledge Compilation	Pedro Zuidberg Dos Martires, Anton Dries, Luc De Raedt	We show how standard knowledge compilation techniques (to SDDs and d-DNNFs) apply to weighted model integration, and use it in two novel solvers, one exact and one approximate solver.
961	Marginal Inference in Continuous Markov Random Fields Using Mixtures	Yuanzhen Guo, Hao Xiong, Nicholas Ruozzi	In this work, we present an alternative family of approximations that, instead of approximating the messages, approximates the beliefs in the continuous Bethe free energy using mixture distributions.
962	A Generative Model for Dynamic Networks with Applications	Shubham Gupta, Gaurav Sharma, Ambedkar Dukkipati	In this paper, we propose a generative, latent space based, statistical model for such networks (called dynamic networks).
963	Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems	Trong Nghia Hoang, Quang Minh Hoang, Kian Hsiang Low, Jonathan How	This paper presents a novel Collective Online Learning of Gaussian Processes (COOL-GP) framework for enabling a massive number of GP inference agents to simultaneously perform (a) efficient online updates of their GP models using their local streaming data with varying correlation structures and (b) decentralized fusion of their resulting online GP models with different learned hyperparameter settings and inducing inputs.
964	MFBO-SSM: Multi-Fidelity Bayesian Optimization for Fast Inference in State-Space Models	Mahdi Imani, Seyede Fatemeh Ghoreishi, Douglas Allaire, Ulisses M. Braga-Neto	In this paper, we propose a multi-fidelity Bayesian optimization algorithm for the inference of general nonlinear state-space models (MFBO-SSM), which enables simultaneous sequential selection of parameters and approximators.
965	Polynomial-Time Probabilistic Reasoning with Partial Observations via Implicit Learning in Probability Logics	Brendan Juba	In this work we consider the use of bounded-degree fragments of the “sum-of-squares” logic as a probability logic.
966	Randomized Strategies for Robust Combinatorial Optimization	Yasushi Kawase, Hanna Sumita	In this paper, we study the following robust optimization problem.
967	Dirichlet Multinomial Mixture with Variational Manifold Regularization: Topic Modeling over Short Texts	Ximing Li, Jiaojiao Zhang, Jihong Ouyang	In this paper, we alleviate this problem by preserving local neighborhood structure of short texts, enabling to spread topical signals among neighboring documents, so as to correct the inaccurate topic representations.
968	Finding All Bayesian Network Structures within a Factor of Optimal	Zhenyu A. Liao, Charupriya Sharma, James Cussens, Peter van Beek	In this paper, we propose a novel approach to model averaging inspired by performance guarantees in approximation algorithms.
969	Interleave Variational Optimization with Monte Carlo Sampling: A Tale of Two Approximate Inference Paradigms	Qi Lou, Rina Dechter, Alexander Ihler	In this paper, we propose a general framework that interleaves optimization of variational bounds (via message passing) with Monte Carlo sampling.
970	Robust Ordinal Embedding from Contaminated Relative Comparisons	Ke Ma, Qianqian Xu, Xiaochun Cao	In this paper, we propose a unified framework to jointly identify the contaminated comparisons and derive reliable embeddings.
971	On Lifted Inference Using Neural Embeddings	Mohammad Maminur Islam, Somdeb Sarkhel, Deepak Venugopal	We present a dense representation for Markov Logic Networks (MLNs) called Obj2Vec that encodes symmetries in the MLN structure.
972	Anytime Recursive Best-First Search for Bounding Marginal MAP	Radu Marinescu, Akihiro Kishimoto, Adi Botea, Rina Dechter, Alexander Ihler	In this paper, we introduce a new recursive best-first search based bounding scheme that operates efficiently within limited memory and computes anytime upper and lower bounds that improve over time.
973	Semi-Parametric Sampling for Stochastic Bandits with Many Arms	Mingdong Ou, Nan Li, Cheng Yang, Shenghuo Zhu, Rong Jin	In this paper, we propose a novel Bayesian framework, called Semi-Parametric Sampling (SPS), for this problem, which employs semi-parametric function as the reward model.
974	Memory Bounded Open-Loop Planning in Large POMDPs Using Thompson Sampling	Thomy Phan, Lenz Belzner, Marie Kiermeier, Markus Friedrich, Kyrill Schmid, Claudia Linnhoff-Popien	In this paper, we propose Partially Observable Stacked Thompson Sampling (POSTS), a memory bounded approach to openloop planning in large POMDPs, which optimizes a fixed size stack of Thompson Sampling bandits.
975	Rethinking the Discount Factor in Reinforcement Learning: A Decision Theoretic Approach	Silviu Pitis	This paper characterizes rationality in sequential decision making using a set of seven axioms and arrives at a form of discounting that generalizes traditional fixed discounting.
976	Structured Bayesian Networks: From Inference to Learning with Routes	Yujia Shen, Anchal Goyanka, Adnan Darwiche, Arthur Choi	These SBNs yield a tractable model of route distributions, whose structure can be learned from GPS data, using a simple algorithm that we propose.
977	Compiling Bayesian Network Classifiers into Decision Graphs	Andy Shih, Arthur Choi, Adnan Darwiche	We propose an algorithm for compiling Bayesian network classifiers into decision graphs that mimic the input and output behavior of the classifiers.
978	Lifted Hinge-Loss Markov Random Fields	Sriram Srinivasan, Behrouz Babaki, Golnoosh Farnadi, Lise Getoor	In this paper, we show how to effectively combine two powerful ideas for scaling inference for large graphical models.
979	Counting and Sampling Markov Equivalent Directed Acyclic Graphs	Topi Talvitie, Mikko Koivisto	These problems efficiently reduce to counting the moral acyclic orientations of a given undirected connected chordal graph on n vertices, for which we give two algorithms.
980	Optimizing Discount and Reputation Trade-Offs in E-Commerce Systems: Characterization and Online Learning	Hong Xie, Yongkun Li, John C. S. Lui	We propose to enhance sellers’ reputation via price discounts.
981	Depth Prediction without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos	Vincent Casser, Soeren Pirk, Reza Mahjourian, Anelia Angelova	In this work we address unsupervised learning of scene depth and robot ego-motion where supervision is provided by monocular videos, as cameras are the cheapest, least restrictive and most ubiquitous sensor for robotics.
982	MotionTransformer: Transferring Neural Inertial Tracking between Domains	Changhao Chen, Yishu Miao, Chris Xiaoxuan Lu, Linhai Xie, Phil Blunsom, Andrew Markham, Niki Trigoni	To overcome the challenges of domain adaptation on long sensory sequences, we propose MotionTransformer – a novel framework that extracts domain-invariant features of raw sequences from arbitrary domains, and transforms to new domains without any paired data.
983	Adversarial Actor-Critic Method for Task and Motion Planning Problems Using Planning Experience	Beomjoon Kim, Leslie Pack Kaelbling, Tomás Lozano-Pérez	We propose an actor-critic algorithm that uses past planning experience to improve the efficiency of solving robot task-and-motion planning (TAMP) problems.
984	Mirroring without Overimitation: Learning Functionally Equivalent Manipulation Actions	Hangxin Liu, Chi Zhang, Yixin Zhu, Chenfanfu Jiang, Song-Chun Zhu	This paper presents a mirroring approach, inspired by the neuroscience discovery of the mirror neurons, to transfer demonstrated manipulation actions to robots.
985	Visual Place Recognition via Robust ℓ2-Norm Distance Based Holism and Landmark Integration	Kai Liu, Hua Wang, Fei Han, Hao Zhang	In this paper, we propose a novel method to learn a location representation that can integrate the semantic landmarks of a place with its holistic representation.
986	Deictic Image Mapping: An Abstraction for Learning Pose Invariant Manipulation Policies	Robert Platt, Colin Kohler, Marcus Gualtieri	This paper proposes a novel state and action abstraction that is invariant to pose shifts called deictic image maps that can be used with deep reinforcement learning.
987	Personalized Robot Tutoring Using the Assistive Tutor POMDP (AT-POMDP)	Aditi Ramachandran, Sarah Strohkorb Sebo, Brian Scassellati	In this work, we formulate the robot-student tutoring help action selection problem as the Assistive Tutor partially observable Markov decision process (AT-POMDP).
988	That’s Mine! Learning Ownership Relations and Norms for Robots	Zhi-Xuan Tan, Jake Brawer, Brian Scassellati	Here, we present a robotic system capable of representing, learning, and inferring ownership relations and norms.
989	Probabilistic Model Checking of Robots Deployed in Extreme Environments	Xingyu Zhao, Valentin Robu, David Flynn, Fateme Dinmohammadi, Michael Fisher, Matt Webster	In this paper, we develop a framework for probabilistic model checking on a layered Markov model to verify the safety and reliability requirements of such robots, both at pre-mission stage and during runtime.
990	TallyQA: Answering Complex Counting Questions	Manoj Acharya, Kushal Kafle, Christopher Kanan	We propose a new algorithm for counting that uses relation networks with region proposals.
991	Densely Supervised Grasp Detector (DSGD)	Umar Asif, Jianbin Tang, Stefan Harrer	This paper presents Densely Supervised Grasp Detector (DSGD), a deep learning framework which combines CNN structures with layer-wise feature fusion and produces grasps and their confidence scores at different levels of the image hierarchy (i.e., global-, region-, and pixel-levels).
992	Object Detection Based on Region Decomposition and Assembly	Seung-Hwan Bae	In this paper, we therefore propose a region decomposition and assembly detector (R-DAD) for more accurate object detection.
993	BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection	Hedi Ben-younes, Remi Cadene, Nicolas Thome, Matthieu Cord	In this paper, we introduce BLOCK, a new multimodal fusion based on the block-superdiagonal tensor decomposition.
994	MR-NET: Exploiting Mutual Relation for Visual Relationship Detection	Yi Bin, Yang Yang, Chaofan Tao, Zi Huang, Jingjing Li, Heng Tao Shen	In this work, we propose a mutual relation net, dubbed MR-Net, to explore the mutual relation between paired objects for visual relationship detection.
995	Action Knowledge Transfer for Action Prediction with Partial Videos	Yijun Cai, Haoxin Li, Jian-Fang Hu, Wei-Shi Zheng	To tackle this challenge, in this work, we propose to transfer action knowledge learned from fully observed videos for improving the prediction of partially observed videos.
996	GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition	Hanqing Chao, Yiwei He, Junping Zhang, Jianfeng Feng	In this paper we present a novel perspective, where a gait is regarded as a set consisting of independent frames.
997	Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering	Binghui Chen, Weihong Deng	We train this confusion term together with the conventional metric objective in an adversarial manner.
998	Improving Image Captioning with Conditional Generative Adversarial Nets	Chen Chen, Shuai Mu, Wanpeng Xiao, Zexiong Ye, Liesi Wu, Qi Ju	In this paper, we propose a novel conditional-generativeadversarial-nets-based image captioning framework as an extension of traditional reinforcement-learning (RL)-based encoder-decoder architecture.
999	Unsupervised Stylish Image Description Generation via Domain Layer Norm	Cheng-Kuan Chen, Zhufeng Pan, Ming-Yu Liu, Min Sun	To address these limitations, we propose a controllable stylish image description generation model.
1000	Unsupervised Meta-Learning of Figure-Ground Segmentation via Imitating Visual Effects	Ding-Jie Chen, Jui-Ting Chien, Hwann-Tzong Chen, Tyng-Luh Liu	This paper presents a “learning to learn” approach to figureground image segmentation.
1001	Temporal Deformable Convolutional Encoder-Decoder Networks for Video Captioning	Jingwen Chen, Yingwei Pan, Yehao Li, Ting Yao, Hongyang Chao, Tao Mei	In this paper, we present a novel design — Temporal Deformable Convolutional Encoder-Decoder Networks (dubbed as TDConvED) that fully employ convolutions in both encoder and decoder networks for video captioning.
1002	Localizing Natural Language in Videos	Jingyuan Chen, Lin Ma, Xinpeng Chen, Zequn Jie, Jiebo Luo	In this paper, we consider the task of natural language video localization (NLVL): given an untrimmed video and a natural language description, the goal is to localize a segment in the video which semantically corresponds to the given natural language description.
1003	Similarity Preserving Deep Asymmetric Quantization for Image Retrieval	Junjie Chen, William K. Cheung	To alleviate this problem, we propose a novel model called Similarity Preserving Deep Asymmetric Quantization (SPDAQ) which can directly learn the compact binary codes and quantization codebooks for all the items in the database efficiently.
1004	Motion Guided Spatial Attention for Video Captioning	Shaoxiang Chen, Yu-Gang Jiang	Motivated by this, we aim to learn spatial attention on video frames under the guidance of motion information for caption generation.
1005	Semantic Proposal for Activity Localization in Videos via Sentence Query	Shaoxiang Chen, Yu-Gang Jiang	This paper presents an efficient algorithm to tackle temporal localization of activities in videos via sentence queries.
1006	Unsupervised Bilingual Lexicon Induction from Mono-Lingual Multimodal Data	Shizhe Chen, Qin Jin, Alexander Hauptmann	We propose a multi-lingual caption model trained with different mono-lingual multimodal data to map words in different languages into joint spaces.
1007	Learning Resolution-Invariant Deep Representations for Person Re-Identification	Yun-Chun Chen, Yu-Jhe Li, Xiaofei Du, Yu-Chiang Frank Wang	Instead of applying separate image super-resolution models, we propose a novel network architecture of Resolution Adaptation and re-Identification Network (RAIN) to solve cross-resolution person re-ID.
1008	Data Fine-Tuning	Saheb Chhabra, Puspita Majumdar, Mayank Vatsa, Richa Singh	Stimulated by the advances in adversarial perturbations, this research proposes the concept of Data Fine-tuning to improve the classification accuracy of a given model without changing the parameters of the model.
1009	Selective Refinement Network for High Performance Face Detection	Cheng Chi, Shifeng Zhang, Junliang Xing, Zhen Lei, Stan Z. Li, Xudong Zou	This paper presents a novel single-shot face detector, named Selective Refinement Network (SRN), which introduces novel twostep classification and regression operations selectively into an anchor-based face detector to reduce false positives and improve location accuracy simultaneously.
1010	Residual Compensation Networks for Heterogeneous Face Recognition	Zhongying Deng, Xiaojiang Peng, Yu Qiao	In this paper, we propose a new two-branch network architecture, termed as Residual Compensation Networks (RCN), to learn separated features for different modalities in HFR.
1011	Attention-Aware Sampling via Deep Reinforcement Learning for Action Recognition	Wenkai Dong, Zhaoxiang Zhang, Tieniu Tan	In this paper, we propose an attentionaware sampling method for action recognition, which aims to discard the irrelevant and misleading frames and preserve the most discriminative frames.
1012	Learning a Deep Convolutional Network for Colorization in Monochrome-Color Dual-Lens System	Xuan Dong, Weixin Li, Xiaojie Wang, Yunhong Wang	We propose a novel deep convolution network to solve the colorization problem in an end-to-end way.
1013	Cubic LSTMs for Video Prediction	Hehe Fan, Linchao Zhu, Yi Yang	Motivated by this analysis, we propose a Cubic Long Short-Term Memory (CubicLSTM) unit for video prediction.
1014	Fully Convolutional Video Captioning with Coarse-to-Fine and Inherited Attention	Kuncheng Fang, Lian Zhou, Cheng Jin, Yuejie Zhang, Kangnian Weng, Tao Zhang, Weiguo Fan	To tackle the obstacles of traditional LSTM-based model for video captioning, we propose a novel architecture to generate the optimal descriptions for videos, which focuses on constructing a new network structure that can generate sentences superior to the basic model with LSTM, and establishing special attention mechanisms that can provide more useful visual information for caption generation.
1015	MeshNet: Mesh Neural Network for 3D Shape Representation	Yutong Feng, Yifan Feng, Haoxuan You, Xibin Zhao, Yue Gao	In this paper, we propose a mesh neural network, named MeshNet, to learn 3D shape representation from mesh data.
1016	STA: Spatial-Temporal Attention for Large-Scale Video-Based Person Re-Identification	Yang Fu, Xiaoyang Wang, Yunchao Wei, Thomas Huang	In this work, we propose a novel Spatial-Temporal Attention (STA) approach to tackle the large-scale person reidentification task in videos.
1017	Horizontal Pyramid Matching for Person Re-Identification	Yang Fu, Yunchao Wei, Yuqian Zhou, Honghui Shi, Gao Huang, Xinchao Wang, Zhiqiang Yao, Thomas Huang	To mitigate this type of failure, we propose a simple yet effective Horizontal Pyramid Matching (HPM) approach to fully exploit various partial information of a given person, so that correct person candidates can be identified even if some key parts are missing.
1018	I Know the Relationships: Zero-Shot Action Recognition via Two-Stream Graph Convolutional Networks and Knowledge Graphs	Junyu Gao, Tianzhu Zhang, Changsheng Xu	To narrow the knowledge gap between existing methods and humans, we propose an end-to-end ZSAR framework based on a structured knowledge graph, which can jointly model the relationships between action-attribute, action-action, and attribute-attribute.
1019	Perceptual Pyramid Adversarial Networks for Text-to-Image Synthesis	Lianli Gao, Daiyuan Chen, Jingkuan Song, Xing Xu, Dongxiang Zhang, Heng Tao Shen	In this paper, we propose Perceptual Pyramid Adversarial Network (PPAN) to directly synthesize multi-scale images conditioned on texts in an adversarial way.
1020	Deliberate Attention Networks for Image Captioning	Lianli Gao, Kaixuan Fan, Jingkuan Song, Xianglong Liu, Xing Xu, Heng Tao Shen	In this paper, we present a novel Deliberate Residual Attention Network, namely DA, for image captioning.
1021	Video Imprint Segmentation for Temporal Action Detection in Untrimmed Videos	Zhanning Gao, Le Wang, Qilin Zhang, Zhenxing Niu, Nanning Zheng, Gang Hua	We propose a temporal action detection by spatial segmentation framework, which simultaneously categorize actions and temporally localize action instances in untrimmed videos.
1022	No-Reference Image Quality Assessment with Reinforcement Recursive List-Wise Ranking	Jie Gu, Gaofeng Meng, Cheng Da, Shiming Xiang, Chunhong Pan	In this paper, we propose an effective opinion-unaware NR-IQA method based on reinforcement recursive list-wise ranking.
1023	Projection Convolutional Neural Networks for 1-bit CNNs via Discrete Back Propagation	Jiaxin Gu, Ce Li, Baochang Zhang, Jungong Han, Xianbin Cao, Jianzhuang Liu, David Doermann	In this paper, we introduce projection convolutional neural networks (PCNNs) with a discrete back propagation via projection (DBPP) to improve the performance of binarized neural networks (BNNs).
1024	Human Action Transfer Based on 3D Model Reconstruction	Shanyan Guan, Shuo Wen, Dexin Yang, Bingbing Ni, Wendong Zhang, Jun Tang, Xiaokang Yang	We present a practical and effective method for human action transfer.
1025	Dual-View Ranking with Hardness Assessment for Zero-Shot Learning	Yuchen Guo, Guiguang Ding, Jungong Han, Xiaohan Ding, Sicheng Zhao, Zheng Wang, Chenggang Yan, Qionghai Dai	Motivated by it, we propose a novel DuAl-view RanKing (DARK) loss for zeroshot learning simultaneously ranking labels for an image by point-to-point metric and ranking images for a label by pointto-set metric, which is capable of better modeling the relationship between images and classes.
1026	Depthwise Convolution Is All You Need for Learning Multiple Visual Domains	Yunhui Guo, Yandong Li, Liqiang Wang, Tajana Rosing	In this paper, we propose a multi-domain learning architecture based on depthwise separable convolution.
1027	View Inter-Prediction GAN: Unsupervised Representation Learning for 3D Shapes by Learning Global Shape Memories to Support Local View Predictions	Zhizhong Han, Mingyang Shang, Yu-Shen Liu, Matthias Zwicker	In this paper, we present a novel unsupervised representation learning approach for 3D shapes, which is an important research challenge as it avoids the manual effort required for collecting supervised data.
1028	HSME: Hypersphere Manifold Embedding for Visible Thermal Person Re-Identification	Yi Hao, Nannan Wang, Jie Li, Xinbo Gao	In this paper, we use Sphere Softmax to learn a hypersphere manifold embedding and constrain the intra-modality variations and cross-modality variations on this hypersphere.
1029	Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos	Dongliang He, Xiang Zhao, Jizhou Huang, Fu Li, Xiao Liu, Shilei Wen	Specifically, we propose a reinforcement learning based framework improved by multi-task learning and it shows steady performance gains by considering additional supervised boundary information during training.
1030	StNet: Local and Global Spatial-Temporal Modeling for Action Recognition	Dongliang He, Zhichao Zhou, Chuang Gan, Fu Li, Xiao Liu, Yandong Li, Limin Wang, Shilei Wen	In this paper, in contrast to the existing CNN+RNN or pure 3D convolution based approaches, we explore a novel spatialtemporal network (StNet) architecture for both local and global modeling in videos.
1031	Mono3D++: Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses and Task Priors	Tong He, Stefano Soatto	We present a method to infer 3D pose and shape of vehicles from a single image.
1032	Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks	Xiang He, Sibei Yang, Guanbin Li, Haofeng Li, Huiyou Chang, Yizhou Yu	In this paper, we discover that global spatial dependencies and global contextual information in a biomedical image can be exploited to defend against adversarial attacks.
1033	Weighted Channel Dropout for Regularization of Deep Convolutional Neural Network	Saihui Hou, Zilei Wang	In this work, we propose a novel method named Weighted Channel Dropout (WCD) for the regularization of deep Convolutional Neural Network (CNN).
1034	Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks	Yuenan Hou, Zheng Ma, Chunxiao Liu, Chen Change Loy	In this paper, we considerably improve the accuracy and robustness of predictions through heterogeneous auxiliary networks feature mimicking, a new and effective training method that provides us with much richer contextual signals apart from steering direction.
1035	Attention-Based Multi-Context Guiding for Few-Shot Semantic Segmentation	Tao Hu, Pengwan Yang, Chiliang Zhang, Gang Yu, Yadong Mu, Cees G. M. Snoek	To tackle this issue, we propose an Attentionbased Multi-Context Guiding (A-MCG) network, which consists of three branches: the support branch, the query branch, the feature fusion branch.
1036	A Novel Framework for Robustness Analysis of Visual QA Models	Jia-Hong Huang, Cuong Duc Dao, Modar Alfadly, Bernard Ghanem	In this work, we propose a flexible framework that focuses on the language part of VQA that uses semantically relevant questions, dubbed basic questions, acting as controllable noise to evaluate the robustness of VQA models. Then, we propose a novel robustness measure Rscore and two largescale basic question datasets (BQDs) in order to standardize robustness analysis for VQA models.
1037	Re<sup>2</sup>EMA: Regularized and Reinitialized Exponential Moving Average for Target Model Update in Object Tracking	Jianglei Huang, Wengang Zhou	In this work, we propose to achieve an optimal target model by learning a transformation matrix from the last target model to the newly generated one, which results into a minimization objective.
1038	Hierarchically Structured Reinforcement Learning for Topically Coherent Visual Story Generation	Qiuyuan Huang, Zhe Gan, Asli Celikyilmaz, Dapeng Wu, Jianfeng Wang, Xiaodong He	We propose a hierarchically structured reinforcement learning approach to address the challenges of planning for generating coherent multi-sentence stories for the visual storytelling task.
1039	A Framework to Coordinate Segmentation and Recognition	Wei Huang, Huimin Yu, Weiwei Zheng, Jing Zhang	A novel coordination framework between the segmentation and the recognition is proposed, to conduct the two tasks collaboratively and iteratively.
1040	3D Volumetric Modeling with Introspective Neural Networks	Wenlong Huang, Brian Lai, Weijian Xu, Zhuowen Tu	In this paper, we study the 3D volumetric modeling problem by adopting the Wasserstein introspective neural networks method (WINN) that was previously applied to 2D static images.
1041	Few-Shot Image and Sentence Matching via Gated Visual-Semantic Embedding	Yan Huang, Yang Long, Liang Wang	In this work, we focus on this challenging problem of few-shot image and sentence matching, and propose a Gated Visual-Semantic Embedding (GVSE) model to deal with it.
1042	Attentive Temporal Pyramid Network for Dynamic Scene Classification	Yuanjun Huang, Xianbin Cao, Xiantong Zhen, Jungong Han	In this paper, we propose the attentive temporal pyramid network (ATP-Net) to establish effective representations of dynamic scenes by extracting and aggregating the most informative and discriminative features.
1043	DeepCCFV: Camera Constraint-Free Multi-View Convolutional Neural Network for 3D Object Retrieval	Zhengyue Huang, Zhehui Zhao, Hengguang Zhou, Xibin Zhao, Yue Gao	In this paper, we investigate the over-fitting issue and remove the constraint of the camera setting.
1044	MLVCNN: Multi-Loop-View Convolutional Neural Network for 3D Shape Retrieval	Jianwen Jiang, Di Bao, Ziqiang Chen, Xibin Zhao, Yue Gao	In order to tackle these problems, in this paper, we propose a multi-loop-view convolutional neural network (MLVCNN) framework for 3D shape retrieval.
1045	Image Saliency Prediction in Transformed Domain: A Deep Complex Neural Network Method	Lai Jiang, Zhe Wang, Mai Xu, Zulin Wang	In this paper, we propose a novel deep complex neural network, named SalDCNN, to predict image saliency by learning features in both pixel and transformed domains.
1046	Video Object Detection with Locally-Weighted Deformable Neighbors	Zhengkai Jiang, Peng Gao, Chaoxu Guo, Qian Zhang, Shiming Xiang, Chunhong Pan	In this paper, we propose LWDN (Locally-Weighted Deformable Neighbors) for video object detection without utilizing time-consuming optical flow extraction networks.
1047	Discriminative Feature Learning for Unsupervised Video Summarization	Yunjae Jung, Donghyeon Cho, Dahun Kim, Sanghyun Woo, In So Kweon	In this paper, we address the problem of unsupervised video summarization that automatically extracts key-shots from an input video.
1048	Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles	Dahun Kim, Donghyeon Cho, In So Kweon	In this paper, we introduce a new self-supervised task called as Space-Time Cubic Puzzles to train 3D CNNs using large scale video dataset.
1049	BiHMP-GAN: Bidirectional 3D Human Motion Prediction GAN	Jogendra Nath Kundu, Maharshi Gor, R. Venkatesh Babu	Devoid of this, we propose a novel probabilistic generative approach called Bidirectional Human motion prediction GAN, or BiHMP-GAN.
1050	Spatio-Temporal Graph Routing for Skeleton-Based Action Recognition	Bin Li, Xi Li, Zhongfei Zhang, Fei Wu	In this paper, we propose a novel spatio-temporal graph routing (STGR) scheme for skeletonbased action recognition, which adaptively learns the intrinsic high-order connectivity relationships for physicallyapart skeleton joints.
1051	SuperVAE: Superpixelwise Variational Autoencoder for Salient Object Detection	Bo Li, Zhengxing Sun, Yuqi Guo	In this paper, we propose a novel salient object detection framework using a superpixelwise variational autoencoder (SuperVAE) network.
1052	Gradient Harmonized Single-Stage Detector	Buyu Li, Yu Liu, Xiaogang Wang	In this work, we first point out that the essential effect of the two disharmonies can be summarized in term of the gradient.
1053	Skeleton-Based Gesture Recognition Using Several Fully Connected Layers with Path Signature Features and Temporal Transformer Module	Chenyang Li, Xin Zhang, Lufan Liao, Lianwen Jin, Weixin Yang	Considering the significance of fine hand movements in the gesture, we propose an ”attention on hand” (AOH) principle to define joint pairs for the S PS and select single joint for the T PS.
1054	Semantic Relationships Guided Representation Learning for Facial Action Unit Recognition	Guanbin Li, Xin Zhu, Yirui Zeng, Qing Wang, Liang Lin	In this paper, we investigate how to integrate the semantic relationship propagation between AUs in a deep neural network framework to enhance the feature representation of facial regions, and propose an AU semantic relationship embedded representation learning (SRERL) framework.
1055	Heterogeneous Transfer Learning via Deep Matrix Completion with Adversarial Kernel Embedding	Haoliang Li, Sinno Jialin Pan, Renjie Wan, Alex C. Kot	In this paper, we propose a new HTL method based on a deep matrix completion framework, where kernel embedding of distributions is trained in an adversarial manner for learning heterogeneous features across domains.
1056	Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition	Hui Li, Peng Wang, Chunhua Shen, Guyu Zhang	In this work, we propose an easy-to-implement strong baseline for irregular scene text recognition, using offthe-shelf neural network components and only word-level annotations.
1057	Multi-Scale 3D Convolution Network for Video Based Person Re-Identification	Jianing Li, Shiliang Zhang, Tiejun Huang	This paper proposes a two-stream convolution network to extract spatial and temporal cues for video based person ReIdentification (ReID).
1058	Meta Learning for Image Captioning	Nannan Li, Zhenzhong Chen, Shan Liu	In this work, we propose to use a new learning method, meta learning, to utilize supervision from the ground truth whilst optimizing the reward function in RL.
1059	Visual-Semantic Graph Reasoning for Pedestrian Attribute Recognition	Qiaozhe Li, Xin Zhao, Ran He, Kaiqi Huang	This paper treats pedestrian attribute recognition as a sequential attribute prediction problem and proposes a novel visual-semantic graph reasoning framework to address this problem.
1060	Distribution Consistency Based Covariance Metric Networks for Few-Shot Learning	Wenbin Li, Jinglin Xu, Jing Huo, Lei Wang, Yang Gao, Jiebo Luo	In this work, we propose a novel end-to-end deep architecture, named Covariance Metric Networks (CovaMNet).
1061	Learning Object Context for Dense Captioning	Xiangyang Li, Shuqiang Jiang, Jungong Han	In this work, we propose a novel scheme with an object context encoding Long Short-Term Memory (LSTM) network to automatically learn complementary object context for each caption region, transferring knowledge from objects to caption regions.
1062	Beyond RNNs: Positional Self-Attention with Co-Attention for Video Question Answering	Xiangpeng Li, Jingkuan Song, Lianli Gao, Xianglong Liu, Wenbing Huang, Xiangnan He, Chuang Gan	We propose a new architecture, Positional Self-Attention with Coattention (PSAC), which does not require RNNs for video question answering.
1063	Robust Estimation of Similarity Transformation for Visual Object Tracking	Yang Li, Jianke Zhu, Steven C.H. Hoi, Wenjie Song, Zhefeng Wang, Hantang Liu	To tackle this challenging problem, in this paper, we propose a new correlation filter-based tracker with a novel robust estimation of similarity transformation on the large displacements.
1064	Temporal Bilinear Networks for Video Action Recognition	Yanghao Li, Sijie Song, Yuqi Li, Jiaying Liu	In this paper, we propose a novel Temporal Bilinear (TB) model to capture the temporal pairwise feature interactions between adjacent frames.
1065	Angular Triplet-Center Loss for Multi-View 3D Shape Retrieval	Zhaoqun Li, Cheng Xu, Biao Leng	In this paper, we address this problem based on the intuition that the cosine distance of shape embeddings should be close enough within the same class and far away across categories.
1066	Zero-Shot Object Detection with Textual Descriptions	Zhihui Li, Lina Yao, Xiaoqin Zhang, Xianzhi Wang, Salil Kanhere, Huaxiang Zhang	In this paper, we address the challenging problem of zero-shot object detection with natural language description, which aims to simultaneously detect and recognize novel concept instances with textual descriptions.
1067	PCGAN: Partition-Controlled Human Image Generation	Dong Liang, Rui Wang, Xiaowei Tian, Cong Zou	In this paper, we propose a novel Partition-Controlled GAN to generate human images according to target pose and background.
1068	Unsupervised Cross-Spectral Stereo Matching by Learning to Synthesize	Mingyang Liang, Xiaoyang Guo, Hongsheng Li, Xiaogang Wang, You Song	We propose a novel unsupervised crossspectral stereo matching framework based on image-to-image translation.
1069	Scene Text Recognition from Two-Dimensional Perspective	Minghui Liao, Jian Zhang, Zhaoyi Wan, Fengming Xie, Jiajun Liang, Pengyuan Lyu, Cong Yao, Xiang Bai	In this paper, we approach scene text recognition from a two-dimensional perspective.
1070	Towards Optimal Discrete Online Hashing with Balanced Similarity	Mingbao Lin, Rongrong Ji, Hong Liu, Xiaoshuai Sun, Yongjian Wu, Yunsheng Wu	In this paper, we propose a novel supervised online hashing method, termed Balanced Similarity for Online Discrete Hashing (BSODH), to solve the above problems in a unified framework.
1071	Hypergraph Optimization for Multi-Structural Geometric Model Fitting	Shuyuan Lin, Guobao Xiao, Yan Yan, David Suter, Hanzi Wang	Recently, some hypergraph-based methods have been proposed to deal with the problem of model fitting in computer vision, mainly due to the superior capability of hypergraph to represent the complex relationship between data points.
1072	A Bottom-Up Clustering Approach to Unsupervised Person Re-Identification	Yutian Lin, Xuanyi Dong, Liang Zheng, Yan Yan, Yi Yang	To relieve this problem, we propose a bottom-up clustering (BUC) approach to jointly optimize a convolutional neural network (CNN) and the relationship among the individual samples.
1073	Learning Neural Bag-of-Matrix-Summarization with Riemannian Network	Hong Liu, Jie Li, Yongjian Wu, Rongrong Ji	In this paper, we propose a Bag-of-Matrix-Summarization (BoMS) method to be combined with Riemannian network, which handles the above issues towards highly efficient and scalable SPD feature representation.
1074	Optimal Projection Guided Transfer Hashing for Image Retrieval	Ji Liu, Lei Zhang	To handle such problems, inspired by transfer learning, we propose a simple yet effective unsupervised hashing method named Optimal Projection Guided Transfer Hashing (GTH) where we borrow the images of other different but related domain i.e., source domain to help learn precise hashing codes for the domain of interest i.e., target domain.
1075	Joint Dynamic Pose Image and Space Time Reversal for Human Action Recognition from Videos	Mengyuan Liu, Fanyang Meng, Chen Chen, Songtao Wu	To solve this problem, this paper takes advantage of pose estimation to enhance the performances of video frame features.
1076	DDFlow: Learning Optical Flow with Unlabeled Data Distillation	Pengpeng Liu, Irwin King, Michael R. Lyu, Jia Xu	We present DDFlow, a data distillation approach to learning optical flow estimation from unlabeled data.
1077	Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-Based Sequence to Sequence Network	Xinhai Liu, Zhizhong Han, Yu-Shen Liu, Matthias Zwicker	To resolve this issue, we propose a novel deep learning model for 3D point clouds, named Point2Sequence, to learn 3D shape features by capturing fine-grained contextual information in a novel implicit way.
1078	Spatial and Temporal Mutual Promotion for Video-Based Person Re-Identification	Yiheng Liu, Zhenxun Yuan, Wengang Zhou, Houqiang Li	To this end, we propose a Refining Recurrent Unit (RRU) that recovers the missing parts and suppresses noisy parts of the current frame’s features by referring historical frames.
1079	Deep Video Frame Interpolation Using Cyclic Frame Generation	Yu-Lun Liu, Yi-Tung Liao, Yen-Yu Lin, Yung-Yu Chuang	We propose that: synthesized frames are more reliable if they can be used to reconstruct the input frames with high quality.
1080	Detect or Track: Towards Cost-Effective Video Object Detection/Tracking	Hao Luo, Wenxuan Xie, Xinggang Wang, Wenjun Zeng	To this end, we propose a scheduler network, which determines to detect or track at a certain frame, as a generalization of Siamese trackers.
1081	Recognizing Unseen Attribute-Object Pair with Generative Model	Zhixiong Nan, Yang Liu, Nanning Zheng, Song-Chun Zhu	In this paper, we are studying the problem of recognizing attribute-object pairs that do not appear in the training dataset, which is called unseen attribute-object pair recognition.
1082	CAPNet: Continuous Approximation Projection for 3D Point Cloud Reconstruction Using 2D Supervision	K. L. Navaneet, Priyanka Mandikal, Mayank Agarwal, R. Venkatesh Babu	To overcome the challenges of sparse projection maps, we propose a loss formulation termed ‘affinity loss’ to generate outlierfree reconstructions.
1083	Dual Semi-Supervised Learning for Facial Action Unit Recognition	Guozhu Peng, Shangfei Wang	To reduce the reliance on time-consuming manual AU annotations, we propose a novel semi-supervised AU recognition method leveraging two kinds of readily available auxiliary information.
1084	Learning Attribute-Specific Representations for Visual Tracking	Yuankai Qi, Shengping Zhang, Weigang Zhang, Li Su, Qingming Huang, Ming-Hsuan Yang	In this paper, we show that attribute information (e.g., illumination changes, occlusion and motion) in the context facilitates training an effective classifier for visual tracking.
1085	Weakly Supervised Scene Parsing with Point-Based Distance Metric Learning	Rui Qian, Yunchao Wei, Honghui Shi, Jiachen Li, Jiaying Liu, Thomas Huang	To tackle this issue, we propose a Point-based Distance Metric Learning (PDML) in this paper.
1086	MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization	Zengyi Qin, Jinglu Wang, Yan Lu	Unlike the pixel-level depth estimation that needs per-pixel annotations, we propose a novel IDE method that directly predicts the depth of the targeting 3D bounding box’s center using sparse supervision.
1087	Backbone Cannot Be Trained at Once: Rolling Back to Pre-Trained Network for Person Re-Identification	Youngmin Ro, Jongwon Choi, Dae Ung Jo, Byeongho Heo, Jongin Lim, Jin Young Choi	In this work, we propose a novel fine-tuning strategy that allows low-level layers to be sufficiently trained by rolling back the weights of high-level layers to their initial pre-trained weights.
1088	Almost Unsupervised Learning for Dense Crowd Counting	Deepak Babu Sam, Neeraj N Sajjan, Himanshu Maurya, R. Venkatesh Babu	We present an unsupervised learning method for dense crowd count estimation.
1089	KVQA: Knowledge-Aware Visual Question Answering	Sanket Shah, Anand Mishra, Naganand Yadati, Partha Pratim Talukdar	We address this gap in this paper, and introduce KVQA – the first dataset for the task of (world) knowledge-aware VQA.
1090	Connecting Language to Images: A Progressive Attention-Guided Network for Simultaneous Image Captioning and Language Grounding	Lingyun Song, Jun Liu, Buyue Qian, Yihe Chen	In this paper, we propose a Progressive Attention-Guided Network (PAGNet), which simultaneously generates image captions and predicts bounding boxes for caption words.
1091	Towards Highly Accurate and Stable Face Alignment for High-Resolution Videos	Ying Tai, Yicong Liang, Xiaoming Liu, Lei Duan, Jilin Li, Chengjie Wang, Feiyue Huang, Yu Chen	In this paper, we propose a Fractional Heatmap Regression (FHR) for high-resolution video-based face alignment.
1092	A Layer-Based Sequential Framework for Scene Generation with GANs	Mehmet Ozgur Turkoglu, William Thong, Luuk Spreeuwers, Berkay Kicanaoglu	In this work, we present a scene generation framework based on Generative Adversarial Networks (GANs) to sequentially compose a scene, breaking down the underlying problem into smaller ones.
1093	Hierarchical Photo-Scene Encoder for Album Storytelling	Bairui Wang, Lin Ma, Wei Zhang, Wenhao Jiang, Feng Zhang	In this paper, we propose a novel model with a hierarchical photo-scene encoder and a reconstructor for the task of album storytelling.
1094	Robust Deep Co-Saliency Detection with Group Semantic	Chong Wang, Zheng-Jun Zha, Dong Liu, Hongtao Xie	This paper proposes a novel end-to-end deep learning approach for robust co-saliency detection by simultaneously learning highlevel group-wise semantic representation as well as deep visual features of a given image group.
1095	Learning Basis Representation to Refine 3D Human Pose Estimations	Chunyu Wang, Haibo Qiu, Alan L. Yuille, Wenjun Zeng	In this work, we present an approach to refine inaccurate 3D pose estimations.
1096	Spatial-Temporal Person Re-Identification	Guangcong Wang, Jianhuang Lai, Peigen Huang, Xiaohua Xie	In this paper, we propose a novel two-stream spatial-temporal person ReID (st-ReID) framework that mines both visual semantic information and spatial-temporal information.
1097	Deep Single-View 3D Object Reconstruction with Visual Hull Embedding	Hanqing Wang, Jiaolong Yang, Wei Liang, Xin Tong	In this paper, we present an approach which aims to preserve more shape details and improve the reconstruction quality.
1098	MVPNet: Multi-View Point Regression Networks for 3D Object Reconstruction from A Single Image	Jinglu Wang, Bo Sun, Yan Lu	In this paper, we address the problem of reconstructing an object’s surface from a single image using generative networks.
1099	Hierarchical Attention Network for Image Captioning	Weixuan Wang, Zhihong Chen, Haifeng Hu	In this paper, we propose a Hierarchical Attention Network (HAN) that enables attention to be calculated on pyramidal hierarchy of features synchronously.
1100	Learning to Compose Topic-Aware Mixture of Experts for Zero-Shot Video Captioning	Xin Wang, Jiawei Wu, Da Zhang, Yu Su, William Yang Wang	Here we introduce a novel task, zeroshot video captioning, that aims at describing out-of-domain videos of unseen activities.
1101	Sparse Adversarial Perturbations for Videos	Xingxing Wei, Jun Zhu, Sha Yuan, Hang Su	To this end, we propose the first white-box video attack method, which utilizes an l2,1-norm based optimization algorithm to compute the sparse adversarial perturbations for videos.
1102	Learning Non-Uniform Hypergraph for Multi-Object Tracking	Longyin Wen, Dawei Du, Shengkun Li, Xiao Bian, Siwei Lyu	In this work, we present a new near-online MOT algorithm based on non-uniform hypergraph, which can model different degrees of dependencies among tracklets in a unified objective.
1103	Graph CNNs with Motif and Variable Temporal Block for Skeleton-Based Action Recognition	Yu-Hui Wen, Lin Gao, Hongbo Fu, Fang-Lue Zhang, Shihong Xia	In this work, we propose a novel model with motif-based graph convolution to encode hierarchical spatial structure, and a variable temporal dense block to exploit local temporal information over different ranges of human skeleton sequences.
1104	Differential Networks for Visual Question Answering	Chenfei Wu, Jinlai Liu, Xiaojie Wang, Ruifan Li	We argue that two differences between those two feature elements themselves, like (vi − vj) and (qi −qj), are more probably in the same space.
1105	Disentangled Variational Representation for Heterogeneous Face Recognition	Xiang Wu, Huaibo Huang, Vishal M. Patel, Ran He, Zhenan Sun	In this paper, we take a different approach in which we make use of the Disentangled Variational Representation (DVR) for crossmodal matching.
1106	Multiple Saliency and Channel Sensitivity Network for Aggregated Convolutional Feature	Xuanlu Xiang, Zhipeng Wang, Zhicheng Zhao, Fei Su	In this paper, aiming at two key problems of instance-level image retrieval, i.e., the distinctiveness of image representation and the generalization ability of the model, we propose a novel deep architecture – Multiple Saliency and Channel Sensitivity Network(MSCNet).
1107	What and Where the Themes Dominate in Image	Xinyu Xiao, Lingfeng Wang, Shiming Xiang, Chunhong Pan	Inspired by this observation, this paper proposes a novel framework, which explicitly applies the known salient objects in image captioning.
1108	Semantic Adversarial Network with Multi-Scale Pyramid Attention for Video Classification	De Xie, Cheng Deng, Hao Wang, Chao Li, Dapeng Tao	In this paper, we proposed a new two-stream based deep framework for video classification to discover spatial and temporal information only from RGB frames, moreover, the multi-scale pyramid attention (MPA) layer and the semantic adversarial learning (SAL) module is introduced and integrated in our framework.
1109	Scene Text Detection with Supervised Pyramid Context Network	Enze Xie, Yuhang Zang, Shuai Shao, Gang Yu, Cong Yao, Guangyao Li	To tackle this issue, mainly inspired by Mask R-CNN, we propose in this paper an effective model for scene text detection, which is based on Feature Pyramid Network (FPN) and instance segmentation.
1110	DeRPN: Taking a Further Step toward More General Object Detection	Lele Xie, Yuliang Liu, Lianwen Jin, Zecheng Xie	To improve the adaptivity of the detectors, in this paper, we present a novel dimension-decomposition region proposal network (DeRPN) that can perfectly displace the traditional Region Proposal Network (RPN).
1111	Residual Attribute Attention Network for Face Image Super-Resolution	Jingwei Xin, Nannan Wang, Xinbo Gao, Jie Li	In this paper, we present a novel deep end-to-end network for face super resolution, named Residual Attribute Attention Network (RAAN), which realizes the efficient feature fusion of various types of facial information.
1112	Multilevel Language and Vision Integration for Text-to-Clip Retrieval	Huijuan Xu, Kun He, Bryan A. Plummer, Leonid Sigal, Stan Sclaroff, Kate Saenko	To capture the inherent structures present in both text and video, we introduce a multilevel model that integrates vision and language features earlier and more tightly than prior work.
1113	Segregated Temporal Assembly Recurrent Networks for Weakly Supervised Multiple Action Detection	Yunlu Xu, Chengwei Zhang, Zhanzhan Cheng, Jianwen Xie, Yi Niu, Shiliang Pu, Fei Wu	This paper proposes a segregated temporal assembly recurrent (STAR) network for weakly-supervised multiple action detection.
1114	A Dual Attention Network with Semantic Embedding for Few-Shot Learning	Shipeng Yan, Songyang Zhang, Xuming He	We propose a novel meta-learning method for few-shot classification based on two simple attention mechanisms: one is a spatial attention to localize relevant object regions and the other is a task attention to select similar training data for label prediction.
1115	Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing	Fan Yang, Ryota Hinami, Yusuke Matsui, Steven Ly, Shin’ichi Satoh	To overcome this weakness, we propose a novel diffusion technique in this paper.
1116	Learning a Visual Tracker from a Single Movie without Annotation	Lingxiao Yang, David Zhang, Lei Zhang	To address this problem, we propose a novel unsupervised learning pipeline which is based on the discriminative correlation filter network.
1117	Safeguarded Dynamic Label Regression for Noisy Supervision	Jiangchao Yao, Hao Wu, Ya Zhang, Ivor W. Tsang, Jun Sun	To overcome this issue, we propose a Latent Class-Conditional Noise model (LCCN) that models the noise transition in a Bayesian form.
1118	Instance-Level Facial Attributes Transfer with Geometry-Aware Flow	Weidong Yin, Ziwei Liu, Chen Change Loy	We propose the use of geometry-aware flow, which serves as a wellsuited representation for modeling the transformation between instance-level facial attributes.
1119	PVRNet: Point-View Relation Neural Network for 3D Shape Recognition	Haoxuan You, Yifan Feng, Xibin Zhao, Changqing Zou, Rongrong Ji, Yue Gao	In this paper, we introduce Point-View Relation Network (PVRNet), an effective network designed to well fuse the view features and the point cloud feature with a proposed relation score module.
1120	ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering	Zhou Yu, Dejing Xu, Jun Yu, Ting Yu, Zhou Zhao, Yueting Zhuang, Dacheng Tao	Here we introduce ActivityNet-QA, a fully annotated and large scale VideoQA dataset.
1121	Spatial Mixture Models with Learnable Deep Priors for Perceptual Grouping	Jinyang Yuan, Bin Li, Xiangyang Xue	In this work, we propose a new type of spatial mixture models with learnable priors for perceptual grouping.
1122	Cycle-SUM: Cycle-Consistent Adversarial LSTM Networks for Unsupervised Video Summarization	Li Yuan, Francis EH Tay, Ping Li, Li Zhou, Jiashi Feng	In this paper, we present a novel unsupervised video summarization model that requires no manual annotation.
1123	Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion	Longhao Yuan, Chao Li, Danilo Mandic, Jianting Cao, Qibin Zhao	In this paper, by exploiting the low-rank structure of the TR latent space, we propose a novel tensor completion method which is robust to model selection.
1124	To Find Where You Talk: Temporal Sentence Localization in Video with Attention Based Location Regression	Yitian Yuan, Tao Mei, Wenwu Zhu	To address these issues, we propose a novel Attention Based Location Regression (ABLR) approach to localize sentence descriptions in videos in an efficient end-to-end manner.
1125	Memory-Augmented Temporal Dynamic Learning for Action Recognition	Yuan Yuan, Dong Wang, Qi Wang	In this work, we propose a memory-augmented temporal dynamic learning network, which learns to write the most evident information into an external memory module and ignore irrelevant ones.
1126	ACM: Adaptive Cross-Modal Graph Convolutional Neural Networks for RGB-D Scene Recognition	Yuan Yuan, Zhitong Xiong, Qi Wang	Considering these limitations, this paper proposes an adaptive crossmodal (ACM) feature learning framework based on graph convolutional neural networks for RGB-D scene recognition.
1127	Large-Scale Visual Relationship Understanding	Ji Zhang, Yannis Kalantidis, Marcus Rohrbach, Manohar Paluri, Ahmed Elgammal, Mohamed Elhoseiny	We develop a new relationship detection model that embeds objects and relations into two vector spaces where both discriminative capability and semantic affinity are preserved.
1128	Multi-Attribute Transfer via Disentangled Representation	Jianfu Zhang, Yuanyuan Huang, Yaoyi Li, Weijie Zhao, Liqing Zhang	In this paper, we propose a novel model formulating disentangled representations by projecting images to latent units, grouped feature channels of Convolutional Neural Network, to disassemble the information between different attributes.
1129	Cousin Network Guided Sketch Recognition via Latent Attribute Warehouse	Kaihao Zhang, Wenhan Luo, Lin Ma, Hongdong Li	In order to overcome these challenges, in this paper we propose to transfer the knowledge of a network learned from natural images to a sketch network – a new deep net architecture which we term as cousin network.
1130	Understanding Pictograph with Facial Features: End-to-End Sentence-Level Lip Reading of Chinese	Xiaobing Zhang, Haigang Gong, Xili Dai, Fan Yang, Nianbo Liu, Ming Liu	In this paper, we implement visual-only Chinese lip reading of unconstrained sentences in a two-step end-to-end architecture (LipCH-Net), in which two deep neural network models are employed to perform the recognition of Pictureto-Pinyin (mouth motion pictures to pronunciations) and the recognition of Pinyin-to-Hanzi (pronunciations to texts) respectively, before having a jointly optimization to improve the overall performance.
1131	Learning to Localize Objects with Noisy Labeled Instances	Xiaopeng Zhang, Yang Yang, Jiashi Feng	We model the missing object locations as latent variables, and contribute a novel self-directed optimization strategy to infer them.
1132	Learning Transferable Self-Attentive Representations for Action Recognition in Untrimmed Videos with Weak Supervision	Xiao-Yu Zhang, Haichao Shi, Changsheng Li, Kai Zheng, Xiaobin Zhu, Lixin Duan	In this paper, given only video-level annotations, we propose a novel weakly supervised framework to simultaneously locate action frames as well as recognize actions in untrimmed videos.
1133	Learning a Key-Value Memory Co-Attention Matching Network for Person Re-Identification	Yaqing Zhang, Xi Li, Zhongfei Zhang	Motivated by this observation, we propose a Key-Value Memory Matching Network (KVM-MN) model that consists of key-value memory representation and key-value co-attention matching.
1134	Learning Incremental Triplet Margin for Person Re-Identification	Yingying Zhang, Qiaoyong Zhong, Liang Ma, Di Xie, Shiliang Pu	In this work, we explore the margin between positive and negative pairs of triplets and prove that large margin is beneficial.
1135	Look across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition	Jian Zhao, Yu Cheng, Yi Cheng, Yang Yang, Fang Zhao, Jianshu Li, Hengzhu Liu, Shuicheng Yan, Jiashi Feng	To this end, we propose a deep Age-Invariant Model (AIM) for face recognition in the wild with three distinct novelties.
1136	M2Det: A Single-Shot Object Detector Based on Multi-Level Feature Pyramid Network	Qijie Zhao, Tao Sheng, Yongtao Wang, Zhi Tang, Ying Chen, Ling Cai, Haibin Ling	Newly, in this work, we present Multi-Level Feature Pyramid Network (MLFPN) to construct more effective feature pyramids for detecting objects of different scales.
1137	3D Object Detection Using Scale Invariant and Feature Reweighting Networks	Xin Zhao, Zhe Liu, Ruolan Hu, Kaiqi Huang	In this paper, we present a new network architecture which focuses on utilizing the front view images and frustum point clouds to generate 3D detection results.
1138	Recurrent Attention Model for Pedestrian Attribute Recognition	Xin Zhao, Liufang Sang, Guiguang Ding, Jungong Han, Na Di, Chenggang Yan	Inspired by Recurrent Neural Network (RNN)’s super capability of learning context correlations and Attention Model’s capability of highlighting the region of interest on feature map, this paper proposes end-to-end Recurrent Convolutional (RC) and Recurrent Attention (RA) models, which are complementary to each other.
1139	Learning Fully Dense Neural Networks for Image Semantic Segmentation	Mingmin Zhen, Jinglu Wang, Lei Zhou, Tian Fang, Long Quan	The “feature map reuse” has been commonly adopted in CNN based approaches to take advantage of feature maps in the early layers for the later spatial reconstruction.
1140	Towards Optimal Fine Grained Retrieval via Decorrelated Centralized Loss with Normalize-Scale Layer	Xiawu Zheng, Rongrong Ji, Xiaoshuai Sun, Baochang Zhang, Yongjian Wu, Feiyue Huang	In this paper, we propose a novel metric learning scheme, termed Normalize-Scale Layer and Decorrelated Global Centralized Ranking Loss, which achieves extremely efficient and discriminative learning, i.e., 5× speedup over triplet loss and 12% recall boost on CARS196.
1141	Talking Face Generation by Adversarially Disentangled Audio-Visual Representation	Hang Zhou, Yu Liu, Ziwei Liu, Ping Luo, Xiaogang Wang	In this work, we integrate both aspects and enable arbitrary-subject talking face generation by learning disentangled audio-visual representation.
1142	A Robust and Efficient Algorithm for the PnL Problem Using Algebraic Distance to Approximate the Reprojection Distance	Lipu Zhou, Yi Yang, Montiel Abello, Michael Kaess	This paper proposes a novel algorithm to solve the pose estimation problem from 2D/3D line correspondences, known as the Perspective-n-Line (PnL) problem.
1143	Free VQA Models from Knowledge Inertia by Pairwise Inconformity Learning	Yiyi Zhou, Rongrong Ji, Jinsong Su, Xiangming Li, Xiaoshuai Sun	In this paper, we uncover the issue of knowledge inertia in visual question answering (VQA), which commonly exists in most VQA models and forces the models to mainly rely on the question content to “guess” answer, without regard to the visual information.
1144	Dynamic Capsule Attention for Visual Question Answering	Yiyi Zhou, Rongrong Ji, Jinsong Su, Xiaoshuai Sun, Weiqiu Chen	In this paper, we propose an extremely compact alternative to this static multi-layer architecture towards accurate yet efficient attention modeling, termed as Dynamic Capsule Attention (CapsAtt).
1145	Singe Image Rain Removal with Unpaired Information: A Differentiable Programming Perspective	Hongyuan Zhu, Xi Peng, Joey Tianyi Zhou, Songfan Yang, Vijay Chanderasekh, Liyuan Li, Joo-Hwee Lim	To overcome these limitations, in this work, we propose RainRemoval-GAN (RRGAN), the first end-to-end adversarial model that generates realistic rain-free images using only unpaired supervision.
1146	Deep Embedding Features for Salient Object Detection	Yunzhi Zhuge, Yu Zeng, Huchuan Lu	In this paper, we propose a novel approach that transforms prior information into an embedding space to select attentive features and filter out outliers for salient object detection.
1147	Calibrated Stochastic Gradient Descent for Convolutional Neural Networks	Li’an Zhuo, Baochang Zhang, Chen Chen, Qixiang Ye, Jianzhuang Liu, David Doermann	This paper introduces a calibrated stochastic gradient descent (CSGD) algorithm for deep neural network optimization.