Paper Digest: AAAI 2018 Highlights

February 1, 2018October 8, 2019 admin

The AAAI Conference on Artificial Intelligence (AAAI) is one of the top artificial intelligence conferences in the world. In 2018, it is to be held in New Orleans, Louisiana.

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 2018 Papers

	Title	Authors	Highlight
1	Algorithms for Trip-Vehicle Assignment in Ride-Sharing	Xiaohui Bei, Shengyu Zhang	We investigate the ride-sharing assignment problem from an algorithmic resource allocation point of view.
2	EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples	Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh	In this paper, we formulate the process of attacking DNNs via adversarial examples as an elastic-net regularized optimization problem.
3	Learning Differences Between Visual Scanning Patterns Can Disambiguate Bipolar and Unipolar Patients	Jonathan Chung, Moshe Eizenman, Uros Rakita, Roger McIntyre, Peter Giacobbe	In this paper, we present novel methods to differentiate between BD and MDD patients.
4	Comparing Population Means Under Local Differential Privacy: With Significance and Power	Bolin Ding, Harsha Nori, Paul Li, Joshua Allen	In this paper, we study how to conduct hypothesis tests to compare population means while preserving privacy.
5	MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment	Hao-Wen Dong, Wen-Yi Hsiao, Li-Chia Yang, Yi-Hsuan Yang	In this paper, we propose three models for symbolic multi-track music generation under the framework of generative adversarial networks (GANs).
6	Picasso, Matisse, or a Fake? Automated Analysis of Drawings at the Stroke Level for Attribution and Authentication	Ahmed Elgammal, Yan Kang, Milko Den Leeuw	This paper proposes a computational approach for analysis of strokes in line drawings by artists.
7	Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning	Nina Grgić-Hlača, Muhammad Bilal Zafar, Krishna P. Gummadi, Adrian Weller	In this work, we leverage the rich literature on organizational justice and focus on another dimension of fair decision making: procedural fairness, i.e., the fairness of the decision making process.
8	Distributed Composite Quantization	Weixiang Hong, Jingjing Meng, Junsong Yuan	Built upon the Composite Quantization, we propose a novel quantization algorithm for data dis- tributed across different nodes of an arbitrary network.
9	Tensorized Projection for High-Dimensional Binary Embedding	Weixiang Hong, Jingjing Meng, Junsong Yuan	To tackle these problems, we propose Tensorized Projection (TP) to decompose the projection matrix using Tensor-Train (TT) format, which is a chain-like representation that allows to operate tensor in an efficient manner.
10	Predicting Aesthetic Score Distribution Through Cumulative Jensen-Shannon Divergence	Xin Jin, Le Wu, Xiaodong Li, Siyu Chen, Siwei Peng, Jingying Chi, Shiming Ge, Chenggen Song, Geng Zhao	In this work, we propose to predict the aesthetic score distribution (i.e., a score distribution vector of the ordinal basic human ratings) using Deep Convolutional Neural Network (DCNN).
11	Norm Conflict Resolution in Stochastic Domains	Daniel Kasenberg, Matthias Scheutz	We propose a hybrid approach, using Linear Temporal Logic (LTL) representations in Markov Decision Processes (MDPs), that manages norm conflicts in a systematic manner while accommodating domain stochasticity.
12	Deep Representation-Decoupling Neural Networks for Monaural Music Mixture Separation	Zhuo Li, Hongwei Wang, Miao Zhao, Wenjie Li, Minyi Guo	In this paper, we study the problem of separating vocals and instruments from monaural music mixture.
13	Early Prediction of Diabetes Complications from Electronic Health Records: A Multi-Task Survival Analysis Approach	Bin Liu, Ying Li, Zhaonan Sun, Soumya Ghosh, Kenney Ng	In this paper, we present a new data-driven predictive approach to predict when a patient will develop complications after the initial T2DM diagnosis.
14	Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction	Luchen Liu, Jianhao Shen, Ming Zhang, Zichang Wang, Jian Tang	In this paper, we propose a novel model for learning the joint representation of heterogeneous temporal events.
15	Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis	Ye Liu, Lifang He, Bokai Cao, Philip S. Yu, Ann B. Ragin, Alex D. Leow	As a novel attempt to tackle this problem, we propose Multi-view Multi-graph Embedding M2E by stacking multi-graphs into multiple partially-symmetric tensors and using tensor techniques to simultaneously leverage the dependencies and correlations among multi-view and multi-graph brain networks.
16	Uplink Communication Efficient Differentially Private Sparse Optimization With Feature-Wise Distributed Data	Jian Lou, Yiu-ming Cheung	To obtain these guarantees, we provide a much generalized convergence analysis for block-coordinate Frank-Wolfe under arbitrary sampling, which greatly extends known convergence results that are only applicable to two specific block sampling distributions.
17	Learning the Probability of Activation in the Presence of Latent Spreaders	Maggie Makar, John Guttag, Jenna Wiens	We present a generative probabilistic model and a variational inference method to learn the parameters of our model.
18	Generating an Event Timeline About Daily Activities From a Semantic Concept Stream	Taiki Miyanishi, Jun-ichiro Hirayama, Takuya Maekawa, Motoaki Kawanabe	In this paper, we introduce a novel framework that produces an event timeline of ADLs in a home environment.
19	On Organizing Online Soirees with Live Multi-Streaming	Chih-Ya Shen, C. P. Kankeu Fotsing, De-Nian Yang, Yi-Shin Chen, Wang-Chien Lee	In this paper, therefore, we formulate a new Social-aware Diverse and Preferred Live Streaming Channel Query (SDSQ) that jointly selects a set of diverse and preferred live streaming channels and a group of socially tight viewers.
20	An AI Planning Solution to Scenario Generation for Enterprise Risk Management	Shirin Sohrabi, Anton V. Riabov, Michael Katz, Octavian Udrea	To this end, we provide a characterization of the problem, knowledge engineering methodology, and transformation to planning.
21	Automated Segmentation of Overlapping Cytoplasm in Cervical Smear Images via Contour Fragments	Youyi Song, Jing Qin, Baiying Lei, Kup-Sze Choi	We present a novel method for automated segmentation of overlapping cytoplasm in cervical smear images based on contour fragments.
22	When Social Advertising Meets Viral Marketing: Sequencing Social Advertisements for Influence Maximization	Shaojie Tang	This motivates us to study the optimal social advertising problem from platform’s perspective, and our objective is to find the best ad sequence for each user in order to maximize the expected revenue.
23	Synthesis of Programs from Multimodal Datasets	Shantanu Thakoor, Simoni Shah, Ganesh Ramakrishnan, Amitabha Sanyal	We describe MultiSynth, a framework for synthesizing domain-specific programs from a multimodal dataset of examples.
24	CD-CNN: A Partially Supervised Cross-Domain Deep Learning Model for Urban Resident Recognition	Jingyuan Wang, Xu He, Ze Wang, Junjie Wu, Nicholas Jing Yuan, Xing Xie, Zhang Xiong	In this paper, a partially supervised cross-domain deep learning model named CD-CNN is proposed for migrant/native recognition using mobile phone signaling data as behavioral features and questionnaire survey data as incomplete labels.
25	Geographic Differential Privacy for Mobile Crowd Coverage Maximization	Leye Wang, Gehua Qin, Dingqi Yang, Xiao Han, Xiaojuan Ma	In this paper, we propose a method to maximize mobile crowd’s future location coverage under a guaranteed location privacy protection scheme.
26	Catching Captain Jack: Efficient Time and Space Dependent Patrols to Combat Oil-Siphoning in International Waters	Xinrun Wang, Bo An, Martin Strobel, Fookwai Kong	In this paper, we address the research challenges and provide four key contributions.
27	Video Summarization via Semantic Attended Networks	Huawei Wei, Bingbing Ni, Yichao Yan, Huanyu Yu, Xiaokang Yang, Chen Yao	To explicitly address this issue, we propose a novel technique which is able to extract the most semantically relevant video segments (i.e., valid for a long term temporal duration) and assemble them into an informative summary.
28	TIMERS: Error-Bounded SVD Restart on Dynamic Networks	Ziwei Zhang, Peng Cui, Jian Pei, Xiao Wang, Wenwu Zhu	In this paper, we propose TIMERS, Theoretically Instructed Maximum-Error-bounded Restart of SVD, a novel approach which optimally sets the restart time in order to reduce error accumulation in time.
29	Ranking Users in Social Networks With Higher-Order Structures	Huan Zhao, Xiaogang Xu, Yangqiu Song, Dik Lun Lee, Zhao Chen, Han Gao	In this paper, we propose a novel framework, motif-based PageRank (MPR), to incorporate higher-order structures into conventional PageRank computation.
30	Mitigating Overexposure in Viral Marketing	Rediet Abebe, Lada A. Adamic, Jon Kleinberg	In this paper, we develop and analyze a theoretical model for this process; we show how it captures a number of the qualitative phenomena associated with overexposure, and for the main formulation of our model, we provide a polynomial-time algorithm to find the optimal marketing strategy.
31	Neural Link Prediction over Aligned Networks	Xuezhi Cao, Haokun Chen, Xuejian Wang, Weinan Zhang, Yong Yu	Hence, in this paper we target atlink prediction over aligned networks using neural networks.
32	Privacy Preserving Point-of-Interest Recommendation Using Decentralized Matrix Factorization	Chaochao Chen, Ziqi Liu, Peilin Zhao, Jun Zhou, Xiaolong Li	To solve these, we present a Decentralized MF (DMF) framework for POI recommendation.
33	CA-RNN: Using Context-Aligned Recurrent Neural Networks for Modeling Sentence Similarity	Qin Chen, Qinmin Hu, Jimmy Xiangji Huang, Liang He	In this paper, we propose a context-aligned RNN (CA-RNN) model, which incorporates the contextual information of the aligned words in a sentence pair for the inner hidden state generation.
34	Dual Deep Neural Networks Cross-Modal Hashing	Zhen-Duo Chen, Wan-Jin Yu, Chuan-Xiang Li, Liqiang Nie, Xin-Shun Xu	In this paper, we propose a novel tri-stage deep cross-modal hashing method – Dual Deep Neural Networks Cross-Modal Hashing, i.e., DDCMH, which employs two deep networks to generate hash codes for different modalities.
35	Representation Learning for Scale-Free Networks	Rui Feng, Yang Yang, Wenjie Hu, Fei Wu, Yueting Zhang	In this paper, we study the problem of learning representations for scale-free networks.
36	VSE-ens: Visual-Semantic Embeddings with Efficient Negative Sampling	Guibing Guo, Songlin Zhai, Fajie Yuan, Yuan Liu, Xingwei Wang	In this paper, we propose a fast adaptive negative sampler that can work well in the settings of no figure pixels available.
37	Partial Multi-View Outlier Detection Based on Collective Learning	Jun Guo, Wenwu Zhu	To address this problem, we propose a novel Collective Learning (CL) based framework to detect outliers from partial multi-view data in a self-guided way.
38	A Network-Specific Markov Random Field Approach to Community Detection	Dongxiao He, Xinxin You, Zhiyong Feng, Di Jin, Xue Yang, Weixiong Zhang	Here we present a network-specific MRF approach to community detection.
39	Robust Detection of Link Communities in Large Social Networks by Exploiting Link Semantics	Di Jin, Xiaobao Wang, Ruifang He, Dongxiao He, Jianwu Dang, Weixiong Zhang	To address these issues altogether, we propose a new unified probabilistic model that can be learned by a dual nested expectation-maximization algorithm.
40	On Validation and Predictability of Digital Badges’ Influence on Individual Users	Tomasz Kuśmierczyk, Kjetil Nørvåg	In this paper, we introduce two complementary approaches for determining badge influence on users.
41	FILE: A Novel Framework for Predicting Social Status in Signed Networks	Xiaoming Li, Hui Fang, Jie Zhang	In this paper, we propose a novel Framework of Integrating both Latent and Explicit features (FILE), to better deal with the no-relation status and improve the overall link prediction performance in signed networks.
42	Community Detection in Attributed Graphs: An Embedding Approach	Ye Li, Chaofeng Sha, Xin Huang, Yanchun Zhang	In this paper, we propose a novel embedding based model to discover communities in attributed graphs.
43	Social Recommendation with an Essential Preference Space	Chun-Yi Liu, Chuan Zhou, Jia Wu, Yue Hu, Li Guo	In this paper, we investigate how to exploit the differences between user preference in recommender systems and that in social networks, with the aim to further improve the social recommendation.
44	Early Detection of Fake News on Social Media Through Propagation Path Classification with Recurrent and Convolutional Networks	Yang Liu, Yi-Fang Brook Wu	To address this limitation, in this paper, we propose a novel model for early detection of fake news on social media through classifying news propagation paths.
45	Cross-Lingual Entity Linking for Web Tables	Xusheng Luo, Kangqi Luo, Xianyang Chen, Kenny Q. Zhu	We present a joint statistical model to simultaneously link all mentions that appear in one table.
46	DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks	Jianxin Ma, Peng Cui, Wenwu Zhu	To overcome these challenges, we propose a Deeply Transformed High-order Laplacian Gaussian Process (DepthLGP) method to infer embeddings for out-of-sample nodes.
47	Listening to the World Improves Speech Command Recognition	Brian McMahan, Delip Rao	We study transfer learning in convolutional network architectures applied to the task of recognizing audio, such as environmental sound events and speech commands.
48	Location-Sensitive User Profiling Using Crowdsourced Labels	Wei Niu, James Caverlee, Haokai Lu	In this paper, we investigate the impact of spatial variation on the construction of location-sensitive user profiles.
49	Binary Generative Adversarial Networks for Image Retrieval	Jingkuan Song, Tao He, Lianli Gao, Xing Xu, Alan Hanjalic, Heng Tao Shen	In this paper, we use binary generative adversarial networks (BGAN) to embed images to binary codes in an unsupervised way.
50	Deep Region Hashing for Generic Instance Search from Images	Jingkuan Song, Tao He, Lianli Gao, Xing Xu, Heng Tao Shen	To tackle these issues, in this paper we propose an effective and efficient Deep Region Hashing (DRH) approach for large-scale INS using an image patch as the query.
51	Improved English to Russian Translation by Neural Suffix Prediction	Kai Song, Yue Zhang, Min Zhang, Weihua Luo	We propose a novel method, which can not only reduce data sparsity but also model morphology through a simple but effective mechanism.
52	Towards Efficient Detection of Overlapping Communities in Massive Networks	Bing-Jie Sun, Huawei Shen, Jinhua Gao, Wentao Ouyang, Xueqi Cheng	In this paper, we aim to offer an off-the-shelf method to detect overlapping communities in massive real world networks.
53	Structural Deep Embedding for Hyper-Networks	Ke Tu, Peng Cui, Xiao Wang, Fei Wang, Wenwu Zhu	In this paper, we propose a novel Deep Hyper-Network Embedding (DHNE) model to embed hyper-networks with indecomposable hyperedges.
54	Confidence-Aware Matrix Factorization for Recommender Systems	Chao Wang, Qi Liu, Runze Wu, Enhong Chen, Chuanren Liu, Xunpeng Huang, Zhenya Huang	In this paper, we propose a Confidence-aware Matrix Factorization (CMF) framework to simultaneously optimize the accuracy of rating prediction and measure the prediction confidence in the model.
55	Deep Asymmetric Transfer Network for Unbalanced Domain Adaptation	Daixin Wang, Peng Cui, Wenwu Zhu	In this paper, we propose a novel Deep Asymmetric Transfer Network (DATN) to address the problem of unbalanced domain adaptation.
56	A Multi-Task Learning Approach for Improving Product Title Compression with User Search Log Data	Jingang Wang, Junfeng Tian, Long Qiu, Sheng Li, Jun Lang, Luo Si, Man Lan	This paper proposes a novel multi-task learning approach for improving product title compression with user search log data.
57	Personalized Time-Aware Tag Recommendation	Keqiang Wang, Yuanyuan Jin, Haofen Wang, Hongwei Peng, Xiaoling Wang	In this paper, we propose an unified tag recommendation approach by considering both time awareness and personalization aspects, which extends PITF by adding weightsto user-tag interaction and item-tag interaction respectively.
58	Telepath: Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems	Yu Wang, Jixing Xu, Aohan Wu, Mantian Li, Yang He, Jinghe Hu, Weipeng P. Yan	This paper proposes Telepath, a vision-based bionic recommender system model, which simulates human brain activities in decision making of shopping, thus understanding users from such perspective.
59	RSDNE: Exploring Relaxed Similarity and Dissimilarity from Completely-Imbalanced Labels for Network Embedding	Zheng Wang, Xiaojun Ye, Chaokun Wang, Yuexin Wu, Changping Wang, Kaiwen Liang	To alleviate this, we propose a novel semi-supervised network embedding method, termed Relaxed Similarity and Dissimilarity Network Embedding (RSDNE).
60	Contrastive Training for Models of Information Cascades	Shaobin Xu, David A. Smith	This paper proposes a model of information cascades as directed spanning trees (DSTs) over observed documents.
61	Retrieving and Classifying Affective Images via Deep Metric Learning	Jufeng Yang, Dongyu She, Yu-Kun Lai, Ming-Hsuan Yang	In this work, we address the problem of understanding affective images via deep metric learning and propose a multi-task deep framework to optimize both retrieval and classification goals.
62	Multi-Facet Network Embedding: Beyond the General Solution of Detection and Representation	Liang Yang, Yuanfang Guo, Xiaochun Cao	To improve the general solution for better modeling the real network, we propose a novel network embedding method, Multi-facet Network Embedding (MNE), to capture the multiple facets of the network.
63	Urban Dreams of Migrants: A Case Study of Migrant Integration in Shanghai	Yang Yang, Chenhao Tan, Zongtao Liu, Fei Wu, Yueting Zhuang	To investigate the process of migrant integration, we employ a one-month complete dataset of telecommunication metadata in Shanghai with 54 million users and 698 million call logs.
64	From Common to Special: When Multi-Attribute Learning Meets Personalized Opinions	Zhiyong Yang, Qianqian Xu, Xiaochun Cao, Qingming Huang	To overcome this challenge, we propose a novel model to learn user-specific predictors across multiple attributes.
65	Discovering and Distinguishing Multiple Visual Senses for Polysemous Words	Yazhou Yao, Jian Zhang, Fumin Shen, Wankou Yang, Pu Huang, Zhenmin Tang	To solve this problem, in this work, we present a novel framework that solves the problem of polysemy by allowing sense-specific diversity in search results.
66	Spatiotemporal Activity Modeling Under Data Scarcity: A Graph-Regularized Cross-Modal Embedding Approach	Chao Zhang, Mengxiong Liu, Zhengchao Liu, Carl Yang, Luming Zhang, Jiawei Han	To address this problem, we propose BranchNet, a spatiotemporal activity model that transfers knowledge from external sources for alleviating data scarcity.
67	Unsupervised Generative Adversarial Cross-Modal Hashing	Jian Zhang, Yuxin Peng, Mingkuan Yuan	To address the above problem, in this paper we propose an Unsupervised Generative Adversarial Cross-modal Hashing approach (UGACH), which makes full use of GAN’s ability for unsupervised representation learning to exploit the underlying manifold structure of cross-modal data.
68	Exploring Implicit Feedback for Open Domain Conversation Generation	Wei-Nan Zhang, Lingzhi Li, Dongyan Cao, Ting Liu	In this paper, we propose a novel reward function which explores the implicit feedback to optimize the future reward of a reinforcement learning based neural conversation model.
69	Joint Training for Neural Machine Translation Models with Monolingual Data	Zhirui Zhang, Shujie Liu, Mu Li, Ming Zhou, Enhong Chen	In this paper, we propose a novel approach to better leveraging monolingual data for neural machine translation by jointly learning source-to-target and target-to-source NMT models for a language pair with a joint EM optimization method.
70	Attention-via-Attention Neural Machine Translation	Shenjian Zhao, Zhihua Zhang	In this paper, we leverage these similarities to improve the translation performance in neural machine translation.
71	Dynamic Network Embedding by Modeling Triadic Closure Process	Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, Yueting Zhuang	In this paper, we present a novel representation learning approach, DynamicTriad, to preserve both structural information and evolution patterns of a given network.
72	Inferring Emotion from Conversational Voice Data: A Semi-Supervised Multi-Path Generative Neural Network Approach	Suping Zhou, Jia Jia, Qi Wang, Yufei Dong, Yufeng Yin, Kehua Lei	In this paper, to better infer emotion from conversational voice data, we proposed a semi-supervised multi-path generative neural network.
73	Learning Nonlinear Dynamics in Efficient, Balanced Spiking Networks Using Local Plasticity Rules	Alireza Alemi, Christian K. Machens, Sophie Deneve, Jean-Jacques Slotine	Here, we consider the credit assignment problem, i.e. determining the local contribution of each synapse to the network’s global output error, for learning nonlinear dynamical computations in a spiking network with the desired properties of biological networks.
74	Perceiving, Learning, and Recognizing 3D Objects: An Approach to Cognitive Service Robots	S. Hamidreza Kasaei, Juil Sock, Luis Seabra Lopes, Ana Maria Tome, Tae-Kyun Kim	This paper proposes a cognitive architecture designed to create a concurrent 3D object category learning and recognition in an interactive and open-ended manner.
75	A Unified Model for Document-Based Question Answering Based on Human-Like Reading Strategy	Weikang Li, Wei Li, Yunfang Wu	Inspired by the strategy of doing reading comprehension tests, we propose a unified model based on the human-like reading strategy.
76	Thinking in PolAR Pictures: Using Rotation-Friendly Mental Images to Solve Leiter-R Form Completion	Joshua H. Palmer, Maithilee Kunda	We describe a new computational cognitive model that addresses Form Completion using a novel, mental-rotation-friendly image representation that we call the Polar Augmented Resolution (PolAR) Picture, which enables high-fidelity mental rotation operations.
77	A Plasticity-Centric Approach to Train the Non-Differential Spiking Neural Networks	Tielin Zhang, Yi Zeng, Dongcheng Zhao, Mengting Shi	In this paper, we will focus on two biological plausible methodologies and try to solve these catastrophic training problems in SNNs.
78	Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering	Somak Aditya, Yezhou Yang, Chitta Baral	In this paper, we present an explicit reasoning layer on top of a set of penultimate neural network based systems.
79	Action Recognition From Skeleton Data via Analogical Generalization Over Qualitative Representations	Kezhen Chen, Kenneth Forbus	This paper describes a new pipeline for recognizing human actions from skeleton data via analogical generalization.
80	Glass-Box Program Synthesis: A Machine Learning Approach	Konstantina Christakopoulou, Adam Tauman Kalai	In this paper, we present an intelligent search system which learns, given the partial program and the glass-box problem, the probabilities over the space of programs.
81	Learning From Unannotated QA Pairs to Analogically Disambiguate and Answer Questions	Maxwell Crouse, Clifton McFate, Kenneth Forbus	This paper introduces an analogy-based approach that instead adapts an existing general purpose semantic parser to answer questions in a novel domain by jointly learning disambiguation heuristics and query construction templates from purely textual question-answer pairs.
82	Style Transfer in Text: Exploration and Evaluation	Zhenxin Fu, Xiaoye Tan, Nanyun Peng, Dongyan Zhao, Rui Yan	We propose two models to achieve this goal.
83	HAN: Hierarchical Association Network for Computing Semantic Relatedness	Xiaolong Gong, Hao Xu, Linpeng Huang	In this paper, we explore the latent semantics (i.e., concepts) of the words to identify highly related word pairs.
84	Maximizing Activity in Ising Networks via the TAP Approximation	Christopher W. Lynn, Daniel D. Lee	In the continuous setting where one can tune the influence applied to each node, we propose a series of approximate gradient ascent algorithms based on the Plefka expansion, which generalizes the naive mean field and TAP approximations.
85	Expected Utility with Relative Loss Reduction: A Unifying Decision Model for Resolving Four Well-Known Paradoxes	Wenjun Ma, Yuncheng Jiang, Weiru Liu, Xudong Luo, Kevin McAreavey	To this end, this paper proposes a new descriptive decision-making model, expected utility with relative loss reduction, which can exhibit the same qualitative behaviours as those observed in experiments of these paradoxes without any additional parameter setting.
86	Towards Building Large Scale Multimodal Domain-Aware Conversation Systems	Amrita Saha, Mitesh M. Khapra, Karthik Sankaranarayanan	To overcome this bottleneck, in this paper we introduce the task of multimodal, domain-aware conversations, and propose the MMD benchmark dataset. Keeping these flows and states in mind, we created a dataset consisting of over 150K conversation sessions between shoppers and sales agents, with the help of in-house annotators using a semi-automated manually intense iterative process.
87	Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph	Amrita Saha, Vardaan Pahuja, Mitesh M. Khapra, Karthik Sankaranarayanan, Sarath Chandar	We believe that this new dataset coupled with the limitations of existing models as reported in this paper should encourage further research in Complex Sequential QA. Through a labor intensive semi-automatic process, involving in-house and crowdsourced workers, we created a dataset containing around 200K dialogs with a total of 1.6M turns.
88	The Structural Affinity Method for Solving the Raven’s Progressive Matrices Test for Intelligence	Snejana Shegheva, Ashok Goel	We present the Structural Affinity method that uses graphical models for first learning and subsequently recognizing the pattern for solving problems on the Raven’s Progressive Matrices Test of general human intelligence.
89	RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems	Chongyang Tao, Lili Mou, Dongyan Zhao, Rui Yan	In this paper, we propose RUBER, a Referenced metric and Unreferenced metric Blended Evaluation Routine, which evaluates a reply by taking into consideration both a groundtruth reply and a query (previous user-issued utterance).
90	Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory	Hao Zhou, Minlie Huang, Tianyang Zhang, Xiaoyan Zhu, Bing Liu	In this paper, we propose Emotional Chatting Machine (ECM) that can generate appropriate responses not only in content (relevant and grammatical) but also in emotion (emotionally consistent).
91	Transferring Decomposed Tensors for Scalable Energy Breakdown Across Regions	Nipun Batra, Yiling Jia, Hongning Wang, Kamin Whitehouse	In this paper, we propose a novel region independent energy breakdown model via statistical transfer learning.
92	Scalable Relaxations of Sparse Packing Constraints: Optimal Biocontrol in Predator-Prey Networks	Johan Bjorck, Yiwei Bai, Xiaojian Wu, Yexiang Xue, Mark Whitmore, Carla Gomes	We evaluate our contributions in the context of biocontrol for the insect pest Hemlock Wolly Adelgid (HWA) in eastern North America.
93	DyETC: Dynamic Electronic Toll Collection for Traffic Congestion Alleviation	Haipeng Chen, Bo An, Guni Sharon, Josiah P. Hanna, Peter Stone, Chunyan Miao, Yeng Chai Soh	In this paper, we propose a novel dynamic ETC (DyETC) scheme which adjusts tolls to traffic conditions in realtime.
94	Cellular Network Traffic Scheduling With Deep Reinforcement Learning	Sandeep Chinchali, Pan Hu, Tianshu Chu, Manu Sharma, Manu Bansal, Rakesh Misra, Marco Pavone, Sachin Katti	In this paper, we present a reinforcement learning (RL) based scheduler that can dynamically adapt to traffic variation, and to various reward functions set by network operators, to optimally schedule IoT traffic.
95	Dispatch Guided Allocation Optimization for Effective Emergency Response	Supriyo Ghosh, Pradeep Varakantham	This paper explores the use of latent factor models to predict interactions that will occur in new contexts (e.g. a different distribution of the set of plant species) based on an observed network.
96	DeepUrbanMomentum: An Online Deep-Learning System for Short-Term Urban Mobility Prediction	Renhe Jiang, Xuan Song, Zipei Fan, Tianqi Xia, Quanjun Chen, Satoshi Miyazawa, Ryosuke Shibasaki	Therefore, in this study, we build an online system called DeepUrbanMomentum to conduct the next short-term mobility predictions by using (the limited steps of) currently observed human mobility data.
97	Variational BOLT: Approximate Learning in Factorial Hidden Markov Models With Application to Energy Disaggregation	Henning Lange, Mario Berges	In this paper we propose a variational learning algorithm mimicking the Baum-Welch algorithm.
98	Group Sparse Bayesian Learning for Active Surveillance on Epidemic Dynamics	Hongbin Pei, Bo Yang, Jiming Liu, Lei Dong	We propose a novel measure, the gamma value, to identify the sentinels by modeling a sentinel network with row sparsity structure.
99	Predicting Links in Plant-Pollinator Interaction Networks Using Latent Factor Models With Implicit Feedback	Eugene Seo, Rebecca A. Hutchinson	This paper explores the use of latent factor models to predict interactions that will occur in new contexts (e.g. a different distribution of the set of plant species) based on an observed network.
100	Computation Error Analysis of Block Floating Point Arithmetic Oriented Convolution Neural Network Accelerator Design	Zhourui Song, Zhenyu Liu, Dongsheng Wang	In this paper, we verify the effects of word width definitions in BFP to the CNN performance without retraining.
101	Multi-Entity Dependence Learning With Rich Context via Conditional Variational Auto-Encoder	Luming Tang, Yexiang Xue, Di Chen, Carla P. Gomes	We propose MEDL_CVAE, which encodes a conditional multivariate distribution as a generating process.
102	Optimal Spot-Checking for Improving Evaluation Accuracy of Peer Grading Systems	Wanyuan Wang, Bo An, Yichuan Jiang	By further exploiting structural properties of the relaxed problem, we propose an efficient algorithm to that relaxation, which also gives a good approximation of the original OptSC.
103	Preventing Infectious Disease in Dynamic Populations Under Uncertainty	Bryan Wilder, Sze-Chuan Suen, Milind Tambe	We present an algorithm to optimally allocate limited outreach resources among demographic groups in the population.
104	Efficiently Approximating the Pareto Frontier: Hydropower Dam Placement in the Amazon Basin	Xiaojian Wu, Jonathan Gomes-Selman, Qinru Shi, Yexiang Xue, Roosevelt Garcia-Villacorta, Elizabeth Anderson, Suresh Sethi, Scott Steinschneider, Alexander Flecker, Carla Gomes	We propose a fully polynomial-time approximation scheme based on Dynamic Programming (DP) for computing a polynomially succinct curve that approximates the Pareto frontier to within an arbitrarily small epsilon > 0 on tree-structured networks.
105	Minesweeper with Limited Moves	Serge Gaspers, Stefan Rümmele, Abdallah Saffidine, Kevin Tran	We consider the problem of playing Minesweeper with a limited number of moves: Given a partially revealed board, a number of available clicks k, and a target probability p, can we win with probability p.
106	Event Representations for Automated Story Generation with Deep Neural Nets	Lara J. Martin, Prithviraj Ammanabrolu, Xinyu Wang, William Hancock, Shruti Singh, Brent Harrison, Mark O. Riedl	We present a technique for preprocessing textual story data into event sequences.
107	Asymmetric Action Abstractions for Multi-Unit Control in Adversarial Real-Time Games	Rubens O. Moraes, Levi H. S. Lelis	In this paper we introduce search algorithms that use an action abstraction scheme we call asymmetric abstraction.
108	PVL: A Framework for Navigating the Precision-Variety Trade-Off in Automated Animation of Smiles	Nicholas Sohre, Moses Adeagbo, Nathaniel Helwig, Sofia Lyford-Pike, Stephen J. Guy	In this work, we investigate the problem of procedurally generating a diverse variety of facial animations that express a given semantic quality (e.g., very happy).
109	Utilitarians Without Utilities: Maximizing Social Welfare for Graph Problems Using Only Ordinal Preferences	Ben Abramowitz, Elliot Anshelevich	We consider ordinal approximation algorithms for a broad class of utility maximization problems for multi-agent systems.
110	On the Complexity of Extended and Proportional Justified Representation	Haris Aziz, Edith Elkind, Shenwei Huang, Martin Lackner, Luis Sanchez-Fernandez, Piotr Skowron	In this paper, we answer open questions from prior work by showing that EJR and PJR have the same worst-case complexity: we provide two polynomial-time algorithms that output committees providing EJR, yet we show that it is coNP-complete to decide whether a given committee provides PJR.
111	Rank Maximal Equal Contribution: A Probabilistic Social Choice Function	Haris Aziz, Pang Luo, Christine Rizkallah	We consider participation as formalized by Brandl, Brandt, and Hofbauer (2015) based on the stochastic dominance (SD) relation.
112	Groupwise Maximin Fair Allocation of Indivisible Goods	Siddharth Barman, Arpita Biswas, Sanath Kumar Krishnamurthy, Yadati Narahari	Motivated by these considerations, in this work we define a stronger notion of fairness, called groupwise maximin share guarantee (GMMS).
113	Truthful and Near-Optimal Mechanisms for Welfare Maximization in Multi-Winner Elections	Umang Bhaskar, Varsha Dani, Abheek Ghosh	We consider the utilitarian social welfare of mechanisms for preference aggregation, measured by the distortion.
114	Multiwinner Elections With Diversity Constraints	Robert Bredereck, Piotr Faliszewski, Ayumi Igarashi, Martin Lackner, Piotr Skowron	We develop a model of multiwinner elections that combines performance-based measures of the quality of the committee (such as, e.g., Borda scores of the committee members) with diversity constraints.
115	A Bayesian Clearing Mechanism for Combinatorial Auctions	Gianluca Brero, Sébastien Lahaie	We cast the problem of combinatorial auction design in a Bayesian framework in order to incorporate prior information into the auction process and minimize the number of rounds to convergence.
116	AIVAT: A New Variance Reduction Technique for Agent Evaluation in Imperfect Information Games	Neil Burch, Martin Schmid, Matej Moravcik, Dustin Morill, Michael Bowling	In this paper, we introduce AIVAT, a low variance, provably unbiased value assessment tool that exploits an arbitrary heuristic estimate of state value, as well as the explicit strategy of a subset of the agents.
117	Reinforcement Mechanism Design for Fraudulent Behaviour in e-Commerce	Qingpeng Cai, Aris Filos-Ratsikas, Pingzhong Tang, Yiwei Zhang	In this paper, we employ the principles of reinforcement mechanism design, a framework that combines the fundamental goals of classical mechanism design, i.e. the consideration of agents’ incentives and their alignment with the objectives of the designer, with deep reinforcement learning for optimizing the performance based on these incentives.
118	Computational Results for Extensive-Form Adversarial Team Games	Andrea Celli, Nicola Gatti	We provide, to the best of our knowledge, the first computational study of extensive-form adversarial team games.
119	On the Distortion of Voting With Multiple Representative Candidates	Yu Cheng, Shaddin Dughmi, David Kempe	We study positional voting rules when candidates and voters are embedded in a common metric space, and cardinal preferences are naturally given by distances in the metric space.
120	Disarmament Games With Resource	Yuan Deng, Vincent Conitzer	In this paper, we introduce a model of disarmament games in which resources, rather than strategies, are removed.
121	Computing the Strategy to Commit to in Polymatrix Games	Giuseppe De Nittis, Alberto Marchesi, Nicola Gatti	In this paper, we look for efficiently solvable games with multiple followers who play either optimistically or pessimistically, i.e., breaking ties in favour or against the leader.
122	Resource Allocation Polytope Games: Uniqueness of Equilibrium, Price of Stability, and Price of Anarchy	Swapnil Dhamal, Walid Ben-Ameur, Tijani Chahed, Eitan Altman	We hence present an efficient algorithm to compute the price of anarchy and the price of stability, given an instance of the game.
123	Allocation Problems in Ride-Sharing Platforms: Online Matching With Offline Reusable Resources	John P. Dickerson, Karthik A. Sankararaman, Aravind Srinivasan, Pan Xu	In this paper, we propose a new model, Online Matching with (offline) Reusable Resources under Known Adversarial Distributions (OM-RR-KAD), in which resources on the offline side are reusable instead of disposable; that is, once matched, resources become available again at some point in the future.
124	Tool Auctions	Janosch Döcker, Britta Dorn, Ulle Endriss, Ronald de Haan, Sebastian Schneckenburger	We introduce tool auctions, a novel market mechanism for constructing a cost-efficient assembly line for producing a desired set of products from a given set of goods and tools.
125	Effective Heuristics for Committee Scoring Rules	Piotr Faliszewski, Martin Lackner, Dominik Peters, Nimrod Talmon	As computing winning committees under such rules is generally intractable, in this paper we investigate efficient heuristics for this task.
126	On Social Envy-Freeness in Multi-Unit Markets	Michele Flammini, Manuel Mauro, Matteo Tonelli	We focus on the revenue maximization problem in multi-unit markets, in which there are multiple copies of a same item being sold and each buyer is assigned a set of identical items.
127	Facility Location Games With Fractional Preferences	Chi Kit Ken Fong, Minming Li, Pinyan Lu, Taiki Todo, Makoto Yokoo	In this paper, we propose a fractional preference model for the facility location game with two facilities that serve the similar purpose on a line where each agent has his location information as well as fractional preference to indicate how well they prefer the facilities.
128	The Complexity of Bribery in Network-Based Rating Systems	Umberto Grandi, James Stewart, Paolo Turrini	We study the complexity of bribery in a network-based rating system, where individuals are connected in a social network and an attacker, typically a service provider, can influence their rating and increase the overall profit.
129	Weighted Voting Via No-Regret Learning	Nika Haghtalab, Ritesh Noothigattu, Ariel D. Procaccia	To develop a formal framework for desirable weighting schemes, we draw on no-regret learning.
130	Cooperative Games With Bounded Dependency Degree	Ayumi Igarashi, Rani Izsak, Edith Elkind	In this paper, we incorporate complexity measures recently proposed by Feige and Izsak (2013), called dependency degree and supermodular degree, into the complexity analysis of coopera- tive games.
131	Committee Selection with Intraclass and Interclass Synergies	Rani Izsak, Nimrod Talmon, Gerhard J. Woeginger	As taking into account all possible relations between the alternatives is generally computationally intractable, in this paper we consider classes of alternatives; intuitively, the number of classes is significantly smaller than the number of alternatives, and thus there is some hope in reaching computational tractability.
132	On Recognising Nearly Single-Crossing Preferences	Florian Jaeckle, Dominik Peters, Edith Elkind	We consider three distance measures, which are based on partitioning voters or candidates or performing a small number of swaps in each vote.
133	Ranking Wily People Who Rank Each Other	Anson Kahng, Yasmine Kotturi, Chinmay Kulkarni, David Kurokawa, Ariel D. Procaccia	We study rank aggregation algorithms that take as input the opinions of players over their peers, represented as rankings, and output a social ordering of the players (which reflects, e.g., relative contribution to a project or fit for a job).
134	Liquid Democracy: An Algorithmic Perspective	Anson Kahng, Simon Mackenzie, Ariel D. Procaccia	We study liquid democracy, a collective decision making paradigm that allows voters to transitively delegate their votes, through an algorithmic lens.
135	Policy Learning for Continuous Space Security Games Using Neural Networks	Nitin Kamra, Umang Gupta, Fei Fang, Yan Liu, Milind Tambe	In this paper, we consider a continuous space security game model with infinite-size action sets for players and present a novel deep learning based approach to extend the existing toolkit for solving security games.
136	Approximately Stable Matchings With Budget Constraints	Yasushi Kawase, Atsushi Iwasaki	This paper examines two-sided matching with budget constraints where one side (a firm or hospital) can make monetary transfers (offer wages) to the other (a worker or doctor).
137	Approximating Bribery in Scoring Rules	Orgad Keller, Avinatan Hassidim, Noam Hazon	The classic bribery problem is to find a minimal subset of voters who need to change their vote to make some preferred candidate win.We find an approximate solution for this problem for a broad family of scoring rules (which includes Borda and t-approval), in the following sense: if there is a strategy which requires bribing k voters, we efficiently find a strategy which requires bribing at most k + Õ(√k) voters.
138	Robust Stackelberg Equilibria in Extensive-Form Games and Extension to Limited Lookahead	Christian Kroer, Gabriele Farina, Tuomas Sandholm	We introduce robust Stackelberg equilibria, where the uncertainty is about the opponent’s payoffs, as well as ones where the opponent has limited lookahead and the uncertainty is about the opponent’s node evaluation function.
139	The Conference Paper Assignment Problem: Using Order Weighted Averages to Assign Indivisible Goods	Jing Wu Lian, Nicholas Mattei, Renee Noble, Toby Walsh	We propose a novel mechanism for solving the assignment problem when we have a two sided matching problem with preferences from one side (the agents/reviewers) over the other side (the objects/papers) and both sides have capacity constraints.
140	Incentivizing High Quality User Contributions: New Arm Generation in Bandit Learning	Yang Liu, Chien-Ju Ho	To address the incentive issue, we consider a model in which users are strategic in deciding whether to contribute and are motivated by exposure, i.e., they aim to maximize the number of times their contributions are viewed.
141	On the Approximation of Nash Equilibria in Sparse Win-Lose Games	Zhengyang Liu, Ying Sheng	We show that the problem of finding an approximate Nash equilibrium with a polynomial precision is PPAD-hard even for two-player sparse win-lose games (i.e., games with {0,1}-entries such that each row and column of the two n×n payoff matrices have at most O(log n) many ones).
142	Balancing Lexicographic Fairness and a Utilitarian Objective With Application to Kidney Exchange	Duncan C. McElfresh, John P. Dickerson	In this work, we close an open problem regarding the theoretical price of fairness in modern kidney exchanges.
143	Single-Peakedness and Total Unimodularity: New Polynomial-Time Algorithms for Multi-Winner Elections	Dominik Peters	We introduce a new technique: carefully chosen integer linear programming (IP) formulations for certain voting problems admit an LP relaxation which is totally unimodular if preferences are single-peaked, and which thus admits an integral optimal solution.
144	Fair Rent Division on a Budget	Ariel D. Procaccia, Rodrigo A. Velez, Dingli Yu	By contrast, we design a polynomial-time algorithm that takes budget constraints as part of its input; it determines whether there exist envy-free allocations that satisfy the budget constraints, and, if so, computes one that optimizes an additional criterion of justice.
145	Approximation-Variance Tradeoffs in Facility Location Games	Ariel D. Procaccia, David Wajc, Hanrui Zhang	We revisit the well-studied problem of constructing strategyproof approximation mechanisms for facility location games, but offer a fundamentally new perspective by considering risk averse designers.
146	MUDA: A Truthful Multi-Unit Double-Auction Mechanism	Erel Segal-Halevi, Avinatan Hassidim, Yonatan Aumann	This paper presents a double-auction mechanism that handles multi-parametric agents and allows multiple units per trader, as long as the valuation functions of all traders have decreasing marginal returns.
147	Traffic Optimization for a Mixture of Self-Interested and Compliant Agents	Guni Sharon, Michael Albert, Tarun Rambha, Stephen Boyles, Peter Stone	Motivated by such scenarios, a computationally tractable method is presented that computes the minimal amount of agents that the system manager needs to influence (compliant agents) in order to achieve system optimal performance.
148	Coalition Manipulation of Gale-Shapley Algorithm	Weiran Shen, Pingzhong Tang, Yuan Deng	In this paper, we consider manipulations by any subset of women with arbitrary preferences.
149	Axioms for Distance-Based Centralities	Oskar Skibski, Jadwiga Sosnowska	Building upon our analysis, we propose the class of additive distance-based centralities and pin-point properties which combined with the axiomatic characterization of the whole class uniquely characterize a number of centralities from the literature.
150	Non-Exploitable Protocols for Repeated Cake Cutting	Omer Tamuz, Shai Vardi, Juba Ziani	We introduce the notion of exploitability in cut-and-choose protocols for repeated cake cutting.
151	Modelling Iterative Judgment Aggregation	Zoi Terzopoulou, Ulle Endriss	We introduce a formal model of iterative judgment aggregation, enabling the analysis of scenarios in which agents repeatedly update their individual positions on a set of issues, before a final decision is made by applying an aggregation rule to these individual positions.
152	Rich Coalitional Resource Games	Nicolas Troquard	We propose a simple model of interaction for resource-conscious agents.
153	It Takes (Only) Two: Adversarial Generator-Encoder Networks	Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky	We present a new autoencoder-type architecture that is trainable in an unsupervised mode, sustains both generation and inference, and has the quality of conditional and unconditional samples boosted by adversarial learning.
154	An Axiomatization of the Eigenvector and Katz Centralities	Tomasz Wąs, Oskar Skibski	In this paper, we study the theoretical underpinning of the feedback centralities.
155	A Regression Approach for Modeling Games With Many Symmetric Players	Bryce Wiedenbeck, Fengjun Yang, Michael P. Wellman	Computing these expectations exactly requires an infeasible sum over the full payoff matrix, so we propose and test several approximation methods.
156	Equilibrium Computation and Robust Optimization in Zero Sum Games With Submodular Structure	Bryan Wilder	We provide a pseudopolynomial-time algorithm which obtains a guaranteed (1 – 1/e)^2-approximate mixed strategy for the maximizing player.
157	Incentive-Compatible Forecasting Competitions	Jens Witkowski, Rupert Freeman, Jennifer Wortman Vaughan, David M. Pennock, Andreas Krause	In this paper, we introduce a truthful forecaster selection mechanism.
158	Strategic Coordination of Human Patrollers and Mobile Sensors With Signaling for Security Games	Haifeng Xu, Kai Wang, Phebe Vayanos, Milind Tambe	Motivated by the emerging application of utilizing mobile sensors (e.g., UAVs) for patrolling, in this paper we propose the novel Sensor-Empowered security Game (SEG) model which captures the joint allocation of human patrollers and mobile sensors.
159	Average-Case Approximation Ratio of Scheduling Without Payments	Jie Zhang	In this paper, we take the average-case analysis approach, and tackle one of the primary motivating problems in Algorithmic Mechanism Design — the scheduling problem [Nisan and Ronen 1999].
160	Avoiding Dead Ends in Real-Time Heuristic Search	Bence Cserna, William J. Doyle, Jordan S. Ramsdell, Wheeler Ruml	We introduce new real-time heuristic search methods that can guarantee safety if the domain obeys certain properties.
161	Efficiently Monitoring Small Data Modification Effect for Large-Scale Learning in Changing Environment	Hiroyuki Hanada, Atsushi Shibagaki, Jun Sakuma, Ichiro Takeuchi	In this paper, we propose a novel method, called the optimal solution bounding (OSB), for monitoring such a data modification effect on the optimal model by efficiently evaluating (without actually re-training) it.
162	A Recursive Scenario Decomposition Algorithm for Combinatorial Multistage Stochastic Optimisation Problems	David Hemmi, Guido Tack, Mark Wallace	In this paper we propose a scenario decomposition method to solve multistage stochastic combinatorial decision problems recursively.
163	Locality Preserving Projection Based on F-norm	Xiangjie Hu, Yanfeng Sun, Junbin Gao, Yongli Hu, Baocai Yin	In order to deal with this issue, we propose two novel F-norm-based models, termed as F-LPP and F-2DLPP, which are developed for vector-based and matrix-based data, respectively.
164	A Two-Stage MaxSAT Reasoning Approach for the Maximum Weight Clique Problem	Hua Jiang, Chu-Min Li, Yanli Liu, Felip Manyà	In this paper, we describe a new BnB algorithm for MWC that incorporates a novel two-stage MaxSAT reasoning approach.
165	Revisiting Immediate Duplicate Detection in External Memory Search	Shunji Lin, Alex Fukunaga	We propose segmented compression, an improved IDD method that significantly reduces the number of false positive access into secondary memory.
166	Warmstarting of Model-Based Algorithm Configuration	Marius Lindauer, Frank Hutter	We introduce two complementary ways in which we can exploit this information to warmstart AC methods based on a predictive model.
167	On the Time and Space Complexity of Genetic Programming for Evolving Boolean Conjunctions	Andrei Lissovoi, Pietro S. Oliveto	In this paper we present a performance analysis that sheds light on the behaviour of simple GP systems for evolving conjunctions of n variables (AND_n).
168	Proximal Alternating Direction Network: A Globally Converged Deep Unrolling Framework	Risheng Liu, Xin Fan, Shichao Cheng, Xiangyu Wang, Zhongxuan Luo	This paper provides a novel proximal unrolling framework to establish deep models by integrating experimentally verified network architectures and rich cues of the tasks.
169	Streaming Non-Monotone Submodular Maximization: Personalized Video Summarization on the Fly	Baharan Mirzasoleiman, Stefanie Jegelka, Andreas Krause	We develop the first efficient single pass streaming algorithm, Streaming Local Search, that for any streaming monotone submodular maximization algorithm with approximation guarantee α under a collection of independence systems I, provides a constant 1/(1+2/√α+1/α+2d(1+√α)) approximation guarantee for maximizing a non-monotone submodular function under the intersection of I and d knapsack constraints.
170	Exact Clustering via Integer Programming and Maximum Satisfiability	Atsushi Miyauchi, Tomohiro Sonobe, Noriyoshi Sukegawa	In this study, we investigate the design of mathematical programming formulations and constraint satisfaction formulations for the problem.
171	On Multiset Selection With Size Constraints	Chao Qian, Yibo Zhang, Ke Tang, Xin Yao	We propose an anytime randomized iterative approach POMS, which maximizes the given objective f and minimizes the multiset size simultaneously.
172	Disjunctive Program Synthesis: A Robust Approach to Programming by Example	Mohammad Raza, Sumit Gulwani	In this work we present a different approach to PBE in which the system avoids making a ranking decision at the synthesis stage, by instead synthesizing a disjunctive program that includes the many top-ranked programs as possible alternatives and selects between these different choices upon execution on a new input.
173	Accelerated Best-First Search With Upper-Bound Computation for Submodular Function Maximization	Shinsaku Sakaue, Masakazu Ishihata	Submodular maximization continues to be an attractive subject of study thanks to its applicability to many real-world problems.
174	Submodular Function Maximization Over Graphs via Zero-Suppressed Binary Decision Diagrams	Shinsaku Sakaue, Masaaki Nishino, Norihito Yasuda	In this paper, we consider a very general class of SFM with such complex constraints (e.g., an s-t path constraint on a given graph).
175	Counting Linear Extensions in Practice: MCMC Versus Exponential Monte Carlo	Topi Talvitie, Kustaa Kangas, Teppo Niinimäki, Mikko Koivisto	This work presents an empirical evaluation of the state-of-the-art schemes and investigates a number of ideas to enhance their performance.
176	Large Scale Constrained Linear Regression Revisited: Faster Algorithms via Preconditioning	Di Wang, Jinhui Xu	In this paper, we revisit the large-scale constrained linear regression problem and propose faster methods based on some recent developments in sketching and optimization.
177	Noisy Derivative-Free Optimization With Value Suppression	Hong Wang, Hong Qian, Yang Yu	This work further delays the noise handling, and proposes a simple noise handling mechanism, i.e., value suppression.
178	Memory-Augmented Monte Carlo Tree Search	Chenjun Xiao, Jincheng Mei, Martin Müller	This paper proposes and evaluates Memory-Augmented Monte Carlo Tree Search (M-MCTS), which provides a new approach to exploit generalization in online real-time search.
179	A Coverage-Based Utility Model for Identifying Unknown Unknowns	Gagan Bansal, Daniel S. Weld	In response, this paper proposes a new class of utility models that rewards how well the discovered UUs cover (or “explain”) a sample distribution of expected queries.
180	Toward Deep Reinforcement Learning Without a Simulator: An Autonomous Steering Example	Bar Hilleli, Ran El-Yaniv	We propose a scheme for training a computerized agent to perform complex human tasks such as highway steering.
181	An Interactive Multi-Label Consensus Labeling Model for Multiple Labeler Judgments	Ashish Kulkarni, Narasimha Raju Uppalapati, Pankaj Singh, Ganesh Ramakrishnan	We propose strategies based on interaction and active learning to obtain higher quality labels that potentially lead to greater consensus.
182	Interactively Learning a Blend of Goal-Based and Procedural Tasks	Aaron Mininger, John E. Laird	We present a hybrid approach to interactive task learning that can learn both goal-oriented and procedural tasks, and mixtures of the two, from human natural language instruction.
183	Emergence of Grounded Compositional Language in Multi-Agent Populations	Igor Mordatch, Pieter Abbeel	In this paper we investigate if, and how, grounded compositional language can emerge as a means to achieve goals in multi-agent populations.
184	Human-in-the-Loop SLAM	Samer B. Nashed, Joydeep Biswas	For such scenarios, where state-of-the-art mapping algorithms produce globally inconsistent maps, we introduce a systematic approach to incorporating sparse human corrections, which we term Human-in-the-Loop Simultaneous Localization and Mapping (HitL-SLAM).
185	An Interpretable Joint Graphical Model for Fact-Checking From Crowds	An T. Nguyen, Aditya Kharosekar, Matthew Lease, Byron Wallace	We introduce a fast variational method for parameter estimation.
186	How AI Wins Friends and Influences People in Repeated Games With Cheap Talk	Mayada Oudah, Talal Rahwan, Tawna Crandall, Jacob W. Crandall	In this paper, we study how AI systems should be designed to win friends and influence people in repeated games with cheap talk (RGCTs).
187	Anchors: High-Precision Model-Agnostic Explanations	Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin	We propose an algorithm to efficiently compute these explanations for any black-box model with high-probability guarantees.
188	Optimizing Interventions via Offline Policy Evaluation: Studies in Citizen Science	Avi Segal, Kobi Gal, Ece Kamar, Eric Horvitz, Grant Miller	We propose a computational approach for increasing users’ engagement in such settings that is based on optimizing policies for displaying motivational messages to users.
189	Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces	Garrett Warnell, Nicholas Waytowich, Vernon Lawhern, Peter Stone	In this paper, we do both: we propose DeepTAMER, an extension of the TAMER framework that leverages the representational power of deep neural networks inorder to learn complex tasks in just a short amount of time with a human trainer.
190	Semi-Supervised Learning From Crowds Using Deep Generative Models	Kyohei Atarashi, Satoshi Oyama, Masahito Kurihara	This paper presents a novel generative model of the labeling process in crowdsourcing.
191	Sentiment Analysis via Deep Hybrid Textual-Crowd Learning Model	Kamran Ghasedi Dizaji, Heng Huang	In this paper, we propose a novel hybrid model to exploit both crowd and text data for sentiment analysis, consisting of a generative crowdsourcing aggregation model and a deep sentimental autoencoder.
192	Understanding Over Participation in Simple Contests	Priel Levy, David Sarne	In this paper we make use of “simple contests,” where contestants only need to strategize on whether to participate in the contest or not, as an infrastructure for studying whether indeed more effort is exerted in contests due to competitiveness, or perhaps this can be attributed to other factors that hold also in non-competitive settings.
193	Understanding Social Interpersonal Interaction via Synchronization Templates of Facial Events	Rui Li, Jared Curhan, Mohammed Ehsan Hoque	In this paper, we propose a probabilistic framework to model interactional synchronization between conversation partners based on their facial expressions.
194	A Voting-Based System for Ethical Decision Making	Ritesh Noothigattu, Snehalkumar S. Gaikwad, Edmond Awad, Sohan Dsouza, Iyad Rahwan, Pradeep Ravikumar, Ariel D. Procaccia	We present a general approach to automating ethical decisions, drawing on machine learning and computational social choice.
195	Partial Truthfulness in Minimal Peer Prediction Mechanisms With Limited Knowledge	Goran Radanovic, Boi Faltings	We study minimal single-task peer prediction mechanisms that have limited knowledge about agents’ beliefs.
196	Information Gathering With Peers: Submodular Optimization With Peer-Prediction Constraints	Goran Radanovic, Adish Singla, Andreas Krause, Boi Faltings	We study a problem of optimal information gathering from multiple data providers that need to be incentivized to provide accurate information.
197	Deep Learning from Crowds	Filipe Rodrigues, Francisco C. Pereira	In this paper, we address the problem of learning deep neural networks from crowds.
198	AdaFlock: Adaptive Feature Discovery for Human-in-the-loop Predictive Modeling	Ryusuke Takahama, Yukino Baba, Nobuyuki Shimizu, Sumio Fujita, Hisashi Kashima	In this paper, we present a novel algorithm called AdaFlock to efficiently obtain informative features through crowdsourcing.
199	Adversarial Learning for Chinese NER From Crowd Annotations	YaoSheng Yang, Meishan Zhang, Wenliang Chen, Wei Zhang, Haofen Wang, Min Zhang	In this paper, we propose an approach to performing crowd annotation learning for Chinese Named Entity Recognition (NER) to make full use of the noisy sequence labels from multiple annotators. In our experiments, we create two data sets for Chinese NER tasks from two domains.
200	Adapting a Kidney Exchange Algorithm to Align With Human Values	Rachel Freedman, Jana Schaich Borg, Walter Sinnott-Armstrong, John P. Dickerson, Vincent Conitzer	In this paper, we provide an end-to-end methodology for estimating weights of individual participant profiles in a kidney exchange.
201	State of the Art: Reproducibility in Artificial Intelligence	Odd Erik Gundersen, Sigbjørn Kjensmo	Hypotheses: 1) AI research is not documented well enough to reproduce the reported results.
202	Towards Imperceptible and Robust Adversarial Example Attacks Against Neural Networks	Bo Luo, Yannan Liu, Lingxiao Wei, Qiang Xu	In this work, we present a new adversarial example attack crafting method, which takes the human perceptual system into consideration and maximizes the noise tolerance of the crafted adversarial example.
203	Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients	Andrew Slavin Ross, Finale Doshi-Velez	In this work, we evaluate the effectiveness of defenses that differentiably penalize the degree to which small changes in inputs can alter model predictions.
204	Beyond Sparsity: Tree Regularization of Deep Models for Interpretability	Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez	In this work, we explicitly regularize deep models so human users might step through the process behind their predictions in little time.
205	Coupled Deep Learning for Heterogeneous Face Recognition	Xiang Wu, Lingxiao Song, Ran He, Tieniu Tan	This paper proposes a coupled deep learning (CDL) approach for the heterogeneous face matching.
206	A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning Agents	Yueh-Hua Wu, Shou-De Lin	This paper proposes a low-cost, easily realizable strategy to equip a reinforcement learning (RL) agent the capability of behaving ethically.
207	Deception Detection in Videos	Zhe Wu, Bharat Singh, Larry S. Davis, V. S. Subrahmanian	We present a system for covert automated deception detection using information available in a video.
208	Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface	Dalin Zhang, Lina Yao, Xiang Zhang, Sen Wang, Weitong Chen, Robert Boots, Boualem Benatallah	In this paper, we introduce both cascade and parallel convolutional recurrent neural network models for precisely identifying human intended movements and instructions effectively learning the compositional spatio-temporal representations of raw EEG streams.
209	WiFi-Based Human Identification via Convex Tensor Shapelet Learning	Han Zou, Yuxun Zhou, Jianfei Yang, Weixi Gu, Lihua Xie, Costas J. Spanos	We propose AutoID, a human identification system that leverages the measurements from existing WiFi-enabled Internet of Things (IoT) devices and produces the identity estimation via a novel sparse representation learning technique.
210	Externally Supported Models for Efficient Computation of Paracoherent Answer Sets	Giovanni Amendola, Carmine Dodaro, Wolfgang Faber, Francesco Ricca	In this paper we present an alternative characterization of the two major paracoherent semantics in terms of (extended) externally supported models.
211	Combining Rules and Ontologies into Clopen Knowledge Bases	Labinot Bajraktari, Magdalena Ortiz, Mantas Šimkus	We propose Clopen Knowledge Bases (CKBs) as a new formalism combining Answer Set Programming (ASP) with ontology languages based on first-order logic.
212	How Many Properties Do We Need for Gradual Argumentation?	Pietro Baroni, Antonio Rago, Francesca Toni	In this paper we provide a systematic analysis for this research landscape by making three main contributions.
213	Situation Calculus Semantics for Actual Causality	Vitaliy Batusov, Mikhail Soutchanski	We tackle both of these issues using a novel approach.
214	Complexity of Verification in Incomplete Argumentation Frameworks	Dorothea Baumeister, Daniel Neugebauer, Jörg Rothe, Hilmar Schadrack	We combine both models into a model of general incompleteness, complement previous results on the complexity of the verification problem in incomplete argumentation frameworks, and provide a full complexity map covering all three models and all classical semantics.
215	Goal-Driven Query Answering for Existential Rules With Equality	Michael Benedikt, Boris Motik, Efthymia Tsamoura	Inspired by the magic sets for Datalog, we present a novel goal-driven approach for answering queries over terminating existential rules with equality (aka TGDs and EGDs).
216	LTLf/LDLf Non-Markovian Rewards	Ronen I. Brafman, Giuseppe De Giacomo, Fabio Patrizi	Building on recent progress in temporal logics over finite traces, we adopt LDLf for specifying non-Markovian rewards and provide an elegant automata construction for building a Markovian model, which extends that of previous work and offers strong minimality and compositionality guarantees.
217	Weighted Abstract Dialectical Frameworks	Gerhard Brewka, Hannes Strass, Johannes P. Wallner, Stefan Woltran	Abstract Dialectical Frameworks (ADFs) generalize Dung’s argumentation frameworks allowing various relationships among arguments to be expressed in a systematic way.
218	SELF: Structural Equational Likelihood Framework for Causal Discovery	Ruichu Cai, Jie Qiao, Zhenjie Zhang, Zhifeng Hao	We provide thorough analysis on the soundness of the model under mild conditions and present efficient heuristic-based algorithms for scalable model training.
219	SenticNet 5: Discovering Conceptual Primitives for Sentiment Analysis by Means of Context Embeddings	Erik Cambria, Soujanya Poria, Devamanyu Hazarika, Kenneth Kwok	In this work, we couple sub-symbolic and symbolic AI to automatically discover conceptual primitives from text and link them to commonsense concepts and named entities in a new three-level knowledge representation for sentiment analysis.
220	Towards a Unified Framework for Syntactic Inconsistency Measures	Glauber De Bona, John Grant, Anthony Hunter, Sébastien Konieczny	In this paper, we introduce a general framework for comparing syntactic inconsistency measures.
221	Convolutional 2D Knowledge Graph Embeddings	Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel	In this work we introduce ConvE, a multi-layer convolutional network model for link prediction, and report state-of-the-art results for several established datasets.
222	TorusE: Knowledge Graph Embedding on a Lie Group	Takuma Ebisu, Ryutaro Ichise	This paper proposes a novel embedding model, TorusE, to solve the regularization problem.
223	Rational Inference Patterns Based on Conditional Logic	Christian Eichhorn, Gabriele Kern-Isberner, Marco Ragni	In this paper we define inference patterns as a formalization of the joint usage or avoidance of these rules.
224	Dependence in Propositional Logic: Formula-Formula Dependence and Formula Forgetting – Application to Belief Update and Conservative Extension	Liangda Fang, Hai Wan, Xianqiao Liu, Biqing Fang, Zhaorong Lai	In this paper, we propose two novel notions of dependence in propositional logic: formula-formula dependence and formula forgetting.
225	Answering Regular Path Queries over SQ Ontologies	Víctor Gutiérrez-Basulto, Yazmín Ibáñez-García, Jean Christoph Jung	Our main contributions are a tree-like model property for SQ-knowledge bases and, building upon this, an optimal automata-based algorithm for answering positive existential regular path queries in 2EXPTIME.
226	Towards Formal Definitions of Blameworthiness, Intention, and Moral Responsibility	Joseph Y. Halpern, Max Kleiman-Weiner	We provide formal definitions of degree of blameworthiness and intention relative to an epistemic state (a probability over causal models and a utility function on outcomes).
227	Behavior Is Everything: Towards Representing Concepts with Sensorimotor Contingencies	Nicholas Hay, Michael Stark, Alexander Schlegel, Carter Wendelken, Dennis Park, Eric Purdy, Tom Silver, D. Scott Phoenix, Dileep George	We take a step towards closing this gap by proposing an interactive, behavior-based model that represents concepts using sensorimotor contingencies grounded in an agent’s experience.
228	Optimised Maintenance of Datalog Materialisations	Pan Hu, Boris Motik, Ian Horrocks	We present two hybrid approaches that combine DRed and B/F with Counting so as to reduce or even eliminate “backward” rule evaluation while still handling arbitrary datalog programs.
229	Qualitative Reasoning About Cardinal Directions Using Answer Set Programming	Yusuf Izmirlioglu, Esra Erdem	We propose a novel method for representing and reasoning about an incomplete set of constraints about basic/disjunctive qualitative direction relations over simple/connected/disconnected regions, using Answer Set Programming, and prove its correctness with respect to cardinal direction calculus.
230	Learning Abduction Using Partial Observability	Brendan Juba, Zongyi Li, Evan Miller	In this work we extend the formulation to utilize such partially specified examples, along with declarative background knowledge about the missing data.
231	Probabilistic Inference Over Repeated Insertion Models	Batya Kenig, Lovro Ilijasić, Haoyue Ping, Benny Kimelfeld, Julia Stoyanovich	In this paper we propose an algorithm for computing the marginal probability of an arbitrary partially ordered set over RIM.
232	Question Answering as Global Reasoning Over Semantic Abstractions	Daniel Khashabi, Tushar Khot, Ashish Sabharwal, Dan Roth	We propose a novel method for exploiting the semantic structure of text to answer multiple-choice questions.
233	In Praise of Belief Bases: Doing Epistemic Logic Without Possible Worlds	Emiliano Lorini	We introduce a new semantics for a logic of explicit and implicit beliefs based on the concept of multi-agent belief base.
234	Maximum A Posteriori Inference in Sum-Product Networks	Jun Mei, Yong Jiang, Kewei Tu	We investigate MAP inference in SPNs from both theoretical and algorithmic perspectives.
235	Fair Inference on Outcomes	Razieh Nabi, Ilya Shpitser	In this paper, we consider the problem of fair statistical inference involving outcome variables.
236	Stream Reasoning in Temporal Datalog	Alessandro Ronca, Mark Kaminski, Bernardo Cuenca Grau, Boris Motik, Ian Horrocks	In this paper, we propose novel reasoning problems to deal with these challenges, and study their computational properties on Datalog extended with a temporal sort and the successor function (a core rule-based language for stream reasoning applications).
237	On Consensus in Belief Merging	Nicolas Schwind, Pierre Marquis	We define a consensus postulate in the propositional belief merging setting.
238	Open-World Knowledge Graph Completion	Baoxu Shi, Tim Weninger	As a first attempt to solve this task we introduce an open-world KGC model called ConMask.
239	Visual Explanation by High-Level Abduction: On Answer-Set Programming Driven Reasoning About Moving Objects	Jakob Suchan, Mehul Bhatt, Przemysław Wałega, Carl Schultz	We propose a hybrid architecture for systematically computing robust visual explanation(s) encompassing hypothesis formation, belief revision, and default reasoning with video data.
240	A Framework and Positive Results for IAR-answering	Despoina Trivela, Giorgos Stoilos, Vasilis Vassalos	A Framework and Positive Results for IAR-answering
241	Repairing Ontologies via Axiom Weakening	Nicolas Troquard, Roberto Confalonieri, Pietro Galliani, Rafael Peñaloza, Daniele Porello, Oliver Kutz	We propose a new method based on weakening these axioms to make them less restrictive, employing the use of refinement operators.
242	Measuring Strong Inconsistency	Markus Ulbricht, Matthias Thimm, Gerhard Brewka	We propose measures based on this notion and investigate their behavior in a nonmonotonic setting by revisiting existing rationality postulates, analyzing the compliance of the proposed measures with these postulates, and by investigating their computational complexity.
243	Splitting an LPMLN Program	Bin Wang, Zhizheng Zhang, Hongxiang Xu, Jun Shen	In this paper, we investigate the splitting set theorem for LPMLN that is a new extension of ASP created by combining the ideas of ASP and Markov Logic Networks (MLN).
244	Incorporating GAN for Negative Sampling in Knowledge Representation Learning	Peifeng Wang, Shuangyin Li, Rong Pan	In this paper, we propose a novel knowledge representation learning framework based on Generative Adversarial Networks (GAN).
245	Forgetting and Unfolding for Existential Rules	Zhe Wang, Kewen Wang, Xiaowang Zhang	In this paper, we lay the foundation for a theory of forgetting for existential rules by developing a novel notion of unfolding.
246	Machine-Translated Knowledge Transfer for Commonsense Causal Reasoning	Jinyoung Yeo, Geungyu Wang, Hyunsouk Cho, Seungtaek Choi, Seung-won Hwang	To overcome these challenges, our goal is thus to identify key techniques to construct a new causality network of cause-effect terms, targeted for the machine-translated English, but without any language-specific knowledge of resource-poor languages.
247	Measuring Conditional Independence by Independent Residuals: Theoretical Results and Application in Causal Discovery	Hao Zhang, Shuigeng Zhou, Jihong Guan	We investigate the relationship between conditional independence (CI)x ⊥ y\|Z and the independence of two residualsx –E(x\|Z) ⊥ –E(y\|Z), wherex andy are two random variables, andZ is a set of random variables.
248	Fairness in Decision-Making — The Causal Explanation Formula	Junzhe Zhang, Elias Bareinboim	In this paper, we use the language of structural causality (Pearl, 2000) to fill in this gap.
249	Embedding of Hierarchically Typed Knowledge Bases	Richong Zhang, Fanshuang Kong, Chenyue Wang, Yongyi Mao	In this paper, we investigate the use of entity type information for knowledge base embedding.
250	On the Satisfiability Problem of Patterns in SPARQL 1.1	Xiaowang Zhang, Jan Van den Bussche, Kewen Wang, Zhe Wang	This paper provides a complete analysis of decidability/undecidability of satisfiability problems for SPARQL 1.1 patterns.
251	Predicting Vehicular Travel Times by Modeling Heterogeneous Influences Between Arterial Roads	Avinash Achar, Venkatesh Sarangan, Rohith Regikumar, Anand Sivasubramaniam	We propose an efficient algorithm to learn model parameters.
252	Deep-Treat: Learning Optimal Personalized Treatments From Observational Data Using Neural Networks	Onur Atan, James Jordon, Mihaela van der Schaar	We propose a novel approach for constructing effective treatment policies when the observed data is biased and lacks counterfactual information.
253	DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction	Brandon Ballinger, Johnson Hsieh, Avesh Singh, Nimit Sohoni, Jack Wang, Geoffrey H. Tison, Gregory M. Marcus, Jose M. Sanchez, Carol Maguire, Jeffrey E. Olgin, Mark J. Pletcher	We believe our work suggests a new approach to patient risk stratification based on cardiovascular risk scores derived from popular wearables such as Fitbit, Apple Watch, or Android Wear.
254	CSWA: Aggregation-Free Spatial-Temporal Community Sensing	Jiang Bian, Haoyi Xiong, Yanjie Fu, Sajal K. Das	In this paper, we present a novel community sensing paradigm CSWA –Community Sensing Without Sensor/Location Data Aggregation.
255	Multi-Level Variational Autoencoder: Learning Disentangled Representations From Grouped Observations	Diane Bouchacourt, Ryota Tomioka, Sebastian Nowozin	We present the Multi-Level Variational Autoencoder (ML-VAE), a new deep probabilistic model for learning a disentangled representation of grouped data.
256	Dress Fashionably: Learn Fashion Collocation With Deep Mixed-Category Metric Learning	Long Chen, Yuhang He	In this paper, we seek to enable machine to answer questions like, given a clutch bag, what kind of skirt, heel and even accessory best fashionably collocate with it ? To motivate more research in fashion collocation, we collect a dataset of 0.2 million fashionably well-collocated images consisting of either on-body or off-body clothing items or accessories.
257	Modeling Scientific Influence for Research Trending Topic Prediction	Chengyao Chen, Zhitao Wang, Wenjie Li, Xu Sun	To predict the trending topics of mutually influenced conferences, we propose a correlated neural influence model, which has the ability to capture the sequential properties of research evolution in each individual conference and discover the dependencies among different conferences simultaneously.
258	Tap and Shoot Segmentation	Ding-Jie Chen, Jui-Ting Chien, Hwann-Tzong Chen, Long-Wen Chang	We present a new segmentation method that leverages latent photographic information available at the moment of taking pictures.
259	HARP: Hierarchical Representation Learning for Networks	Haochen Chen, Bryan Perozzi, Yifan Hu, Steven Skiena	We present HARP, a novel method for learning low dimensional embeddings of a graph’s nodes which preserves higher-order structural features.
260	Latent Sparse Modeling of Longitudinal Multi-Dimensional Data	Ko-Shin Chen, Tingyang Xu, Jinbo Bi	We propose a tensor-based approach to analyze multi-dimensional data describing sample subjects.
261	Learning Datum-Wise Sampling Frequency for Energy-Efficient Human Activity Recognition	Weihao Cheng, Sarah Erfani, Rui Zhang, Ramamohanarao Kotagiri	In this paper, we formalize the problem as minimizing both classification error and energy cost by choosing dynamically appropriate sampling rates.
262	A Neural Attention Model for Urban Air Quality Inference: Learning the Weights of Monitoring Stations	Weiyu Cheng, Yanyan Shen, Yanmin Zhu, Linpeng Huang	In this paper, we propose a generic neural approach, named ADAIN, for urban air quality inference.
263	Modeling Temporal Tonal Relations in Polyphonic Music Through Deep Networks With a Novel Image-Based Representation	Ching-Hua Chuan, Dorien Herremans	We propose an end-to-end approach for modeling polyphonic music with a novel graphical representation, based on music theory, in a deep neural network.
264	Adversarial Network Embedding	Quanyu Dai, Qiang Li, Jian Tang, Dan Wang	In this paper, we aim to exploit the strengths of generative adversarial networks in capturing latent features, and investigate its contribution in learning stable and robust graph representations.
265	Collaborative Filtering With User-Item Co-Autoregressive Models	Chao Du, Chongxuan Li, Yin Zheng, Jun Zhu, Bo Zhang	We propose CF-UIcA, a neural co-autoregressive model for CF tasks, which exploits the structural correlation in the domains of both users and items.
266	The Shape of Art History in the Eyes of the Machine	Ahmed Elgammal, Bingchen Liu, Diana Kim, Mohamed Elhoseiny, Marian Mazzone	We conducted a comprehensive study of several of the state-of-the-art convolutional neural networks applied to the task of style classification on 67K images of paintings, and analyzed the learned representation through correlation analysis with concepts derived from art history.
267	Multi-Step Time Series Generator for Molecular Dynamics	Katsuhiro Endo, Katsufumi Tomobe, Kenji Yasuoka	In this paper, we propose a multi-step time series generator using a deep neural network based on Wasserstein generative adversarial nets.
268	Search Action Sequence Modeling With Long Short-Term Memory for Search Task Success Evaluation	Alin Fan, Ling Chen, Gencai Chen	In this paper, we employ Long Short-Term Memory (LSTM) that performs end-to-end fine-tuning during the training to learn search action sequence representation for search task success evaluation.
269	Discriminant Projection Representation-Based Classification for Vision Recognition	Qingxiang Feng, Yicong Zhou	In order to obtain the better representation, a novel method called projection representation-based classification (PRC) is proposed for image recognition in this paper.
270	The Geometric Block Model	Sainyam Galhotra, Arya Mazumdar, Soumyabrata Pal, Barna Saha	To capture the inherent geometric features of many community detection problems, we propose to use a new random graph model of communities that we call a Geometric Block Model.
271	Group-Pair Convolutional Neural Networks for Multi-View Based 3D Object Retrieval	Zan Gao, Deyu Wang, Xiangnan He, Hua Zhang	In this work, we address the above limitations for 3D object retrieval by developing a novel end-to-end solution named Group Pair Convolutional Neural Network (GPCNN).
272	Dependence Guided Unsupervised Feature Selection	Jun Guo, Wenwu Zhu	To address this problem, we propose a Dependence Guided Unsupervised Feature Selection (DGUFS) method to select features and partition data in a joint manner.
273	On Trivial Solution and High Correlation Problems in Deep Supervised Hashing	Yuchen Guo, Xin Zhao, Guiguang Ding, Jungong Han	In this paper, we show that the widely used loss functions, pair-wise loss and triplet loss, suffer from the trivial solution problem and usually lead to highly correlated bits in practice, limiting the performance of DSH.
274	Learning User Preferences to Incentivize Exploration in the Sharing Economy	Christoph Hirnschall, Adish Singla, Sebastian Tschiatschek, Andreas Krause	To efficiently learn the optimal incentives to offer, we consider structural information in user preferences and introduce a novel algorithm—Coordinated Online Learning (CoOL)—for learning with structural information modeled as convex constraints.
275	Video-Based Sign Language Recognition Without Temporal Segmentation	Jie Huang, Wengang Zhou, Qilin Zhang, Houqiang Li, Weiping Li	To address these challenges, we propose a novel continuous sign recognition framework, the Hierarchical Attention Network with Latent Space (LS-HAN), which eliminates the preprocessing of temporal segmentation.
276	Energy-Efficient Automatic Train Driving by Learning Driving Patterns	Jin Huang, Yue Gao, Sha Lu, Xibin Zhao, Yangdong Deng, Ming Gu	To tackle the problem, this paper employs a high-order correlation learning method for online generation of the energy optimized train driving solutions.
277	Video-Based Person Re-Identification via Self Paced Weighting	Wenjun Huang, Chao Liang, Yi Yu, Zheng Wang, Weijian Ruan, Ruimin Hu	Based on this finding, we propose a novel video-based person re-id method via self paced weighting (SPW).
278	Generating Music Medleys via Playing Music Puzzle Games	Yu-Siang Huang, Szu-Yu Chou, Yi-Hsuan Yang	Toward this goal, we propose a self-supervised learning task, called the music puzzle game, to train neural network models to learn the sequential patterns in music.
279	Link Prediction With Personalized Social Influence	Zepeng Huo, Xiao Huang, Xia Hu	Given the timestamps of each user, we use entropy to measure the reduction of uncertainty of his/her neighbors.
280	Task-Aware Compressed Sensing With Generative Adversarial Networks	Maya Kabkab, Pouya Samangouei, Rama Chellappa	In this paper, we use Generative Adversarial Networks (GANs) to impose structure in compressed sensing problems, replacing the usual sparsity constraint.
281	Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning	Hao-Cheng Kao, Kai-Fu Tang, Edward Y. Chang	In this paper we present our context-aware hierarchical reinforcement learning scheme, which significantly improves accuracy of symptom checking over traditional systems while also making a limited number of inquiries.
282	DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks	Changhee Lee, William R. Zame, Jinsung Yoon, Mihaela van der Schaar	This paper proposes a very different approach to survival analysis, DeepHit, that uses a deep neural network to learn the distribution of survival times directly.DeepHit makes no assumptions about the underlying stochastic process and allows for the possibility that the relationship between covariates and risk(s) changes over time.
283	DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices	Dawei Li, Xiaolong Wang, Deguang Kong	This motivates us to design a novel acceleration framework: DeepRebirth through “slimming” existing consecutive and parallel non-tensor and tensor layers.
284	Discriminative Semi-Coupled Projective Dictionary Learning for Low-Resolution Person Re-Identification	Kai Li, Zhengming Ding, Sheng Li, Yun Fu	In this paper, we design a novel Discriminative Semi-coupled Projective Dictionary Learning (DSPDL) model to effectively and efficiently solve this problem.
285	Unified Locally Linear Classifiers With Diversity-Promoting Anchor Points	Chenghao Liu, Teng Zhang, Peilin Zhao, Jianling Sun, Steven C. H. Hoi	To address the first issue, we propose a novel diversified regularization which could capture infrequent patterns and reduce the model size without sacrificing the representation power.
286	Multi-Modal Multi-Task Learning for Automatic Dietary Assessment	Qi Liu, Yue Zhang, Zhenguang Liu, Ye Yuan, Li Cheng, Roger Zimmermann	To address this practical yet challenging problem, which is multi-modal and multi-task in nature, an end-to-end neural model is proposed.
287	Distance-Aware DAG Embedding for Proximity Search on Heterogeneous Graphs	Zemin Liu, Vincent W. Zheng, Zhou Zhao, Fanwei Zhu, Kevin Chen-Chuan Chang, Minghui Wu, Jing Ying	In this paper, we explore a more expressive DAG (directed acyclic graph) data structure for modeling the connections between two nodes.
288	Semi-Supervised Biomedical Translation With Cycle Wasserstein Regression GANs	Matthew B. A. McDermott, Tom Yan, Tristan Naumann, Nathan Hunt, Harini Suresh, Peter Szolovits, Marzyeh Ghassemi	In this work, we develop a method to address these issues with semi-supervised learning in regression tasks (e.g., translation from source to target).
289	Probabilistic Ensemble of Collaborative Filters	Zhiyu Min, Dahua Lin	In this paper, we explore an ensemble-based framework to enhance thecapability of a recommender in handling diverse data.
290	A Combinatorial-Bandit Algorithm for the Online Joint Bid/Budget Optimization of Pay-per-Click Advertising Campaigns	Alessandro Nuara, Francesco Trovò, Nicola Gatti, Marcello Restelli	In this paper, we propose, for the first time to the best of our knowledge, an algorithm for the online joint bid/budget optimization of pay-per-click multi-channel advertising campaigns.
291	Hierarchical Video Generation From Orthogonal Information: Optical Flow and Texture	Katsunori Ohnishi, Shohei Yamamoto, Yoshitaka Ushiku, Tatsuya Harada	In this study, we focus on the motion and appearance information as two important orthogonal components of a video, and propose Flow-and-Texture-Generative Adversarial Networks (FTGAN) consisting of FlowGAN and TextureGAN.
292	Sequence-to-Sequence Learning via Shared Latent Representation	Xu Shen, Xinmei Tian, Jun Xing, Yong Rui, Dacheng Tao	In this paper, we propose a star-like framework for general and flexible sequence-to-sequence learning, where different types of media contents (the peripheral nodes) could be encoded to and decoded from a shared latent representation (SLR) (the central node).
293	Compatibility Family Learning for Item Recommendation and Generation	Yong-Siang Shih, Kai-Yueh Chang, Hsuan-Tien Lin, Min Sun	We propose an end-to-end trainable system to embed each item into a latent vector and project a query item into K compatible prototypes in the same space.
294	Neural Ideal Point Estimation Network	Kyungwoo Song, Wonsung Lee, Il-Chul Moon	This paper presents a new model to reflect and understand this political setting, NIPEN, including factors mentioned above in the legislation.
295	Nonlocal Patch Based t-SVD for Image Inpainting: Algorithm and Error Analysis	Liangchen Song, Bo Du, Lefei Zhang, Liangpei Zhang, Jia Wu, Xuelong Li	In this paper, we propose a novel image inpainting framework consisting of an interpolation step and a low-rank tensor completion step.
296	r-BTN: Cross-Domain Face Composite and Synthesis From Limited Facial Patches	Yang Song, Zhifei Zhang, Hairong Qi	Recent face composite and synthesis related works have shown promising results in generating realistic face images from deep convolutional networks.
297	Exercise-Enhanced Sequential Modeling for Student Performance Prediction	Yu Su, Qingwen Liu, Qi Liu, Zhenya Huang, Yu Yin, Enhong Chen, Chris Ding, Si Wei, Guoping Hu	In this paper, we propose a novel Exercise-Enhanced Recurrent Neural Network (EERNN) framework for student performance prediction by taking full advantage of both student exercising records and the text of each exercise.
298	Compressed Sensing MRI Using a Recursive Dilated Network	Liyan Sun, Zhiwen Fan, Yue Huang, Xinghao Ding, John Paisley	We propose a recursive dilated network (RDN) for CS-MRI that achieves good performance while reducing the number of network parameters.
299	Mesh-Based Autoencoders for Localized Deformation Component Analysis	Qingyang Tan, Lin Gao, Yu-Kun Lai, Jie Yang, Shihong Xia	We introduce sparse regularization in this framework, which along with convolutional operations, helps localize deformations.Our framework is capable of extracting localized deformation components from mesh data sets with large-scale deformations and is robust to noise.
300	Maximum-Variance Total Variation Denoising for Interpretable Spatial Smoothing	Wesley Tansey, Jesse Thomason, James G. Scott	To address this problem, we propose Maximum Variance Total Variation (MVTV) denoising, a novel method for interpretable nonlinear spatial regression.
301	Differential Performance Debugging With Discriminant Regression Trees	Saeid Tizpaz-Niari, Pavol Cerny, Bor-Yuh Evan Chang, Ashutosh Trivedi	We propose a data-driven technique based on discriminant regression tree (DRT) learning problem where the goal is to discriminate among different classes of inputs.
302	Adversarial Zero-shot Learning With Semantic Augmentation	Bin Tong, Martin Klinkigt, Junwen Chen, Xiankun Cui, Quan Kong, Tomokazu Murakami, Yoshiyuki Kobayashi	We propose a simple yet effective method for applying the augmented semantics to the hinge loss functions to learn a robust mapping.
303	Model-Free Iterative Temporal Appliance Discovery for Unsupervised Electricity Disaggregation	Mark Valovage, Akshay Shekhawat, Maria Gini	This paper introduces the concept of iterative appliance discovery, a novel unsupervised disaggregation method that progressively identifies the “easiest to find” or “most likely” appliances first.
304	Multimodal Poisson Gamma Belief Network	Chaojie Wang, Bo Chen, Mingyuan Zhou	To learn a deep generative model of multimodal data, we propose a multimodal Poisson gamma belief network (mPGBN) that tightly couple the data of different modalities at multiple hidden layers.
305	When Will You Arrive? Estimating Travel Time Based on Deep Neural Networks	Dong Wang, Junbo Zhang, Wei Cao, Jian Li, Yu Zheng	To address these issues, we propose an end-to-end Deep learning framework for Travel Time Estimation called DeepTTE that estimates the travel time of the whole path directly.
306	GraphGAN: Graph Representation Learning With Generative Adversarial Nets	Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, Minyi Guo	In this paper, we propose GraphGAN, an innovative graph representation learning framework unifying above two classes of methods, in which the generative model and discriminative model play a game-theoretical minimax game.
307	Collaborative Filtering With Social Exposure: A Modular Approach to Social Recommendation	Menghan Wang, Xiaolin Zheng, Yang Yang, Kun Zhang	We propose two methods to implement SERec, namely social regularization and social boosting, each with different ways to construct social exposures.
308	AJILE Movement Prediction: Multimodal Deep Learning for Natural Human Neural Recordings and Video	Nancy X. R. Wang, Ali Farhadi, Rajesh P. N. Rao, Bingni W. Brunton	This paper describes our approach to detect and to predict natural human arm movements in the future, a key challenge in brain computer interfacing that has never before been attempted.
309	Attention-Based Transactional Context Embedding for Next-Item Recommendation	Shoujin Wang, Liang Hu, Longbing Cao, Xiaoshui Huang, Defu Lian, Wei Liu	To this end, we design an effective attention based transaction embedding model (ATEM) for context embedding to weight each observed item in a transaction without assuming order.
310	Fully Convolutional Network Based Skeletonization for Handwritten Chinese Characters	Tie-Qiang Wang, Cheng-Lin Liu	This paper presents an effective fully convolutional network (FCN) to extract stroke skeletons for handwritten Chinese characters.
311	Directional Label Rectification in Adaptive Graph	Xiaoqian Wang, Hao Huang	To address this problem, we present a novel directional label rectification model which identifies the fault-relevant timestamps and features in a simultaneous approach.
312	Hybrid Attentive Answer Selection in CQA With Deep Users Modelling	Jiahui Wen, Jingwei Ma, Yiliu Feng, Mingyang Zhong	In this paper, we propose solutions to advance answer selection in Community Question Answering (CQA).
313	Modeling Attention and Memory for Auditory Selection in a Cocktail Party Environment	Jiaming Xu, Jing Shi, Guangcan Liu, Xiuyi Chen, Bo Xu	In this work, we employ ideas from auditory selective attention of behavioral and cognitive neurosciences and from recent advances of memory-augmented neural networks.
314	Measuring the Popularity of Job Skills in Recruitment Market: A Multi-Criteria Approach	Tong Xu, Hengshu Zhu, Chen Zhu, Pan Li, Hui Xiong	To that end, in this paper, we propose a data driven approach for modeling the popularity of job skills based on the analysis of large-scale recruitment data.
315	Learning Generative Neural Networks for 3D Colorization	Zhenpei Yang, Lihang Liu, Qixing Huang	In this paper, we propose to learn a generative model that maps a latent color parameter space to a space of colorizations across a shape collection.
316	Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction	Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, Zhenhui Li	We propose a Deep Multi-View Spatial-Temporal Network (DMVST-Net) framework to model both spatial and temporal relations.
317	WalkRanker: A Unified Pairwise Ranking Model With Multiple Relations for Item Recommendation	Lu Yu, Chuxu Zhang, Shichao Pei, Guolei Sun, Xiangliang Zhang	In this paper, we aim at incorporating multiple types of user-item relations into a unified pairwise ranking model towards approximately optimizing ranking metrics mean average precision (MAP), and mean reciprocal rank (MRR).
318	Sequence-to-Point Learning With Neural Networks for Non-Intrusive Load Monitoring	Chaoyun Zhang, Mingjun Zhong, Zongzuo Wang, Nigel Goddard, Charles Sutton	In this paper, we propose sequence-to-point learning, where the input is a window of the mains and the output is a single point of the target appliance.
319	Feature Enhancement Network: A Refined Scene Text Detector	Sheng Zhang, Yuliang Liu, Lianwen Jin, Canjie Luo	In this paper, we propose a refined scene text detector with a novel Feature Enhancement Network (FEN)for Region Proposal and Text Detection Refinement.
320	COSINE: Community-Preserving Social Network Embedding From Information Diffusion Cascades	Yuan Zhang, Tianshu Lyu, Yan Zhang	Based on the above observations, we propose a probabilistic generative model, called COSINE, to learn community-preserving social network embeddings from the recurrent and time-stamped social contagion logs, namely information diffusion cascades.
321	Data Poisoning Attacks on Multi-Task Relationship Learning	Mengchen Zhao, Bo An, Yaodong Yu, Sulin Liu, Sinno Jialin Pan	We propose an efficient algorithm called PATOM for computing optimal attack strategies.
322	An Adversarial Hierarchical Hidden Markov Model for Human Pose Modeling and Generation	Rui Zhao, Qiang Ji	We propose a hierarchical extension to hidden Markov model (HMM) under the Bayesian framework to overcome its limited model capacity.
323	Unsupervised Representation Learning With Long-Term Dynamics for Skeleton Based Action Recognition	Nenggan Zheng, Jun Wen, Risheng Liu, Liangqu Long, Jianhua Dai, Zhefeng Gong	In this paper, we explore an unsupervised representation learning approach for the first time to capture the long-term global motion dynamics in skeleton sequences.
324	SFCN-OPI: Detection and Fine-Grained Classification of Nuclei Using Sibling FCN With Objectness Prior Interaction	Yanning Zhou, Qi Dou, Hao Chen, Jing Qin, Pheng-Ann Heng	In this paper, we present a novel method of sibling fully convolutional network with prior objectness interaction (called SFCN-OPI) to tackle the two tasks simultaneously and interactively using a unified end-to-end framework.
325	Parameter-Free Centralized Multi-Task Learning for Characterizing Developmental Sex Differences in Resting State Functional Connectivity	Xiaofeng Zhu, Hongming Li, Yong Fan	In contrast to most existing studies that typically characterize the developmental sex differences using analysis of variance or equivalently multiple linear regression, we present a parameter-free centralized multi-task learning method to identify sex specific and common resting state functional connectivity (RSFC) patterns underlying the brain development based on resting state functional MRI (rs-fMRI) data.
326	Safe Reinforcement Learning via Shielding	Mohammed Alshiekh, Roderick Bloem, Rüdiger Ehlers, Bettina Könighofer, Scott Niekum, Ufuk Topcu	We introduce a new approach to learn optimal policies while enforcing properties expressed in temporal logic.
327	Sample-Efficient Learning of Mixtures	Hassan Ashtiani, Shai Ben-David, Abbas Mehrabian	Let F be an arbitrary class of probability distributions, and let Fk denote the class of k-mixtures of elements of F. Assuming the existence of a method for learning F with sample complexity m(ε), we provide a method for learning Fk with sample complexity O((k.log k .
328	Learning to Attack: Adversarial Transformation Networks	Shumeet Baluja, Ian Fischer	We demonstrate that it is possible, and that the generated attacks yield startling insights into the weaknesses of the target network.
329	Online Learning for Structured Loss Spaces	Siddharth Barman, Aditya Gopalan, Aadirupa Saha	We consider prediction with expert advice when the loss vectors are assumed to lie in a set described by the sum of atomic norm balls.
330	ARC: Adversarial Robust Cuts for Semi-Supervised and Multi-Label Classification	Sima Behpour, Wei Xing, Brian D. Ziebart	We develop Adversarial Robust Cuts (ARC), an approach that poses the learning task as a minimax game between predictor and “label approximator” based on minimum cost graph cuts.
331	Estimating the Class Prior in Positive and Unlabeled Data Through Decision Tree Induction	Jessa Bekker, Jesse Davis	In this paper, we propose a simple yet effective method for estimating the class prior, by estimating the probability that a positive example is selected to be labeled.
332	Long-Term Image Boundary Prediction	Apratim Bhattacharyya, Mateusz Malinowski, Bernt Schiele, Mario Fritz	Long-Term Image Boundary Prediction
333	Algorithms for Generalized Topic Modeling	Avrim Blum, Nika Haghtalab	In this work we consider a broad generalization of the traditional topic modeling framework, where we no longer assume that words are drawn i.i.d. and instead view a topic as a complex distribution over sequences of paragraphs.
334	Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure	Aleksandar Bojchevski, Stephan Günnemann	In this paper, we present a novel probabilistic generative model (PAICAN) that explicitly models partial anomalies by generalizing ideas of Degree Corrected Stochastic Block Models and Bernoulli Mixture Models.
335	Trace Ratio Optimization With Feature Correlation Mining for Multiclass Discriminant Analysis	Forough Rezaei Boroujeni, Sen Wang, Zhihui Li, Nicholas West, Bela Stantic, Lina Yao, Guodong Long	In this work, we study a weighted trace ratio by maximising the harmonic mean of the multiple objective reciprocals.
336	Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning	Daniel S. Brown, Scott Niekum	We propose a sampling method based on Bayesian inverse reinforcement learning that uses demonstrations to determine practical high-confidence upper bounds on the alpha-worst-case difference in expected return between any evaluation policy and the optimal policy under the expert’s unknown reward function.
337	Teaching a Machine to Read Maps With Deep Reinforcement Learning	Gino Brunner, Oliver Richter, Yuyi Wang, Roger Wattenhofer	In this paper we teach a reinforcement learning agent to read a map in order to find the shortest way out of a random maze it has never seen before.
338	Graph Scan Statistics With Uncertainty	Jose Cadena, Arinjoy Basak, Anil Vullikanti, Xinwei Deng	In this paper, we develop the first systematic approach to incorporating uncertainty in scan statistics.
339	Mining Heavy Temporal Subgraphs: Fast Algorithms and Applications	Jose Cadena, Anil Vullikanti	In this paper, we study an approach for identifying anomalous subgraphs based on the Heaviest Dynamic Subgraph (HDS) problem.
340	Efficient Architecture Search by Network Transformation	Han Cai, Tianyao Chen, Weinan Zhang, Yong Yu, Jun Wang	In this paper, we propose a new framework toward efficient architecture search by exploring the architecture space based on the current network and reusing its weights.
341	Unsupervised Domain Adaptation With Distribution Matching Machines	Yue Cao, Mingsheng Long, Jianmin Wang	In this paper, we show that either feature matching or instance reweighting can only reduce, but not remove, the cross-domain discrepancy, and the knowledge hidden in the relations between the data labels from the source and target domains is important for unsupervised domain adaptation.
342	Link Prediction via Subgraph Embedding-Based Convex Matrix Completion	Zhu Cao, Linlin Wang, Gerard de Melo	In this work, we present a new representation learning-based approach called SEMAC that jointly exploits fine-grained node features as well as the overall graph topology.
343	Reversible Architectures for Arbitrarily Deep Residual Neural Networks	Bo Chang, Lili Meng, Eldad Haber, Lars Ruthotto, David Begert, Elliot Holtham	In this work, we interpret deep residual networks as ordinary differential equations (ODEs), which have long been studied in mathematics and physics with rich theoretical and empirical success.
344	Gated-Attention Architectures for Task-Oriented Language Grounding	Devendra Singh Chaplot, Kanthashree Mysore Sathyendra, Rama Kumar Pasumarthi, Dheeraj Rajagopal, Ruslan Salakhutdinov	We propose an end-to-end trainable neural architecture for task-oriented language grounding in 3D environments which assumes no prior linguistic or perceptual knowledge and requires only raw pixels from the environment and the natural language instruction as input.
345	AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training	Chia-Yu Chen, Jungwook Choi, Daniel Brand, Ankur Agrawal, Wei Zhang, Kailash Gopalakrishnan	In this paper we introduce a novel technique – the Adaptive Residual Gradient Compression (AdaComp) scheme.
346	LSTD: A Low-Shot Transfer Detector for Object Detection	Hao Chen, Yali Wang, Guoyou Wang, Yu Qiao	The main contributions are described as follows.
347	Automatic Segmentation of Data Sequences	Liangzhe Chen, Sorour E. Amiri, B. Aditya Prakash	In this paper, we give DASSA, the first self-guided and efficient algorithm to automatically find a segmentation that best detects the change of pattern in data sequences.
348	DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer	Yuntao Chen, Naiyan Wang, Zhaoxiang Zhang	Inspired by their work, we introduce a new type of knowledge—cross sample similarities for model compression and acceleration.
349	Automatic Parameter Tying: A New Approach for Regularized Parameter Learning in Markov Networks	Li Chou, Pracheta Sahoo, Somdeb Sarkhel, Nicholas Ruozzi, Vibhav Gogate	We propose and use a block coordinate ascent algorithm to solve the optimization task.
350	Expected Policy Gradients	Kamil Ciosek, Shimon Whiteson	We propose expected policy gradients (EPG), which unify stochastic policy gradients (SPG) and deterministic policy gradients (DPG) for reinforcement learning.
351	Diverse Exploration for Fast and Safe Policy Improvement	Andrew Cohen, Lei Yu, Robert Wright	As its solution, we propose a novel exploration strategy – diverse exploration (DE), which learns and deploys a diverse set of safe policies to explore the environment.
352	Clustering Small Samples With Quality Guarantees: Adaptivity With One2all PPS	Edith Cohen, Shiri Chechik, Haim Kaplan	For clustering, we present a wrapper that adaptively applies a base clustering algorithm to a sampleS, using the smallest sample that provides the desired statistical guarantees on quality.
353	Distributional Reinforcement Learning With Quantile Regression	Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos	In this paper, we build on recent work advocating a distributional approach to reinforcement learning in which the distribution over returns is modeled explicitly instead of only estimating the mean.
354	Multi-Step Reinforcement Learning: A Unifying Algorithm	Kristopher De Asis, J. Fernando Hernandez-Garcia, G. Zacharias Holland, Richard S. Sutton	In this paper, we study a new multi-step action-value algorithm called Q(σ) that unifies and generalizes these existing algorithms, while subsuming them as special cases.
355	Randomized Kernel Selection With Spectra of Multilevel Circulant Matrices	Lizhong Ding, Shizhong Liao, Yong Liu, Peng Yang, Xin Gao	In this paper, we propose a randomized kernel selection approach to evaluate and select the kernel with the spectra of the specifically designed multilevel circulant matrices (MCMs), which is statistically sound and computationally efficient.
356	Coupled Poisson Factorization Integrated With User/Item Metadata for Modeling Popular and Sparse Ratings in Scalable Recommendation	Trong Dinh Thac Do, Longbing Cao	This work proposes Coupled Poisson Factorization (CPF) to learn the couplings between users (items), and the user/item attributes (i.e., metadata) are integrated into CPF to form the Metadata-integrated CPF (mCPF) to not only handle sparse but also popular ratings in very large-scale data.
357	Learning From Semi-Supervised Weak-Label Data	Hao-Chen Dong, Yu-Feng Li, Zhi-Hua Zhou	In this work we propose the SSWL (Semi-Supervised Weak-Label) method to address this problem.
358	Decomposition Strategies for Constructive Preference Elicitation	Paolo Dragone, Stefano Teso, Mohit Kumar, Andrea Passerini	We propose a decomposition technique for large preference-based decision problems relying exclusively on inference and feedback over partial configurations.
359	Constructive Preference Elicitation Over Hybrid Combinatorial Spaces	Paolo Dragone, Stefano Teso, Andrea Passerini	We propose the Choice Perceptron, a Perceptron-like algorithm for learning user preferences from set-wise choice feedback over constructive domains and hybrid Boolean-numeric feature spaces.
360	Learning to Rank Based on Analogical Reasoning	Mohsen Ahmadi Fahandar, Eyke Hüllermeier	In this paper, we propose a new approach to object ranking based on principles of analogical reasoning.
361	Learning Lexicographic Preference Trees From Positive Examples	Hélène Fargier, Pierre-François Gimenez, Jérôme Mengin	In this paper, we study the particular task of learning conditional lexicographic preferences.
362	AutoEncoder by Forest	Ji Feng, Zhi-Hua Zhou	In this paper, we propose EncoderForest (abbrv.
363	Counterfactual Multi-Agent Policy Gradients	Jakob N. Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, Shimon Whiteson	To this end, we propose a new multi-agent actor-critic method called counterfactual multi-agent (COMA) policy gradients.
364	Lagrangian Constrained Community Detection	Mohadeseh Ganji, James Bailey, Peter J. Stuckey	In this paper, we propose a new constrained community detection algorithm based on Lagrangian multipliers to incorporate and fully satisfy the instance level supervisio nconstraints.
365	DID: Distributed Incremental Block Coordinate Descent for Nonnegative Matrix Factorization	Tianxiang Gao, Chris Chu	We propose a novel distributed algorithm, called distributed incremental block coordinate descent (DID), to solve the problem.
366	Incomplete Label Multi-Task Ordinal Regression for Spatial Event Scale Forecasting	Yuyang Gao, Liang Zhao	Incomplete Label Multi-Task Ordinal Regression for Spatial Event Scale Forecasting
367	Learning Combinatory Categorial Grammars for Plan Recognition	Christopher W. Geib, Pavan Kantharaju	This paper defines a learning algorithm for plan grammars used for plan recognition.
368	Characterization of the Convex Łukasiewicz Fragment for Learning From Constraints	Francesco Giannini, Michelangelo Diligenti, Marco Gori, Marco Maggini	This paper provides a theoretical insight for the integration of logical constraints into a learning process.
369	Topic Modeling on Health Journals With Regularized Variational Inference	Robert Giaquinto, Arindam Banerjee	To overcome this challenge we introduce the Dynamic Author-Persona topic model (DAP), a probabilistic graphical model designed for temporal corpora with multiple authors.
370	Non-Discriminatory Machine Learning Through Convex Fairness Criteria	Naman Goel, Mohammad Yaghini, Boi Faltings	In this paper, we introduce a novel technique to achieve non-discrimination without sacrificing convexity and probabilistic interpretation.
371	Margin Based PU Learning	Tieliang Gong, Guangtao Wang, Jieping Ye, Zongben Xu, Ming Lin	In this work, we show that not all margin-based heuristic rules are able to improve the learned classifiers iteratively.
372	A Continuous Relaxation of Beam Search for End-to-End Training of Neural Sequence Models	Kartik Goyal, Graham Neubig, Chris Dyer, Taylor Berg-Kirkpatrick	In order to train models that can more effectively make use of beam search, we propose a new training procedure that focuses on the final loss metric (e.g. Hamming loss) evaluated on the output of beam search.
373	Human Guided Linear Regression With Feature-Level Constraints	Aubrey Gress, Ian Davidson	In this paper we explore an alternative, non-data centric approach.
374	Learning Predictive State Representations From Non-Uniform Sampling	Yuri Grinberg, Hossein Aboutalebi, Melanie Lyman-Abramovitch, Borja Balle, Doina Precup	In this paper, we examine closely conditional modelling within the PSR framework.
375	Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models	Aditya Grover, Manik Dhar, Stefano Ermon	To bridge this gap, we propose Flow-GANs, a generative adversarial network for which we can perform exact likelihood evaluation, thus supporting both adversarial and maximum likelihood training.
376	Boosted Generative Models	Aditya Grover, Stefano Ermon	We propose a novel approach for using unsupervised boosting to create an ensemble of generative models, where models are trained in sequence to correct earlier mistakes.
377	Asynchronous Doubly Stochastic Sparse Kernel Learning	Bin Gu, Miao Xin, Zhouyuan Huo, Heng Huang	Kernel methods have achieved tremendous success in the past two decades.
378	Inexact Proximal Gradient Methods for Non-Convex and Non-Smooth Optimization	Bin Gu, De Wang, Zhouyuan Huo, Heng Huang	After that, we provide the theoretical analysis to the basic and Nesterov’s accelerated versions.
379	An Euclidean Distance Based on Tensor Product Graph Diffusion Related Attribute Value Embedding for Nominal Data Clustering	Lei Gu, Ningning Zhou, Yang Zhao	This paper mainly aims to make the Euclidean distance measure appropriate to nominal data clustering, and the core idea is the attribute value embedding, namely, transforming each nominal attribute value into a numerical vector.
380	Who Said What: Modeling Individual Labelers Improves Classification	Melody Y. Guan, Varun Gulshan, Andrew M. Dai, Geoffrey E. Hinton	To make use of this extra information, we propose modeling the experts individually and then learning averaging weights for combining them, possibly in sample-specific ways.
381	Nonparametric Stochastic Contextual Bandits	Melody Y. Guan, Heinrich Jiang	We analyze the K-armed bandit problem where the reward for each arm is a noisy realization based on an observed context under mild nonparametric assumptions.We attain tight results for top-arm identification and a sublinear regret ofÕ(T1+D/(2+D), whereD is the context dimension, for a modified UCB algorithm that is simple to implement.
382	A General Formulation for Safely Exploiting Weakly Supervised Data	Lan-Zhe Guo, Yu-Feng Li	In this paper we present a scheme, which builds the final prediction results by integrating several weakly supervised learners.
383	Double Forward Propagation for Memorized Batch Normalization	Yong Guo, Qingyao Wu, Chaorui Deng, Jian Chen, Mingkui Tan	To alleviate this issue, we present a simple Double-Forward scheme in MBN which can further improve the performance.
384	Learning Across Scales—Multiscale Methods for Convolution Neural Networks	Eldad Haber, Lars Ruthotto, Elliot Holtham, Seong-Hwan Jun	In this work, we establish the relation between optimal control and training deep Convolution Neural Networks (CNNs).
385	A Framework for Multistream Regression With Direct Density Ratio Estimation	Ahsanul Haque, Hemeng Tao, Swarup Chandra, Jie Liu, Latifur Khan	In this paper, we study the regression problem over data streams in a novel setting.
386	Approximate and Exact Enumeration of Rule Models	Satoshi Hara, Masakazu Ishihata	In this study, instead of finding a single rule model, we propose algorithms for enumerating multiple rule models.
387	When Waiting Is Not an Option: Learning Options With a Deliberation Cost	Jean Harb, Pierre-Luc Bacon, Martin Klissarov, Doina Precup	We formulate our answer to what good options should be in the bounded rationality framework (Simon, 1957) through the notion of deliberation cost.
388	Learning With Options That Terminate Off-Policy	Anna Harutyunyan, Peter Vrancx, Pierre-Luc Bacon, Doina Precup, Ann Nowé	We propose to resolve this dilemma by decoupling the behavior and target terminations, just like it is done with policies in off-policy learning.
389	Reinforced Multi-Label Image Classification by Exploring Curriculum	Shiyi He, Chang Xu, Tianyu Guo, Chao Xu, Dacheng Tao	Inspired by this curriculum learning mechanism, we propose a reinforced multi-label image classification approach imitating human behavior to label image from easy to complex.
390	An Efficient, Expressive and Local Minima-Free Method for Learning Controlled Dynamical Systems	Ahmed Hefny, Carlton Downey, Geoffrey Gordon	We propose a framework for modeling and estimating the state of controlled dynamical systems, where an agent can affect the system through actions and receives partial observations.
391	OptionGAN: Learning Joint Reward-Policy Options Using Generative Adversarial Inverse Reinforcement Learning	Peter Henderson, Wei-Di Chang, Pierre-Luc Bacon, David Meger, Joelle Pineau, Doina Precup	We therefore extend the options framework and propose a method to simultaneously recover reward options in addition to policy options.
392	Deep Reinforcement Learning That Matters	Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, David Meger	In this paper, we investigate challenges posed by reproducibility, proper experimental techniques, and reporting procedures.
393	Rainbow: Combining Improvements in Deep Reinforcement Learning	Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, David Silver	This paper examines six extensions to the DQN algorithm and empirically studies their combination.
394	Deep Q-learning From Demonstrations	Todd Hester, Matej Vecerik, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, Gabriel Dulac-Arnold, John Agapiou, Joel Z. Leibo, Audrunas Gruslys	In this paper we study a setting where the agent may access data from previous control of the system.
395	Decentralized High-Dimensional Bayesian Optimization With Factor Graphs	Trong Nghia Hoang, Quang Minh Hoang, Ruofei Ouyang, Kian Hsiang Low	This paper presents a novel decentralized high-dimensional Bayesian optimization (DEC-HBO) algorithm that, in contrast to existing HBO algorithms, can exploit the interdependent effects of various input components on the output of the unknown objective function f for boosting the BO performance and still preserve scalability in the number of input dimensions without requiring prior knowledge or the existence of a low (effective) dimension of the input space.
396	A Deep Model With Local Surrogate Loss for General Cost-Sensitive Multi-Label Learning	Cheng-Yu Hsieh, Yi-An Lin, Hsuan-Tien Lin	In this work, we propose a novel cost-sensitive multi-label learning model for any general criteria.
397	From Hashing to CNNs: Training Binary Weight Networks via Hashing	Qinghao Hu, Peisong Wang, Jian Cheng	To achieve this goal, we propose a novel approach named BWNH to train Binary Weight Networks via Hashing.
398	SNNN: Promoting Word Sentiment and Negation in Neural Sentiment Classification	Qinmin Hu, Jie Zhou, Qin Chen, Liang He	We mainly investigate word influence in neural sentiment classification, which results in a novel approach to promoting word sentiment and negation as attentions.
399	On Convergence of Epanechnikov Mean Shift	Kejun Huang, Xiao Fu, Nicholas D. Sidiropoulos	Based on our analysis, we propose a simple remedy to fix it.
400	Orthogonal Weight Normalization: Solution to Optimization Over Multiple Dependent Stiefel Manifolds in Deep Neural Networks	Lei Huang, Xianglong Liu, Bo Lang, Adams Wei Yu, Yongliang Wang, Bo Li	In this paper, we generalize such square orthogonal matrix to orthogonal rectangular matrix and formulating this problem in feed-forward Neural Networks (FNNs) as Optimization over Multiple Dependent Stiefel Manifolds (OMDSM).
401	Building Deep Networks on Grassmann Manifolds	Zhiwu Huang, Jiqing Wu, Luc Van Gool	In order to enable deep learning on Grassmann manifolds, this paper proposes a deep network architecture by generalizing the Euclidean network paradigm to Grassmann manifolds.
402	Accelerated Method for Stochastic Composition Optimization With Nonsmooth Regularization	Zhouyuan Huo, Bin Gu, Ji Liu, Heng Huang	In this paper, we focus on the composition problem with nonsmooth regularization penalty.
403	Product Quantized Translation for Fast Nearest Neighbor Search	Yoonho Hwang, Mooyeol Baek, Saehoon Kim, Bohyung Han, Hee-Kap Ahn	This paper proposes a simple nearest neighbor search algorithm, which provides the exact solution in terms of the Euclidean distance efficiently.
404	Selective Experience Replay for Lifelong Learning	David Isele, Akansel Cosgun	To mitigate forgetting, we propose an experience replay process that augments the standard FIFO buffer and selectively stores experiences in a long-term memory.
405	Label Distribution Learning by Exploiting Label Correlations	Xiuyi Jia, Weiwei Li, Junyu Liu, Yu Zhang	In this paper, we propose a novel label distribution learning algorithm to address the above issue.
406	Metric-Based Auto-Instructor for Learning Mixed Data Representation	Songlei Jian, Liang Hu, Longbing Cao, Kai Lu	To address these issues, we propose an auto-instructive representation learning scheme to enable margin-enhanced distance metric learning for a discrimination-enhanced representation.
407	Efficient Multi-Dimensional Tensor Sparse Coding Using t-Linear Combination	Fei Jiang, Xiao-Yang Liu, Hongtao Lu, Ruimin Shen	In this paper, we propose two novel multi-dimensional tensor sparse coding (MDTSC) schemes using the t-linear combination.
408	PAC Reinforcement Learning With an Imperfect Model	Nan Jiang	In this work we aim at better understanding the theoretical nature of this approach.
409	Asymmetric Deep Supervised Hashing	Qing-Yuan Jiang, Wu-Jun Li	In this paper, we propose a novel deep supervised hashing method, called asymmetric deep supervised hashing (ADSH), for large-scale nearest neighbor search.
410	On Controlling the Size of Clusters in Probabilistic Clustering	Aditya Jitta, Arto Klami	In this work, we present a family of probabilistic clustering models that can be steered towards clusters of desired size by providing a prior distribution over the possible sizes, allowing the analyst to fine-tune exploratory analysis or to produce clusters of suitable size for future down-stream processing.Our formulation supports arbitrary multimodal prior distributions, generalizing the previous work on clustering algorithms searching for clusters of equal size or algorithms designed for the microclustering task of finding small clusters.
411	Less-Forgetful Learning for Domain Expansion in Deep Neural Networks	Heechul Jung, Jeongwoo Ju, Minju Jung, Junmo Kim	In this paper, we propose a less-forgetful learning method for the domain expansion scenario.
412	Unified Spectral Clustering With Optimal Graph	Zhao Kang, Chong Peng, Qiang Cheng, Zenglin Xu	In this work, we transform the candidate solution into a new one that better approximates the discrete one.
413	Batchwise Patching of Classifiers	Sebastian Kauschke, Johannes Fürnkranz	In this work we present classifier patching, an approach for adapting an existing black-box classification model to new data.
414	Deep Semi-Random Features for Nonlinear Function Approximation	Kenji Kawaguchi, Bo Xie, Le Song	We propose semi-random features for nonlinear function approximation.
415	Measuring Catastrophic Forgetting in Neural Networks	Ronald Kemker, Marc McClure, Angelina Abitino, Tyler L. Hayes, Christopher Kanan	In this paper, we introduce new metrics and benchmarks for directly comparing five different mechanisms designed to mitigate catastrophic forgetting in neural networks: regularization, ensembling, rehearsal, dual-memory, and sparse-coding.
416	Approximate Vanishing Ideal via Data Knotting	Hiroshi Kera, Yoshihiko Hasegawa	The present paper proposes a vanishing ideal that is tolerant to noisy data and also pursued to have a better algebraic structure.
417	Feature Engineering for Predictive Modeling Using Reinforcement Learning	Udayan Khurana, Horst Samulowitz, Deepak Turaga	We present a new framework to automate feature engineering.
418	Imitation Learning via Kernel Mean Embedding	Kee-Eung Kim, Hyun Soo Park	We show that the kernelization of a classical algorithm naturally reduces the imitation learning to a distribution learning problem, where the imitation policy tries to match the state-action visitation distribution of the expert.
419	On the Optimal Bit Complexity of Circulant Binary Embedding	Saehoon Kim, Jungtaek Kim, Seungjin Choi	Of particular interest in this paper is circulant binary embedding (CBE) with angle preservation, where a random circulant matrix is used for projection.
420	Joint Dictionaries for Zero-Shot Learning	Soheil Kolouri, Mohammad Rostami, Yuri Owechko, Kyungnam Kim	In this paper, we propose to learn a visual feature dictionary that has semantically meaningful atoms.
421	Dialogue Act Sequence Labeling Using Hierarchical Encoder With CRF	Harshit Kumar, Arvind Agarwal, Riddhiman Dasgupta, Sachindra Joshi	In this work, we build a hierarchical recurrent neural network using bidirectional LSTM as a base unit and the conditional random field (CRF) as the top layer to classify each utterance into its corresponding dialogue act.
422	gOCCF: Graph-Theoretic One-Class Collaborative Filtering Based on Uninteresting Items	Yeon-Chang Lee, Sang-Wook Kim, Dongwon Lee	We investigate how to address the shortcomings of the popular One-Class Collaborative Filtering (OCCF) methods in handling challenging “sparse” dataset in one-class setting (e.g., clicked or bookmarked), and propose a novel graph-theoretic OCCF approach, named as gOCCF, by exploiting both positive preferences (derived from rated items) as well as negative preferences (derived from unrated items).
423	On Value Function Representation of Long Horizon Problems	Lucas Lehnert, Romain Laroche, Harm van Seijen	We show that the generalized Rademacher complexity of the hypothesis space of all optimal value functions is dependent on the planning horizon and independent of the state and action space size.
424	Extremely Low Bit Neural Network: Squeeze the Last Bit Out With ADMM	Cong Leng, Zesheng Dou, Hao Li, Shenghuo Zhu, Rong Jin	In this paper, we focus on compressing and accelerating deep models with network weights represented by very small numbers of bits, referred to as extremely low bit neural network.
425	Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces	Daniel Levy, Stefano Ermon	In this work, we present a new hybrid method based on an approximation of the dynamics as an expectation over the next state under the current policy.
426	Spatio-Temporal Graph Convolution for Skeleton Based Action Recognition	Chaolong Li, Zhen Cui, Wenming Zheng, Chunyan Xu, Jian Yang	In this paper, we propose a spatio-temporal graph convolution (STGC) approach for assembling the successes of local convolutional filtering and sequence learning ability of autoregressive moving average.
427	Learning to Generalize: Meta-Learning for Domain Generalization	Da Li, Yongxin Yang, Yi-Zhe Song, Timothy M. Hospedales	We propose a novel meta-learning method for domain generalization.
428	A Probabilistic Hierarchical Model for Multi-View and Multi-Feature Classification	Jinxing Li, Hongwei Yong, Bob Zhang, Mu Li, Lei Zhang, David Zhang	In this paper, a probabilistic hierarchical model is proposed to address this issue and applied for classification.
429	Predictive Coding Machine for Compressed Sensing and Image Denoising	Jun Li, Hongfu Liu, Yun Fu	In order to avoid the expensive inference, we propose a predictive coding machine (PCM) which aims to train a deep neural network (DNN) encoder to approximate the codes.
430	Unsupervised Personalized Feature Selection	Jundong Li, Liang Wu, Harsh Dani, Huan Liu	Motivated by this, we propose to study a novel problem of personalized feature selection.
431	Latent Discriminant Subspace Representations for Multi-View Outlier Detection	Kai Li, Sheng Li, Zhengming Ding, Weidong Zhang, Yun Fu	In this paper, we propose a novel method capable of detecting outliers from any number of dataviews.
432	Deep Learning for Case-Based Reasoning Through Prototypes: A Neural Network That Explains Its Predictions	Oscar Li, Hao Liu, Chaofan Chen, Cynthia Rudin	In this work, we create a novel network architecture for deep learning that naturally explains its own reasoning for each prediction.
433	Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning	Qimai Li, Zhichao Han, Xiao-ming Wu	In this paper, we develop deeper insights into the GCN model and address its fundamental limits.
434	Adaptive Graph Convolutional Neural Networks	Ruoyu Li, Sheng Wang, Feiyun Zhu, Junzhou Huang	The paper proposes a generalized and flexible graph CNN taking data of arbitrary graph structure as input.
435	Online Clustering of Contextual Cascading Bandits	Shuai Li, Shengyu Zhang	We propose an algorithm of CLUB-cascade for this setting and prove an n-step regret bound of order O(√n).
436	An Optimal Online Method of Selecting Source Policies for Reinforcement Learning	Siyuan Li, Chongjie Zhang	In this paper, we develop an optimal online method to select source policies for reinforcement learning.
437	Statistical Inference Using SGD	Tianyang Li, Liu Liu, Anastasios Kyrillidis, Constantine Caramanis	We present a novel method for frequentist statistical inference in M-estimation problems, based on stochastic gradient descent (SGD) with a fixed step size: we demonstrate that the average of such SGD sequences can be used for statistical inference, after proper scaling.
438	Domain Generalization via Conditional Invariant Representations	Ya Li, Mingming Gong, Xinmei Tian, Tongliang Liu, Dacheng Tao	In this paper, we consider the general situation where both P(X) and P(Y\|X) can change across all domains.
439	Learning With Incomplete Labels	Yingming Li, Zenglin Xu, Zhongfei Zhang	Learning With Incomplete Labels
440	Balanced Clustering via Exclusive Lasso: A Pragmatic Approach	Zhihui Li, Feiping Nie, Xiaojun Chang, Zhigang Ma, Yi Yang	To achieve more accurate clustering for balanced dataset, we propose to leverage exclusive lasso on k-means and min-cut to regulate the balance degree of the clustering results.
441	Robust Formulation for PCA: Avoiding Mean Calculation With L	Shuangli Liao, Jin Li, Yang Liu, Quanxue Gao, Xinbo Gao	Motivated by the fact that the variance of data can be characterized by the variation between each pair of data, we propose a novel robust formulation for PCA.
442	CoDiNMF: Co-Clustering of Directed Graphs via NMF	Woosang Lim, Rundong Du, Haesun Park	In this paper, we propose a new co-clustering method, called CoDiNMF, which improves the clustering quality and finds directional patterns among co-clusters by using multiple directed and undirected graphs.
443	Transferable Contextual Bandit for Cross-Domain Recommendation	Bo Liu, Ying Wei, Yu Zhang, Zhixian Yan, Qiang Yang	To solve the two problems together, in this paper, we propose the first applicable transferable contextual bandit (TCB) policy for the cross-domain recommendation.
444	Riemannian Stein Variational Gradient Descent for Bayesian Inference	Chang Liu, Jun Zhu	We develop Riemannian Stein Variational Gradient Descent (RSVGD), a Bayesian inference method that generalizes Stein Variational Gradient Descent (SVGD) to Riemann manifold.
445	Dual Set Multi-Label Learning	Chong Liu, Peng Zhao, Sheng-Jun Huang, Yuan Jiang, Zhi-Hua Zhou	In this paper, we propose a new learning framework named dual set multi-label learning, where there are two sets of labels, and an object has one and only one positive label in each set.
446	Information Directed Sampling for Stochastic Bandits With Graph Feedback	Fang Liu, Swapna Buccapatnam, Ness Shroff	We consider stochastic multi-armed bandit problems with graph feedback, where the decision maker is allowed to observe the neighboring actions of the chosen action.
447	A Change-Detection Based Framework for Piecewise-Stationary Multi-Armed Bandit Problem	Fang Liu, Joohyun Lee, Ness Shroff	In this paper, we propose a change-detection (CD) based framework for multi-armed bandit problems under the piecewise-stationary setting, and study a class of change-detection based UCB (Upper Confidence Bound) policies, CD-UCB, that actively detects change points and restarts the UCB indices.
448	Nonlinear Pairwise Layer and Its Training for Kernel Learning	Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Li Li	To address this shortcoming, this paper introduces a nonlinear layer in kernel learning to enhance the model flexibility.
449	A Batch Learning Framework for Scalable Personalized Ranking	Kuan Liu, Prem Natarajan	We propose a new framework for personalized ranking.
450	Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-Offs by Selective Execution	Lanlan Liu, Jia Deng	We introduce Dynamic Deep Neural Networks (D2NN), a new type of feed-forward deep neural network that allows selective execution.
451	Doubly Approximate Nearest Neighbor Classification	Weiwei Liu, Zhuanghua Liu, Ivor W. Tsang, Wenjie Zhang, Xuemin Lin	In this paper, we propose a doubly approximate nearest neighbor classification strategy, which marries the two branches which compress the dimensions for decreasing distance computation cost as well as reduce the number of distance comparison instead of full scan.
452	Euler Sparse Representation for Image Classification	Yang Liu, Quanxue Gao, Jungong Han, Shujian Wang	To solve Euler SRC, we present an efficient algorithm, which is fast and has good convergence.
453	Variational Probability Flow for Biologically Plausible Training of Deep Neural Networks	Zuozhu Liu, Tony Q. S. Quek, Shaowei Lin	In this paper, we propose a new algorithm named Variational Probably Flow (VPF), an extension of minimum probability flow for training binary Deep Boltzmann Machines (DBMs).
454	A Parallelizable Acceleration Framework for Packing Linear Programs	Palma London, Shai Vardi, Adam Wierman, Hanling Yi	This paper presents an acceleration framework for packing linear programming problems where the amount of data available is limited, i.e., where the number of constraints m is small compared to the variable dimension n.
455	Nonconvex Sparse Spectral Clustering by Alternating Direction Method of Multipliers and Its Convergence Analysis	Canyi Lu, Jiashi Feng, Zhouchen Lin, Shuicheng Yan	We propose an efficient Alternating Direction Method of Multipliers (ADMM) to solve the nonconvex SSC and provide the convergence guarantee.
456	Matrix Variate Gaussian Mixture Distribution Steered Robust Metric Learning	Lei Luo, Heng Huang	In this paper, we propose a novel robust metric learning method with learning the structure of the distance matrix in a new and natural way.
457	Consistent and Specific Multi-View Subspace Clustering	Shirui Luo, Changqing Zhang, Wei Zhang, Xiaochun Cao	In this paper, we propose a novel multi-view subspace clustering method (CSMSC), where consistency and specificity are jointly exploited for subspace representation learning.
458	Stochastic Non-Convex Ordinal Embedding With Stabilized Barzilai-Borwein Step Size	Ke Ma, Jinshan Zeng, Jiechao Xiong, Qianqian Xu, Xiaochun Cao, Wei Liu, Yuan Yao	To overcome this challenge, we propose a stochastic algorithm called SVRG-SBB, which has the following features: (a) SVD-free via dropping convexity, with good scalability by the use of stochastic algorithm, i.e., stochastic variance reduced gradient (SVRG), and (b) adaptive step size choice via introducing a new stabilized Barzilai-Borwein (SBB) method as the original version for convex problems might fail for the considered stochastic non-convex optimization problem.
459	MDP-Based Cost Sensitive Classification Using Decision Trees	Shlomi Maliah, Guy Shani	In this paper we suggest Markov Decision Processes as a modeling tool for cost sensitive classification.
460	Data-Dependent Learning of Symmetric/Antisymmetric Relations for Knowledge Base Completion	Hitoshi Manabe, Katsuhiko Hayashi, Masashi Shimbo	To mitigate this problem, we propose a new L1 regularizer for Complex Embeddings, which is one of the state-of-the-art embedding-based methods for KBC.
461	Belief Reward Shaping in Reinforcement Learning	Ofir Marom, Benjamin Rosman	We present a novel Bayesian reward shaping framework that augments the reward distribution with prior beliefs that decay with experience.
462	Learning Multi-Way Relations via Tensor Decomposition With Neural Networks	Koji Maruhashi, Masaru Todoriki, Takuya Ohwa, Keisuke Goto, Yu Hasegawa, Hiroya Inakoshi, Hirokazu Anai	We propose a novel method which can learn and classify multi-way data using neural networks.
463	Subgraph Pattern Neural Networks for High-Order Graph Evolution Prediction	Changping Meng, S Chandra Mouli, Bruno Ribeiro, Jennifer Neville	In this work we generalize traditional node/link prediction tasks in dynamic heterogeneous networks, to consider joint prediction over larger k-node induced subgraphs.
464	Exploiting Emotion on Reviews for Recommender Systems	Xuying Meng, Suhang Wang, Huan Liu, Yujun Zhang	In this paper, we provide a principled and mathematical way to exploit both positive and negative emotion on reviews, and propose a novel framework MIRROR, exploiting eMotIon on Reviews for RecOmmendeR systems from both global and local perspectives.
465	Personalized Privacy-Preserving Social Recommendation	Xuying Meng, Suhang Wang, Kai Shu, Jundong Li, Bo Chen, Huan Liu, Yujun Zhang	In this paper, we aim to address the problem of achieving privacy-preserving social recommendation under personalized privacy settings.
466	Proper Loss Functions for Nonlinear Hawkes Processes	Aditya Krishna Menon, Young Lee	We propose a framework to design new loss functions to train linear and nonlinear Hawkes processes.
467	Bernoulli Embeddings for Graphs	Vinith Misra, Sumit Bhatia	We introduce a simple but effective model for learning such binary vectors for nodes in a graph.
468	Core Dependency Networks	Alejandro Molina, Alexander Munteanu, Kristian Kersting	In this paper, we show how to construct coresets—compressed data sets which can be used as proxy for the original data and have provably bounded worst case error—for Gaussian dependency networks (DNs), i.e., cyclic directed graphical models over Gaussians, where the parents of each variable are its Markov blanket.
469	Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains	Alejandro Molina, Antonio Vergari, Nicola Di Mauro, Sriraam Natarajan, Floriana Esposito, Kristian Kersting	To make this difficult task easier, we propose the first trainable probabilistic deep architecture for hybrid domains that features tractable queries.
470	Alternating Circulant Random Features for Semigroup Kernels	Yusuke Mukuta, Yoshitaka Ushiku, Tatsuya Harada	In this paper, we propose novel random features called “alternating circulant random features,” which consist of a random mixture of independent random structured matrices.
471	Overlap-Robust Decision Boundary Learning for Within-Network Classification	Sharad Nandanwar, M. N. Murty	We propose a structural loss function for learning in networks based on the hypothesis that loss is induced when a node fails to acquire a label that is consistent with the labels of the majority of the nodes in its neighborhood.
472	A Provable Approach for Double-Sparse Coding	Thanh V. Nguyen, Raymond K. W. Wong, Chinmay Hegde	In this paper, we consider the double-sparsity model introduced by Rubinstein, Zibulevsky, and Elad (2010) where the dictionary itself is the product of a fixed, known basis and a data-adaptive sparse component.
473	Hierarchical Policy Search via Return-Weighted Density Estimation	Takayuki Osa, Masashi Sugiyama	In this paper, we propose a novel method called hierarchical policy search via return-weighted density estimation (HPSDE), which can efficiently identify the modes through density estimation with return-weighted importance sampling.
474	Dynamic Determinantal Point Processes	Takayuki Osogami, Rudy Raymond, Akshay Goel, Tomoyuki Shirai, Takanori Maehara	Here, we propose a dynamic DPP, which is a DPP whose kernel can change over time, and develop efficient learning algorithms for the dynamic DPP.
475	Gaussian Process Decentralized Data Fusion Meets Transfer Learning in Large-Scale Distributed Cooperative Perception	Ruofei Ouyang, Kian Hsiang Low	This paper presents novel Gaussian process decentralized data fusion algorithms exploiting the notion of agent-centric support sets for distributed cooperative perception of large-scale environmental phenomena.
476	Training CNNs With Normalized Kernels	Mete Ozay, Takayuki Okatani	In this study, we pose estimation of convolution kernels under normalization constraints as constraint-free optimization on kernel submanifolds that are identified by the employed constraints.
477	Sparse Modeling-Based Sequential Ensemble Learning for Effective Outlier Detection in High-Dimensional Numeric Data	Guansong Pang, Longbing Cao, Ling Chen, Defu Lian, Huan Liu	This paper introduces a novel sequential ensemble-based framework SEMSE and its instance CINFO to address this issue.
478	SAGA: A Submodular Greedy Algorithm for Group Recommendation	Shameem A. Puthiya Parambath, Nishant Vijayakumar, Sanjay Chawla	In this paper, we propose a unified framework and an algorithm for the problem of group recommendation where a fixed number of items or alternatives can be recommended to a group of users.
479	Quantized Memory-Augmented Neural Networks	Seongsik Park, Seijoon Kim, Seil Lee, Ho Bae, Sungroh Yoon	In this paper, we identify memory addressing (specifically, content-based addressing) as the main reason for the performance degradation and propose a robust quantization method for MANNs to address the challenge.
480	Adversarial Dropout for Supervised and Semi-Supervised Learning	Sungrae Park, JunKeon Park, Su-Jin Shin, Il-Chul Moon	In contrast to the biased individual inputs to enhance the generality, this paper introduces adversarial dropout, which is a minimal set of dropouts that maximize the divergence between 1) the training supervision and 2) the outputs from the network with the dropouts.
481	Alternating Optimisation and Quadrature for Robust Control	Supratik Paul, Konstantinos Chatzilygeroudis, Kamil Ciosek, Jean-Baptiste Mouret, Michael A. Osborne, Shimon Whiteson	We present Alternating Optimisation and Quadrature (ALOQ), which uses Bayesian optimisation and Bayesian quadrature to address such settings.
482	Multi-Adversarial Domain Adaptation	Zhongyi Pei, Zhangjie Cao, Mingsheng Long, Jianmin Wang	In this paper, we present a multi-adversarial domain adaptation (MADA) approach, which captures multimode structures to enable fine-grained alignment of different data distributions based on multiple domain discriminators.
483	FiLM: Visual Reasoning with a General Conditioning Layer	Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, Aaron Courville	We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation.
484	Source Traces for Temporal Difference Learning	Silviu Pitis	This paper motivates and develops source traces for temporal difference (TD) learning in the tabular setting.
485	Randomized Clustered Nystrom for Large-Scale Kernel Machines	Farhad Pourkamali-Anaraki, Stephen Becker, Michael B. Wakin	In this paper, we introduce a randomized algorithm for generating landmark points that is scalable to large high-dimensional data sets.
486	Joint Learning of Set Cardinality and State Distribution	S. Hamid Rezatofighi, Anton Milan, Qinfeng Shi, Anthony Dick, Ian Reid	We present a novel approach for learning to predict sets using deep learning.
487	Interpretable Graph-Based Semi-Supervised Learning via Flows	Raif M. Rustamov, James T. Klosowski	In this paper, we consider the interpretability of the foundational Laplacian-based semi-supervised learning approaches on graphs.
488	Hypergraph p-Laplacian: A Differential Geometry View	Shota Saito, Danilo P. Mandic, Hideyuki Suzuki	In this paper, we generalize the analogy between graph Laplacian and differential geometry to the hypergraph setting, and propose a novel hypergraph p-Laplacian.
489	Word Co-Occurrence Regularized Non-Negative Matrix Tri-Factorization for Text Data Co-Clustering	Aghiles Salah, Melissa Ailem, Mohamed Nadif	To infer the factor matrices, we derive a scalable alternating optimization algorithm, whose convergence is guaranteed.
490	Learning Vector Autoregressive Models With Latent Processes	Saber Salehkaleybar, Jalal Etesami, Negar Kiyavash, Kun Zhang	We study the problem of learning the support of transition matrix between random processes in a Vector Autoregressive (VAR) model from samples when a subset of the processes are latent.
491	Regularizing Deep Networks Using Efficient Layerwise Adversarial Training	Swami Sankaranarayanan, Arpit Jain, Rama Chellappa, Ser Nam Lim	In this paper, we present a novel approach to regularize deep neural networks by perturbing intermediate layer activations in an efficient manner.
492	From Monte Carlo to Las Vegas: Improving Restricted Boltzmann Machine Training Through Stopping Sets	Pedro H. P. Savarese, Mayank Kakodkar, Bruno Ribeiro	We propose a Las Vegas transformation of Markov Chain Monte Carlo (MCMC) estimators of Restricted Boltzmann Machines (RBMs).
493	On Data-Dependent Random Features for Improved Generalization in Supervised Learning	Shahin Shahrampour, Ahmad Beirami, Vahid Tarokh	In this paper, we are concerned with the randomized-feature approach in supervised learning for good generalizability.
494	Labeled Memory Networks for Online Model Adaptation	Shiv Shankar, Sunita Sarawagi	In this paper, we establish their potential in online adapting a batch trained neural network to domain-relevant labeled data at deployment time.
495	No Modes Left Behind: Capturing the Data Distribution Effectively Using GANs	Shashank Sharma, Vinay P. Namboodiri	In this paper, we propose a simple approach that combines an encoder based objective with novel loss functions for generator and discriminator that improves the solution in terms of capturing missing modes.
496	Reduced-Rank Linear Dynamical Systems	Qi She, Yuan Gao, Kai Xu, Rosa H. M. Chan	We propose Reduced-Rank Linear Dynamical Systems (RRLDS), to automatically retrieve the intrinsic dimensionality of the latent space during model learning.
497	Wasserstein Distance Guided Representation Learning for Domain Adaptation	Jian Shen, Yanru Qu, Weinan Zhang, Yong Yu	Inspired by Wasserstein GAN, in this paper we propose a novel approach to learn domain invariant feature representations, namely Wasserstein Distance Guided Representation Learning (WDGRL).
498	Compact Multi-Label Learning	Xiaobo Shen, Weiwei Liu, Ivor W. Tsang, Quan-Sen Sun, Yew-Soon Ong	To address the above issues, this paper proposes a Co-Hashing (CoH) method by formulating multi-label learning from the perspective of cross-view learning.
499	Dynamic Optimization of Neural Network Structures Using Probabilistic Modeling	Shinichi Shirakawa, Yasushi Iwata, Youhei Akimoto	In this paper, we propose a method to simultaneously optimize the network structure and weight parameters during neural network training.
500	Learning to Interact With Learning Agents	Adish Singla, Hamed Hassani, Andreas Krause	Motivated by the application of learning to offer personalized deals to users, we highlight these challenges by studying a variant of the framework of “online learning using expert advice with bandit feedback.”
501	Attend and Diagnose: Clinical Time Series Analysis Using Attention Models	Huan Song, Deepta Rajan, Jayaraman J. Thiagarajan, Andreas Spanias	In this paper, for the first time, we utilize attention models for clinical time-series modeling, thereby dispensing recurrence entirely.
502	Reinforcement Learning in POMDPs With Memoryless Options and Option-Observation Initiation Sets	Denis Steckelmacher, Diederik M. Roijers, Anna Harutyunyan, Peter Vrancx, Hélène Plisnier, Ann Nowé	More specifically, we make the initiation set of options conditional on the previously-executed option, and show that options with such Option-Observation Initiation Sets (OOIs) are at least as expressive as Finite State Controllers (FSCs), a state-of-the-art approach for learning in POMDPs.
503	Active Lifelong Learning With “Watchdog”	Gan Sun, Yang Cong, Xiaowei Xu	Therefore, in this paper, we try to mimic an effective “human cognition” strategy by actively sorting the importance of new tasks in the process of unknown-to-known and selecting to learn the important tasks with more information preferentially.
504	Leaf-Smoothed Hierarchical Softmax for Ordinal Prediction	Wesley Tansey, Karl Pichotta, James G. Scott	We propose a new approach to conditional probability estimation for ordinal labels.
505	Reliable Multi-View Clustering	Hong Tao, Chenping Hou, Xinwang Liu, Tongliang Liu, Dongyun Yi, Jubo Zhu	In this paper, we focus on clustering and propose the Reliable Multi-View Clustering (RMVC) method.
506	Action Branching Architectures for Deep Reinforcement Learning	Arash Tavakoli, Fabio Pardo, Petar Kormushev	In this paper, we propose a novel neural architecture featuring a shared decision module followed by several network branches, one for each action dimension.
507	Detecting Adversarial Examples Through Image Transformation	Shixin Tian, Guolei Yang, Ying Cai	In this paper, we propose an effective method to detect adversarial examples in image classification.
508	Selective Verification Strategy for Learning From Crowds	Tian Tian, Yichi Zhou, Jun Zhu	In this paper, we explore the learning from crowds with selective verification problem.
509	Fourier Feature Approximations for Periodic Kernels in Time-Series Modelling	Anthony Tompkins, Fabio Ramos	In this paper we introduce an alternative method using Fourier series to obtain spectral representations of common kernels, in particular for periodic warpings, which surprisingly have a convergent, non-random form using special functions, requiring fewer spectral features to approximate their corresponding kernel to high accuracy.
510	Sum-Product Autoencoding: Encoding and Decoding Representations Using Sum-Product Networks	Antonio Vergari, Robert Peharz, Nicola Di Mauro, Alejandro Molina, Kristian Kersting, Floriana Esposito	We characterize when this Sum-Product Autoencoding (SPAE) leads to equivalent reconstructions and extend it towards dealing with missing embedding information.
511	Bayesian Functional Optimization	Ngo Anh Vien, Heiko Zimmermann, Marc Toussaint	This paper proposes a new Bayesian optimization framework that is able to optimize directly on the domain of function spaces.
512	Kernel Cross-Correlator	Chen Wang, Le Zhang, Lihua Xie, Junsong Yuan	Beyond the linear cross-correlator, this paper proposes a kernel cross-correlator (KCC) that breaks traditional limitations.
513	Efficient Test-Time Predictor Learning With Group-Based Budget	Li Wang, Dajiang Zhu, Yujie Chi	In this paper, we propose a novel approach to learn a linear predictor by introducing binary indicator variables for selecting feature groups and imposing an explicit budget constraint to up-bound the total cost of selected groups.
514	Learning Transferable Subspace for Human Motion Segmentation	Lichen Wang, Zhengming Ding, Yun Fu	To this end, we propose a novel transferable subspace clustering approach by exploring useful information from relevant source data to enhance clustering performance in target temporal data.
515	Information-Theoretic Domain Adaptation Under Severe Noise Conditions	Wei Wang, Hao Wang, Zhi-Yong Ran, Ran He	To enhance the robustness of domain adaptation under severe noise conditions, this paper proposes a novel reconstruction based algorithm in an information-theoretic setting.
516	Zero-Shot Learning via Class-Conditioned Deep Generative Models	Wenlin Wang, Yunchen Pu, Vinay Kumar Verma, Kai Fan, Yizhe Zhang, Changyou Chen, Piyush Rai, Lawrence Carin	We present a deep generative model for Zero-Shot Learning (ZSL).
517	Sparse Gaussian Conditional Random Fields on Top of Recurrent Neural Networks	Xishun Wang, Minjie Zhang, Fenghui Ren	We propose CoR, Sparse Gaussian Conditional Random Fields (SGCRF) on top of Recurrent Neural Networks (RNN), for problems of this kind.
518	On Multi-Relational Link Prediction With Bilinear Models	Yanjie Wang, Rainer Gemulla, Hui Li	The main goal of this paper is to explore the expressiveness of and the connections between various bilinear models proposed in the literature.
519	Towards Ultra-High Performance and Energy Efficiency of Deep Learning Systems: An Algorithm-Hardware Co-Optimization Framework	Yanzhi Wang, Caiwen Ding, Zhe Li, Geng Yuan, Siyu Liao, Xiaolong Ma, Bo Yuan, Xuehai Qian, Jian Tang, Qinru Qiu, Xue Lin	The aim of this paper is to achieve ultra-high energy efficiency and performance for hardware implementations of deep neural networks (DNNs).
520	On the ERM Principle With Networked Data	Yuanhong Wang, Yuyi Wang, Xingwu Liu, Juhua Pu	In this case, neither the classical i.i.d. assumption nor techniques based on complete U-statistics can be used.
521	High Rank Matrix Completion With Side Information	Yugang Wang, Ehsan Elhamifar	As our proposed optimization, searching for missing entries and sparse coefficients, is non-convex and NP-hard, we propose a lifting framework, where we couple sparse coefficients and missing values and define an equivalent optimization that is amenable to convex relaxation.
522	Adversarial Learning of Portable Student Networks	Yunhe Wang, Chang Xu, Chao Xu, Dacheng Tao	To overcome this challenge, we utilize the generative adversarial network (GAN) to learn the student network.
523	Orthant-Wise Passive Descent Algorithms for Training L	Jianqiao Wangni	In this paper, we propose the orthant-wise passive descent algorithm (OPDA) for solving L1-regularized models, as an improved substitute of proximal algorithms, which are the standard tools for optimizing the models nowadays.
524	MERCS: Multi-Directional Ensembles of Regression and Classification Trees	Elia Van Wolputte, Evgeniya Korneva, Hendrik Blockeel	In this paper, we explore the possibility of omitting the specification of X and Y at training time altogether, by learning a multi-directional, or versatile model, which will allow prediction of any Y from any X. Specifically, we introduce a decision tree-based paradigm that generalizes the well-known Random Forests approach to allow for multi-directionality.
525	Decoupled Convolutions for CNNs	Guotian Xie, Ting Zhang, Kuiyuan Yang, Jianhuang Lai, Jingdong Wang	In this paper, we are interested in designing small CNNs by decoupling the convolution along the spatial and channel domains.
526	Cooperative Learning of Energy-Based Model and Latent Variable Model via MCMC Teaching	Jianwen Xie, Yang Lu, Ruiqi Gao, Ying Nian Wu	This paper proposes a cooperative learning algorithm to train both the undirected energy-based model and the directed latent variable model jointly.
527	Partial Multi-Label Learning	Ming-Kun Xie, Sheng-Jun Huang	In this paper, we formalize such problems as a new learning framework called partial multi-label learning (PML).
528	Semi-Supervised AUC Optimization Without Guessing Labels of Unlabeled Data	Zheng Xie, Ming Li	In this paper, we argue that, in semi-supervised AUC optimization, it is unnecessary to guess the possible labels of the unlabeled data or prior probability based on any distributional assumptions.
529	Perception Coordination Network: A Framework for Online Multi-Modal Concept Acquisition and Binding	You-Lu Xing, Fu-Rao Shen, Jin-Xi Zhao, Jing-Xin Pan, Ah-Hwee Tan	A biologically plausible neural network model named Perception Coordination Network (PCN) is proposed for online multi-modal concept acquisition and binding.
530	HodgeRank With Information Maximization for Crowdsourced Pairwise Ranking Aggregation	Qianqian Xu, Jiechao Xiong, Xi Chen, Qingming Huang, Yuan Yao	In this paper, we study the principle of information maximization for active sampling strategies in the framework of HodgeRank, an approach based on Hodge Decomposition of pairwise ranking data with multiple workers.
531	Deep Neural Network Compression With Single and Multiple Level Quantization	Yuhui Xu, Yongzhuang Wang, Aojun Zhou, Weiyao Lin, Hongkai Xiong	In this paper, we propose two novel network quantization approaches, single-level network quantization (SLQ) for high-bit quantization and multi-level network quantization (MLQ) for extremely low-bit quantization (ternary).
532	Informed Non-Convex Robust Principal Component Analysis With Features	Niannan Xue, Jiankang Deng, Yannis Panagakis, Stefanos Zafeiriou	To this aim, a novel, elegant, non-convex optimization approach is proposed to decompose a given observation matrix into a low-rank core and the corresponding sparse residual.
533	Dictionary Learning in Optimal Metric Space	Jiexi Yan, Cheng Deng, Xianglong Liu	To tackle it, we propose a new unified unsupervised model which naturally integrates metric learning to enhance dictionary learning model with fully utilizing the side information.
534	Automatic Model Selection in Subspace Clustering via Triplet Relationships	Jufeng Yang, Jie Liang, Kai Wang, Yong-Liang Yang, Ming-Ming Cheng	In this paper, we propose to simultaneously estimate K and segment the samples according to the local similarity relationships derived from the affinity matrix.
535	A Poisson Gamma Probabilistic Model for Latent Node-Group Memberships in Dynamic Networks	Sikun Yang, Heinz Koeppl	We present a probabilistic model for learning from dynamic relational data, wherein the observed interactions among networked nodes are modeled via the Bernoulli Poisson link function, and the underlying network structure are characterized by nonnegative latent node-group memberships, which are assumed to be gamma distributed.
536	New l	Xu Yang, Cheng Deng, Xianglong Liu, Feiping Nie	In this paper, we propose a new relaxed multi-way graph cut clustering method, where l2,1-norm distance instead of squared distance is utilized to preserve the solution having much more clearer cluster structures.
537	Efficient K-Shot Learning With Regularized Deep Networks	Donghyun Yoo, Haoqi Fan, Vishnu Naresh Boddeti, Kris M. Kitani	To address these problems, we propose a simple yet effective regularization method for fine-tuning pre-trained deep networks for the task of k-shot learning.
538	Learning With Single-Teacher Multi-Student	Shan You, Chang Xu, Chao Xu, Dacheng Tao	In this paper we study a new learning problem defined as “Single-Teacher Multi-Student” (STMS) problem, which investigates how to learn a series of student (simple and specific) models from a single teacher (complex and universal) model.
539	Tau-FPL: Tolerance-Constrained Learning in Linear Time	Ao Zhang, Nan Li, Jian Pu, Jun Wang, Junchi Yan, Hongyuan Zha	In this paper, we propose a novel scoring-thresholding approach, tau-False Positive Learning (tau-FPL) to address this problem.
540	Multi-Layer Multi-View Classification for Alzheimer’s Disease Diagnosis	Changqing Zhang, Ehsan Adeli, Tao Zhou, Xiaobo Chen, Dinggang Shen	In this paper, we propose a novel multi-view learning method for Alzheimer’s Disease (AD) diagnosis, using neuroimaging and genetics data.
541	Latent Semantic Aware Multi-View Multi-Label Classification	Changqing Zhang, Ziwei Yu, Qinghua Hu, Pengfei Zhu, Xinwang Liu, Xiaobo Wang	To address this issue, we propose a novel approach for multi-view multi-label learning based on matrix factorization to exploit complementarity among different views.
542	ROAR: Robust Label Ranking for Social Emotion Mining	Jason (Jiasheng) Zhang, Dongwon Lee	In this paper, we fill this gap by formulating social emotion mining as a robust label ranking problem, and propose: (1) a robust measure, named as G-mean-rank (GMR), which sets a formal criterion consistent with practical intuition; and (2) a simple yet effective label ranking model, named as ROAR, that is more robust toward unbalanced datasets (which are common).
543	Beyond Link Prediction: Predicting Hyperlinks in Adjacency Space	Muhan Zhang, Zhicheng Cui, Shali Jiang, Yixin Chen	In this paper, we formally define the hyperlink prediction problem, and propose a new algorithm called Coordinated Matrix Minimization (CMM), which alternately performs nonnegative matrix factorization and least square matching in the vertex adjacency space of the hypernetwork, in order to infer a subset of candidate hyperlinks that are most suitable to fill the training hypernetwork.
544	An End-to-End Deep Learning Architecture for Graph Classification	Muhan Zhang, Zhicheng Cui, Marion Neumann, Yixin Chen	In this paper, we propose a novel neural network architecture accepting graphs of arbitrary structure.
545	Feature-Induced Labeling Information Enrichment for Multi-Label Learning	Qian-Wen Zhang, Yun Zhong, Min-Ling Zhang	In this paper, a novel multi-label learning approach is proposed which aims to enrich the labeling information by leveraging the structural information in feature space.
546	Interpreting CNN Knowledge via an Explanatory Graph	Quanshi Zhang, Ruiming Cao, Feng Shi, Ying Nian Wu, Song-Chun Zhu	Considering that each filter in a conv-layer of a pre-trained CNN usually represents a mixture of object parts, we propose a simple yet efficient method to automatically disentangles different part patterns from each filter, and construct an explanatory graph.
547	Examining CNN Representations With Respect to Dataset Bias	Quanshi Zhang, Wenguan Wang, Song-Chun Zhu	Given a pre-trained CNN without any testing samples, this paper proposes a simple yet effective method to diagnose feature representations of the CNN.
548	Optimal Margin Distribution Clustering	Teng Zhang, Zhi-Hua Zhou	In this paper, we propose a novel approach ODMC (Optimal margin Distribution Machine for Clustering), which tries to cluster the data and achieve optimal margin distribution simultaneously.
549	Training Set Debugging Using Trusted Items	Xuezhou Zhang, Xiaojin Zhu, Stephen Wright	The set of trusted items may not by itself be adequate for learning, so we propose an algorithm that uses these items to identify bugs in the training set and thus improves learning.
550	EMD Metric Learning	Zizhao Zhang, Yubo Zhang, Xibin Zhao, Yue Gao	To tackle this issue, we propose an EMD metric learning algorithm in this paper.
551	Distant-Supervision of Heterogeneous Multitask Learning for Social Event Forecasting With Multilingual Indicators	Liang Zhao, Junxiang Wang, Xiaojie Guo	In order to simultaneously address these issues, we present a novel model capable of distant-supervision of heterogeneous multitask learning (DHML) for multilingual spatial social event forecasting.
552	Label Distribution Learning by Optimal Transport	Peng Zhao, Zhi-Hua Zhou	In this paper, we proposed an approach to learn the label distribution and exploit label correlations simultaneously based on the Optimal Transport (OT) theory.
553	Substructure Assembling Network for Graph Classification	Xiaohan Zhao, Bo Zong, Ziyu Guan, Kai Zhang, Wei Zhao	In this work, we propose a novel neural network structure called Substructure Assembling Network (SAN) to extract graph features and improve the generalization performance of graph classification.
554	Hypergraph Learning With Cost Interval Optimization	Xibin Zhao, Nan Wang, Heyuan Shi, Hai Wan, Jin Huang, Yue Gao	To tackle these issues, in this paper, we propose a hypergraph learning method with cost interval optimization, which is able to handle cost interval when data is formulated using the high-order relationships.
555	Learning Mixtures of Random Utility Models	Zhibing Zhao, Tristan Villamil, Lirong Xia	We tackle the problem of identifiability and efficient learning of mixtures of Random Utility Models (RUMs).
556	Direct Hashing Without Pseudo-Labels	Feng Zheng, Heng Huang	In this paper, we propose a novel general framework to simultaneously minimize the measurement distortion and the quantization loss, which enable to learn hash functions directly without requiring the pseudo-labels.
557	Learning Graph-Structured Sum-Product Networks for Probabilistic Semantic Maps	Kaiyu Zheng, Andrzej Pronobis, Rajesh P. N. Rao	We introduce Graph-Structured Sum-Product Networks (GraphSPNs), a probabilistic approach to structured prediction for problems where dependencies between latent variables are expressed in terms of arbitrary, dynamic graphs.
558	Label Distribution Learning by Exploiting Sample Correlations Locally	Xiang Zheng, Xiuyi Jia, Weiwei Li	In this paper, we propose a new label distribution learning algorithm by exploiting sample correlations locally (LDL-SCL).
559	ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation	Chang Zhou, Jinze Bai, Junshuai Song, Xiaofei Liu, Zhengchao Zhao, Xiusi Chen, Jun Gao	This paper proposes an attention based user behavior modeling framework called ATRank, which we mainly use for recommendation tasks.
560	Budget-Constrained Multi-Armed Bandits With Multiple Plays	Datong P. Zhou, Claire J. Tomlin	We study the multi-armed bandit problem with multiple plays and a budget constraint for both the stochastic and the adversarial setting.
561	Rocket Launching: A Universal and Efficient Framework for Training Well-Performing Light Net	Guorui Zhou, Ying Fan, Runpeng Cui, Weijie Bian, Xiaoqiang Zhu, Kun Gai	In order to get neural networks of better performance given the time limitations, we propose a universal framework that exploits a booster net to help train the lightweight net for prediction.
562	SC2Net: Sparse LSTMs for Sparse Coding	Joey Tianyi Zhou, Kai Di, Jiawei Du, Xi Peng, Hao Yang, Sinno Jialin Pan, Ivor W. Tsang, Yong Liu, Zheng Qin, Rick Siow Mong Goh	To address these challenging issues, we propose a novel formulation of ISTA (named as adaptive ISTA) by introducing a novel \textit{adaptive momentum vector}.
563	Adaptive Quantization for Deep Neural Network	Yiren Zhou, Seyed-Mohsen Moosavi-Dezfooli, Ngai-Man Cheung, Pascal Frossard	In this work, we propose an optimization framework for deep model quantization.
564	Non-Parametric Outliers Detection in Multiple Time Series A Case Study: Power Grid Data Analysis	Yuxun Zhou, Han Zou, Reza Arghandeh, Weixi Gu, Costas J. Spanos	In this study we consider the problem of outlier detection with multiple co-evolving time series data.
565	A Spherical Hidden Markov Model for Semantics-Rich Human Mobility Modeling	Wanzheng Zhu, Chao Zhang, Shuochao Yao, Xiaobin Gao, Jiawei Han	We propose SHMM, a multi-modal spherical hidden Markov model for semantics-rich human mobility modeling.
566	Weighted Multi-View Spectral Clustering Based on Spectral Perturbation	Linlin Zong, Xianchao Zhang, Xinyue Liu, Hong Yu	In this paper, based on the spectral perturbation theory of spectral clustering, we propose a weighted multi-view spectral clustering algorithm which employs the spectral perturbation to model the weights of the views.
567	Learning the Behavior of a Dynamical System Via a “20 Questions” Approach	Abhijin Adiga, Chris J. Kuhlman, Madhav V. Marathe, Ravi S. S., Daniel J. Rosenkrantz, Richard E. Stearns	We investigate the problem assuming an active form of interaction with the system through queries.
568	Knowledge, Fairness, and Social Constraints	Haris Aziz, Sylvain Bouveret, Ioannis Caragiannis, Ira Giagkousi, Jérôme Lang	We argue that these notions could also be defined relative to the knowledge that an agent has on how the items that she does not receive are distributed among other agents.
569	POMDP-Based Decision Making for Fast Event Handling in VANETs	Shuo Chen, Athirai A. Irissappane, Jie Zhang	We propose a Partially Observable Markov Decision Process (POMDP) based approach to balance the trade-off between information gathering and exploiting actions resulting in faster responses.
570	An Ant-Based Algorithm to Solve Distributed Constraint Optimization Problems	Ziyu Chen, Tengfei Wu, Yanchen Deng, Cheng Zhang	In this paper, we present a novel algorithm that takes the power of ants to solve Distributed Constraint Optimization Problems (DCOPs), called ACO_DCOP.
571	Preallocation and Planning Under Stochastic Resource Constraints	Frits de Nijs, Matthijs T. J. Spaan, Mathijs M. de Weerdt	To address these limitations, we propose to extend algorithms for constrained multi-agent planning problems to handle stochastic resource constraints.
572	Manipulative Elicitation — A New Attack on Elections with Incomplete Preferences	Palash Dey	Hence, in this work, we discover a fundamental vulnerability in using minimax regret based approach in partial preferential setting and propose a novel way to tackle it.
573	Control Argumentation Frameworks	Yannis Dimopoulos, Jean-Guy Mailly, Pavlos Moraitis	This work proposes Control Argumentation Frameworks (CAFs), a new approach that generalizes existing techniques, namely normal extension enforcement, by accommodating the possibility of uncertainty in dynamic scenarios.
574	Decentralised Learning in Systems With Many, Many Strategic Agents	David Mguni, Joel Jennings, Enrique Munoz de Cote	In this paper, we propose a method for computing closed-loop optimal policies in multi-agent systems that scales independently of the number of agents.
575	Dilated FCN for Multi-Agent 2D/3D Medical Image Registration	Shun Miao, Sebastien Piat, Peter Fischer, Ahmet Tuysuzoglu, Philip Mewes, Tommaso Mansi, Rui Liao	In this paper, we propose a multi-agent system with an auto attention mechanism for robust and efficient 2D/3D image registration.
576	Strategic Coalitions With Perfect Recall	Pavel Naumov, Jia Tao	The paper proposes a bimodal logic that describes an interplay between distributed knowledge modality and coalition know-how modality.
577	The Role of Data-Driven Priors in Multi-Agent Crowd Trajectory Estimation	Gang Qiao, Sejong Yoon, Mubbasir Kapadia, Vladimir Pavlovic	To address these limitations, we propose to extend algorithms for constrained multi-agent planning problems to handle stochastic resource constraints.
578	Dynamic Pricing for Reusable Resources in Competitive Market With Stochastic Demand	Jiang Rong, Tao Qin, Bo An	This paper provides the first study of service providers’ dynamic pricing in consideration of market competition and makes three key contributions along this line.
579	Social Norms of Cooperation With Costly Reputation Building	Fernando P. Santos, Jorge M. Pacheco, Francisco C. Santos	Here we develop a new model of indirect reciprocity that allows reputation building to be costly.
580	Multiagent Connected Path Planning: PSPACE-Completeness and How to Deal With It	Davide Tateo, Jacopo Banfi, Alessandro Riva, Francesco Amigoni, Andrea Bonarini	In this paper, we show that, in fact, even deciding whether a feasible plan exists is a PSPACE-complete problem.
581	Maximizing Influence in an Unknown Social Network	Bryan Wilder, Nicole Immorlica, Eric Rice, Milind Tambe	We present the ARISEN algorithm, which leverages community structure to find an influential seed set.
582	Integrated Cooperation and Competition in Multi-Agent Decision-Making	Kyle Hollins Wray, Akshat Kumar, Shlomo Zilberstein	Observing that many real-world sequential decision problems are not purely cooperative or purely competitive, we propose a new model—cooperative-competitive process (CCP)—that can simultaneously encapsulate both cooperation and competition.
583	Privacy-Preserving Policy Iteration for Decentralized POMDPs	Feng Wu, Shlomo Zilberstein, Xiaoping Chen	We propose the first privacy-preserving approach to address the privacy issues that arise in multi-agent planning problems modeled as a Dec-POMDP.
584	HogRider: Champion Agent of Microsoft Malmo Collaborative AI Challenge	Yanhai Xiong, Haipeng Chen, Mengchen Zhao, Bo An	We present HogRider—the champion agent of MCAC in 2017 out of 81 teams from 26 countries.
585	Effective Broad-Coverage Deep Parsing	James F. Allen, Omid Bahkshandeh, William de Beaumont, Lucian Galescu, Choh Man Teng	We describe a system that provides broad-coverage, deep semantic parsing designed to work in any domain using a core domain-general lexicon, ontology and grammar.
586	Faithful to the Original: Fact Aware Neural Abstractive Summarization	Ziqiang Cao, Furu Wei, Wenjie Li, Sujian Li	To avoid generating fake facts in a summary, we leverage open information extraction and dependency parse technologies to extract actual fact descriptions from the source text.
587	Syntax-Directed Attention for Neural Machine Translation	Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao	In this paper, we extend the local attention with syntax-distance constraint, which focuses on syntactically related source words with the predicted target word to learning a more effective context vector for predicting translation.
588	A Semantic QA-Based Approach for Text Summarization Evaluation	Ping Chen, Fei Wu, Tong Wang, Wei Ding	In this paper, we will present some preliminary results on one especially useful and challenging problem in NLP system evaluation—how to pinpoint content differences of two text passages (especially for large passages such as articles and books).
589	Learning Sentiment-Specific Word Embedding via Global Sentiment Representation	Peng Fu, Zheng Lin, Fengcheng Yuan, Weiping Wang, Dan Meng	To address this issue, we present a sentiment-specific word embedding learning architecture that utilizes local context informationas well as global sentiment representation.
590	Knowledge Graph Embedding With Iterative Guidance From Soft Rules	Shu Guo, Quan Wang, Lihong Wang, Bin Wang, Li Guo	In this paper, we propose Rule-Guided Embedding (RUGE), a novel paradigm of KG embedding with iterative guidance from soft rules.
591	280 Birds With One Stone: Inducing Multilingual Taxonomies From Wikipedia Using Character-Level Classification	Amit Gupta, Rémi Lebret, Hamza Harkous, Karl Aberer	We propose a novel fully-automated approach towards inducing multilingual taxonomies from Wikipedia.
592	Neural Knowledge Acquisition via Mutual Attention Between Knowledge Graph and Text	Xu Han, Zhiyuan Liu, Maosong Sun	We propose a general joint representation learning framework for knowledge acquisition (KA) on two tasks, knowledge graph completion (KGC) and relation extraction (RE) from text.
593	FEEL: Featured Event Embedding Learning	I-Ta Lee, Dan Goldwasser	In this work, we suggest a general learning model–Featured Event Embedding Learning (FEEL)–for injecting event embeddings with fine grained information.
594	Linguistic Properties Matter for Implicit Discourse Relation Recognition: Combining Semantic Interaction, Topic Continuity and Attribution	Wenqiang Lei, Yuanxin Xiang, Yuwei Wang, Qian Zhong, Meichun Liu, Min-Yen Kan	In contrast to such learning models, we build our model from first principles, analyzing the linguistic properties of the individual top-level Penn Discourse Treebank (PDTB) styled implicit discourse relations: Comparison, Contingency and Expansion.
595	Actionable Email Intent Modeling With Reparametrized RNNs	Chu-Cheng Lin, Dongyeop Kang, Michael Gamon, Patrick Pantel	We propose to annotate these emails for what action its recipient will take.
596	Event Detection via Gated Multilingual Attention Mechanism	Jian Liu, Yubo Chen, Kang Liu, Jun Zhao	In this paper, we propose a novel multilingual approach—dubbed as Gated Multilingual Attention (GMLATT) framework—to address the two issues simultaneously.
597	Improving Sequence-to-Sequence Constituency Parsing	Lemao Liu, Muhua Zhu, Shuming Shi	In this paper, we thereby extend the deterministic attention to directly conduct on the top-down tree linearization.
598	Table-to-Text Generation by Structure-Aware Seq2seq Learning	Tianyu Liu, Kexiang Wang, Lei Sha, Baobao Chang, Zhifang Sui	To encode both the content and the structure of a table, we propose a novel structure-aware seq2seq architecture which consists of field-gating encoder and description generator with dual attention.
599	Never Retreat, Never Retract: Argumentation Analysis for Political Speeches	Stefano Menini, Elena Cabrio, Sara Tonelli, Serena Villata	In this work, we apply argumentation mining techniques, in particular relation prediction, to study political speeches in monological form, where there is no direct interaction between opponents.
600	AMR Parsing With Cache Transition Systems	Xiaochang Peng, Daniel Gildea, Giorgio Satta	In this paper, we present a transition system that generalizes transition-based dependency parsing techniques to generateAMR graphs rather than tree structures.
601	Early Syntactic Bootstrapping in an Incremental Memory-Limited Word Learner	Sepideh Sadeghi, Matthias Scheutz	Early Syntactic Bootstrapping in an Incremental Memory-Limited Word Learner
602	Recognizing and Justifying Text Entailment Through Distributional Navigation on Definition Graphs	Vivian S. Silva, Siegfried Handschuh, André Freitas	We propose an interpretable text entailment approach that, given a structured definition graph, uses a navigation algorithm based on distributional semantic models to find a path in the graph which links text and hypothesis.
603	SPINE: SParse Interpretable Neural Embeddings	Anant Subramanian, Danish Pruthi, Harsh Jhamtani, Taylor Berg-Kirkpatrick, Eduard Hovy	We propose a novel variant of denoising k-sparse autoencoders that generates highly efficient and interpretable distributed word representations (word embeddings), beginning with existing word representations from state-of-the-art methods like GloVe and word2vec.
604	Deep Semantic Role Labeling With Self-Attention	Zhixing Tan, Mingxuan Wang, Jun Xie, Yidong Chen, Xiaodong Shi	In this paper, we present a simple and effective architecture for SRL which aims to address these problems.
605	Translating Pro-Drop Languages With Reconstruction Models	Longyue Wang, Zhaopeng Tu, Shuming Shi, Tong Zhang, Yvette Graham, Qun Liu	In this work, we propose a novel reconstruction-based approach to alleviating DP translation problems for NMT models.
606	Event Representations With Tensor-Based Compositions	Noah Weber, Niranjan Balasubramanian, Nathanael Chambers	We propose a new tensor-based composition method for creating event representations.
607	Does William Shakespeare REALLY Write Hamlet? Knowledge Representation Learning With Confidence	Ruobing Xie, Zhiyuan Liu, Fen Lin, Leyu Lin	To address this problem, we propose a novel confidence-aware knowledge representation learning framework (CKRL), which detects possible noises in KGs while learning knowledge representations with confidence simultaneously.
608	Multi-Channel Encoder for Neural Machine Translation	Hao Xiong, Zhongjun He, Xiaoguang Hu, Hua Wu	Motivated by this demand, we propose Multi-channel Encoder (MCE), which enhances encoding components with different levels of composition.
609	Augmenting End-to-End Dialogue Systems With Commonsense Knowledge	Tom Young, Erik Cambria, Iti Chaturvedi, Hao Zhou, Subham Biswas, Minlie Huang	In this paper, we investigate the impact of providing commonsense knowledge about the concepts covered in the dialogue.
610	An Unsupervised Model With Attention Autoencoders for Question Retrieval	Minghua Zhang, Yunfang Wu	In this paper, we propose a novel unsupervised framework, namely reduced attentive matching network (RAMN), to compute semantic matching between two questions.
611	Sequential Copying Networks	Qingyu Zhou, Nan Yang, Furu Wei, Ming Zhou	In this paper, we propose a novel copying framework, named Sequential Copying Networks (SeqCopyNet), which not only learns to copy single words, but also copies sequences from the input sentence.
612	Lattice Recurrent Unit: Improving Convergence and Statistical Efficiency for Sequence Modeling	Chaitanya Ahuja, Louis-Philippe Morency	We introduce a new family of models, called Lattice Recurrent Units (LRU), to address the challenge of learning deep multi-layer recurrent models with limited resources.
613	Leveraging Lexical Substitutes for Unsupervised Word Sense Induction	Domagoj Alagić, Jan Šnajder, Sebastian Padó	In this paper, we investigate the use of an alternative instance representation based onlexical substitutes, i.e., contextually suitable, meaning-preserving replacements.
614	Generalizing and Improving Bilingual Word Embedding Mappings with a Multi-Step Framework of Linear Transformations	Mikel Artetxe, Gorka Labaka, Eneko Agirre	In this work, we propose a multi-step framework of linear transformations that generalizes a substantial body of previous work.
615	Table-to-Text: Describing Table Region With Natural Language	Junwei Bao, Duyu Tang, Nan Duan, Zhao Yan, Yuanhua Lv, Ming Zhou, Tiejun Zhao	In this paper, we present a generative model to generate a natural language sentence describing a table region, e.g., a row.
616	Learning Interpretable Spatial Operations in a Rich 3D Blocks World	Yonatan Bisk, Kevin J Shih, Yejin Choi, Daniel Marcu	In this paper, we study the problem of mapping natural language instructions to complex spatial actions in a 3D blocks world. We first introduce a new dataset that pairs complex 3D spatial operations to rich natural language descriptions that require complex spatial and pragmatic interpretations such as “mirroring”, “twisting”, and “balancing”.
617	Using k-Way Co-Occurrences for Learning Word Embeddings	Danushka Bollegala, Yuichi Yoshida, Ken-ichi Kawarabayashi	Co-occurrences between two words provide useful insights into the semantics of those words.Consequently, numerous prior work on word embedding learning has used co-occurrences between two wordsas the training signal for learning word embeddings.However, in natural language texts it is common for multiple words to be related and co-occurring in the same context.We extend the notion of co-occurrences to coverk(≥2)-way co-occurrences among a set ofk-words.
618	Proposition Entailment in Educational Applications using Deep Neural Networks	Florin Adrian Bulgarov, Rodney Nielsen	To this end, we propose an approach that splits the reference answer into its constituent propositions and two methods for detecting entailment relations between each reference answer proposition and a student response.
619	cw2vec: Learning Chinese Word Embeddings with Stroke n-gram Information	Shaosheng Cao, Wei Lu, Jun Zhou, Xiaolong Li	We propose cw2vec, a novel method for learning Chinese word embeddings.
620	Knowledge-based Word Sense Disambiguation using Topic Models	Devendra Singh Chaplot, Ruslan Salakhutdinov	In this paper, we leverage the formalism of topic model to design a WSD system that scales linearly with the number of words in the context.
621	Meta Multi-Task Learning for Sequence Modeling	Junkun Chen, Xipeng Qiu, Pengfei Liu, Xuanjing Huang	In this paper, we propose a new sharing scheme of composition function across multiple tasks.
622	IMS-DTM: Incremental Multi-Scale Dynamic Topic Models	Xilun Chen, K. Selcuk Candan, Maria Luisa Sapino	In this paper, we propose a Multi-Scale Dynamic Topic Model (MS-DTM) and a complementary Incremental Multi-Scale Dynamic Topic Model (IMS-DTM) inference method that can be used to capture latent topics and their dynamics simultaneously, at different scales.
623	Zero-Resource Neural Machine Translation with Multi-Agent Communication Game	Yun Chen, Yang Liu, Victor O.K. Li	To tackle this problem, we propose an interactive multimodal framework for zero-resource neural machine translation.
624	Learning to Compose Task-Specific Tree Structures	Jihun Choi, Kang Min Yoo, Sang-goo Lee	In this paper, we propose Gumbel Tree-LSTM, a novel tree-structured long short-term memory architecture that learns how to compose task-specific tree structures only from plain text data efficiently.
625	Geometric Relationship between Word and Context Representations	Jiangtao Feng, Xiaoqing Zheng	In order to make better use of the information contained in the magnitudes of word representations, we propose a hierarchical Gaussian model combined with maximum a posteriori estimation to learn word representations, and extend it to represent polysemous words.
626	A Knowledge-Grounded Neural Conversation Model	Marjan Ghazvininejad, Chris Brockett, Ming-Wei Chang, Bill Dolan, Jianfeng Gao, Wen-tau Yih, Michel Galley	This paper presents a novel, fully data-driven, and knowledge-grounded neural conversation model aimed at producing more contentful responses.
627	Learning to Predict Readability Using Eye-Movement Data From Natives and Learners	Ana V. González-Garduño, Anders Søgaard	We therefore compare the performance of deep learning readability models that use nativespeaker eye movement data to models using data from language learners.
628	Neural Machine Translation with Gumbel-Greedy Decoding	Jiatao Gu, Daniel Jiwoong Im, Victor O.K. Li	In this paper, we propose the \textit{Gumbel-Greedy Decoding} which trains a generative network to predict translation under a trained model.
629	Search Engine Guided Neural Machine Translation	Jiatao Gu, Yong Wang, Kyunghyun Cho, Victor O.K. Li	In this paper, we extend an attention-based neural machine translation (NMT) model by allowing it to access an entire training set of parallel sentence pairs even after training.
630	Long Text Generation via Adversarial Training with Leaked Information	Jiaxian Guo, Sidi Lu, Han Cai, Weinan Zhang, Yong Yu, Jun Wang	In this paper, we propose a new framework, called LeakGAN, to address the problem for long text generation.
631	A Deep Generative Framework for Paraphrase Generation	Ankush Gupta, Arvind Agarwal, Prawaan Singh, Piyush Rai	In this paper, we address the problem of generating paraphrases automatically.
632	Placing Objects in Gesture Space: Toward Incremental Interpretation of Multimodal Spatial Descriptions	Ting Han, Casey Kennington, David Schlangen	In this paper, we model the hearer’s task, using a multimodal spatial description corpus we collected.
633	Jointly Parse and Fragment Ungrammatical Sentences	Homa B. Hashemi, Rebecca Hwa	We propose two automatic methods that jointly parse the ungrammatical sentence and prune the incorrect arcs: a parser retrained on a parallel corpus of ungrammatical sentences with their corrections, and a sequence-to-sequence method.
634	Persuasive Influence Detection: The Role of Argument Sequencing	Christopher Thomas Hidey, Kathleen McKeown	We focus on modeling the sequence of arguments in social media posts using neural models with embeddings for words, discourse relations, and semantic frames.
635	An Interpretable Generative Adversarial Approach to Classification of Latent Entity Relations in Unstructured Sentences	Shiou Tian Hsu, Changsung Moon, Paul Jones, Nagiza Samatova	We propose a generative adversarial neural network model for relation classification that attempts to emulate the way in which human analysts might process sentences.
636	SciTaiL: A Textual Entailment Dataset from Science Question Answering	Tushar Khot, Ashish Sabharwal, Peter Clark	We present a new dataset and model for textual entailment, derived from treating multiple-choice question-answering as an entailment problem.
637	Efficient Large-Scale Multi-Modal Classification	Douwe Kiela, Edouard Grave, Armand Joulin, Tomas Mikolov	We investigate various methods for performing multi-modal fusion and analyze their trade-offs in terms of classification accuracy and computational efficiency.
638	Neural Character-level Dependency Parsing for Chinese	Haonan Li, Zhisong Zhang, Yuqi Ju, Hai Zhao	This paper presents a truly full character-level neural dependency parser together with a newly released character-level dependency treebank for Chinese, which has suffered a lot from the dilemma of defining word or not to model character interactions.
639	Conversational Model Adaptation via KL Divergence Regularization	Juncen Li, Ping Luo, Fen Lin, Bo Chen	In this study we formulate the problem of conversational model adaptation, where we aim to build a generative conversational model for a target domain based on a limited amount of dialogue data from this target domain and some existing dialogue models from related source domains.
640	Slim Embedding Layers for Recurrent Neural Language Models	Zhongliang Li, Raymond Kulhanek, Shaojun Wang, Yunxin Zhao, Shuang Wu	In this paper, we introduce a simple space compression method that randomly shares the structured parameters at both the input and output embedding layers of the recurrent neural language models to significantly reduce the size of model parameters, but still compactly represent the original input and output embedding layers.
641	Automatic Generation of Text Descriptive Comments for Code Blocks	Yuding Liang, Kenny Qili Zhu	We propose a framework to automatically generate descriptive comments for source code blocks.
642	BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems	Zachary Lipton, Xiujun Li, Jianfeng Gao, Lihong Li, Faisal Ahmed, Li Deng	We present a new algorithm that significantly improves the efficiency of exploration for deep Q-learning agents in dialogue systems.
643	Customized Nonlinear Bandits for Online Response Selection in Neural Conversation Models	Bing Liu, Tong Yu, Ian Lane, Ole J. Mengshoel	In this paper, we focus on online learning of response selection in retrieval-based dialog systems.
644	Empower Sequence Labeling with Task-Aware Neural Language Model	Liyuan Liu, Jingbo Shang, Xiang Ren, Frank Fangzheng Xu, Huan Gui, Jian Peng, Jiawei Han	In this study, we develop a neural framework to extract knowledge from raw texts and empower the sequence labeling task.
645	Semantic Structure-Based Word Embedding by Incorporating Concept Convergence and Word Divergence	Qian Liu, Heyan Huang, Guangquan Zhang, Yang Gao, Junyu Xuan, Jie Lu	In this paper, we propose a semantic structure-based word embedding method, and introduce concept convergence and word divergence to reveal semantic structures in the word embedding learning process.
646	Improved Text Matching by Enhancing Mutual Information	Yang Liu, Wenge Rong, Zhang Xiong	In this work, we focus on matching question and answers.
647	Improving Language Modelling with Noise Contrastive Estimation	Farhana Ferdousi Liza, Marek Grzes	In this paper, we showed that NCE can be a very successful approach in neural language modelling when the hyperparameters of a neural network are tuned appropriately.
648	Sentence Ordering and Coherence Modeling using Recurrent Neural Networks	Lajanugen Logeswaran, Honglak Lee, Dragomir Radev	We propose an end-to-end unsupervised deep learning approach based on the set-to-sequence framework to address this problem.
649	Eliciting Positive Emotion through Affect-Sensitive Dialogue Response Generation: A Neural Network Approach	Nurul Lubis, Sakriani Sakti, Koichiro Yoshino, Satoshi Nakamura	In this paper, we build a fully data driven chat-oriented dialogue system that can dynamically mimic affective human interactions by utilizing a neural network architecture.
650	CoChat: Enabling Bot and Human Collaboration for Task Completion	Xufang Luo, Zijia Lin, Yunhong Wang, Zaiqing Nie	In this paper, we introduce CoChat, a dialog management framework to enable effective collaboration between bots and human workers.
651	Fact Checking in Community Forums	Tsvetomila Mihaylova, Preslav Nakov, Lluís Màrquez, Alberto Barrón-Cedeño, Mitra Mohtarami, Georgi Karadzhov, James Glass	As this is a new problem, we create a specialized dataset for it.
652	Personalizing a Dialogue System With Transfer Reinforcement Learning	Kaixiang Mo, Yu Zhang, Shuangyin Li, Jiajun Li, Qiang Yang	By following this idea, we propose a PErsonalized Task-oriented diALogue (PETAL) system, a transfer reinforcement learning framework based on POMDP, to construct a personalized dialogue system.
653	Context Aware Conversational Understanding for Intelligent Agents With a Screen	Vishal Ishwar Naik, Angeliki Metallinou, Rahul Goel	We describe an intelligent context-aware conversational system that incorporates screen context information to service multimodal user requests.
654	Controlling Global Statistics in Recurrent Neural Network Text Generation	Thanapon Noraset, David Demeter, Doug Downey	In this paper, we present a method for training RNNLMs to simultaneously optimize likelihood and follow a given set of statistical constraints on text generation.
655	Few Shot Transfer Learning BetweenWord Relatedness and Similarity Tasks Using A Gated Recurrent Siamese Network	James O' Neill, Paul Buitelaar	As a baseline, we present regression models that incorporate both lexical featuresand word embeddings to produce consistent and competitive results compared to the state of the art.We present our main contribution, the best performing model across seven of the eight datasets – a Gated Recurrent Siamese Networkthat learns relationships between lexical word definitions.A parameter transfer learning strategy is employed for theSiamese Network.
656	Question-Answering with Grammatically-Interpretable Representations	Hamid Palangi, Paul Smolensky, Xiaodong He, Li Deng	We introduce an architecture, the Tensor Product RecurrentNetwork (TPRN).
657	Canonical Correlation Inference for Mapping Abstract Scenes to Text	Nikos Papasarantopoulos, Helen Jiang, Shay B. Cohen	We describe a technique for structured prediction, based on canonical correlation analysis.
658	Exploring the Terrain of Metaphor Novelty: A Regression-Based Approach for Automatically Scoring Metaphors	Natalie Parde, Rodney D. Nielsen	We introduce a large, publicly available metaphor novelty dataset to stimulate research in this area, and propose a regression-based approach to automatically score the novelty of potential metaphors that are expressed as word pairs.
659	Two Knowledge-based Methods for High-Performance Sense Distribution Learning	Tommaso Pasini, Roberto Navigli	We present two fully automatic and language-independent methods for computing the distribution of senses given a raw corpus of sentences.
660	Attention-based Belief or Disbelief Feature Extraction for Dependency Parsing	Haoyuan Peng, Lu Liu, Yi Zhou, Junying Zhou, Xiaoqing Zheng	In this study, we propose a neural feature extraction method that learns to extract arc-specific features.
661	Multi-Task Learning For Parsing The Alexa Meaning Representation Language	Vittorio Perera, Tagyoung Chung, Thomas Kollar, Emma Strubell	The resulting model, which leverages learned embeddings from both tasks, is able to predict the AMRL representation more accurately than other approaches, decreasing the error rates in the full parse by 3.56% absolute and reducing the amount of natively annotated data needed to train accurate parsing models.
662	Bayesian Verb Sense Clustering	Daniel W Peterson, Martha Palmer	We address the main drawbacks of VerbNet, by proposing a Bayesian model to build VerbNet-like clusters automatically and with full coverage.
663	DeepType: Multilingual Entity Linking by Neural Type System Evolution	Jonathan Raphael Raiman, Olivier Michel Raiman	The original problem cannot be solved exactly, so we propose a 2-step algorithm: 1) heuristic search or stochastic optimization over discrete variables that define a type system informed by an Oracle and a Learnability heuristic, 2) gradient descent to fit classifier parameters.We apply DeepType to the problem of Entity Linking on three standard datasets (i.e. WikiDisamb30, CoNLL (YAGO), TAC KBP 2010) and find that it outperforms all existing solutions by a wide margin, including approaches that rely on a human-designed type system or recent deep learning-based entity embeddings, while explicitly using symbolic information lets it integrate new entities without retraining.
664	Order-Planning Neural Text Generation From Structured Data	Lei Sha, Lili Mou, Tianyu Liu, Pascal Poupart, Sujian Li, Baobao Chang, Zhifang Sui	In this paper, we propose an order-planning text generation model, where order information is explicitly captured by link-based attention.
665	A Multi-View Fusion Neural Network for Answer Selection	Lei Sha, Xiaodong Zhang, Feng Qian, Baobao Chang, Zhifang Sui	To overcome this problem, we propose a Multi-View Fusion Neural Network, where each attention component generates a “view” of the QA pair and a fusion RNN integrates the generated views to form a more holistic representation.
666	Generating Sentences Using a Dynamic Canvas	Harshil Shah, Bowen Zheng, David Barber	We introduce the Attentive Unsupervised Text (W)riter (AUTR), which is a word level generative model for natural language.
667	Deconvolutional Latent-Variable Model for Text Sequence Matching	Dinghan Shen, Yizhe Zhang, Ricardo Henao, Qinliang Su, Lawrence Carin	A latent-variable model is introduced for text matching, inferring sentence representations by jointly optimizing generative and discriminative objectives.
668	DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding	Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Shirui Pan, Chengqi Zhang	We propose a novel attention mechanism in which the attention between elements from input sequence(s) is directional and multi-dimensional (i.e., feature-wise).
669	Improving Variational Encoder-Decoders in Dialogue Generation	Xiaoyu Shen, Hui Su, Shuzi Niu, Vera Demberg	In this paper, we separate the training step into two phases: The first phase learns to autoencode discrete texts into continuous embeddings, from which the second phase learns to generalize latent representations by reconstructing the encoded embedding.
670	Neural Cross-Lingual Entity Linking	Avirup Sil, Gourab Kundu, Radu Florian, Wael Hamza	In this paper, we propose a neural EL model that trains fine-grained similarities and dissimilarities between the query and candidate document from multiple perspectives, combined with convolution and tensor networks.
671	Unity in Diversity: Learning Distributed Heterogeneous Sentence Representation for Extractive Summarization	Abhishek Kumar Singh, Manish Gupta, Vasudeva Varma	Experiments on the DUC benchmark datasets (DUC-2001, DUC-2002 and DUC-2004) indicate that our model shows significant performance gain of around 1.5-2 points in terms of ROUGE score compared with the state-of-the-art baselines.
672	Spectral Word Embedding with Negative Sampling	Behrouz Haji Soleimani, Stan Matwin	In this work, we investigate word embedding algorithms in the context of natural language processing.
673	Variational Recurrent Neural Machine Translation	Jinsong Su, Shan Wu, Deyi Xiong, Yaojie Lu, Xianpei Han, Biao Zhang	Partially inspired by successful applications of variational recurrent neural networks, we propose a novel variational recurrent neural machine translation (VRNMT) model in this paper.
674	Incorporating Discriminator in Sentence Generation: a Gibbs Sampling Method	Jinyue Su, Jiacheng Xu, Xipeng Qiu, Xuanjing Huang	In this paper, we propose a novel framework to generate constrained sentences via Gibbs Sampling.
675	Source-Target Inference Models for Spatial Instruction Understanding	Hao Tan, Mohit Bansal	For target position prediction, we compare two inference approaches: annealed sampling via policy gradient versus expectation inference via supervised regression.
676	Cross Temporal Recurrent Networks for Ranking Question Answer Pairs	Yi Tay, Luu Anh Tuan, Siu Cheung Hui	This paper explores the idea of learning temporal gates for sequence pairs (question and answer), jointly influencing the learned representations in a pairwise manner.
677	Guiding Exploratory Behaviors for Multi-Modal Grounding of Linguistic Descriptions	Jesse Thomason, Jivko Sinapov, Raymond J. Mooney, Peter Stone	This paper proposes a method for guiding a robot’s behavioral exploration policy when learning a novel predicate based on known grounded predicates and the novel predicate’s linguistic relationship to them.
678	Learning Better Name Translation for Cross-Lingual Wikification	Chen-Tse Tsai, Dan Roth	In order to cover as many languages as possible, we propose a probabilistic model that leverages indirect supervision signals in a knowledge base.
679	Learning Latent Opinions for Aspect-level Sentiment Classification	Bailin Wang, Wei Lu	Extensive experiments show that our model achieves the state-of-the-art performance while extracting interpretable sentiment expressions.
680	MathDQN: Solving Arithmetic Word Problems via Deep Reinforcement Learning	Lei Wang, Dongxiang Zhang, Lianli Gao, Jingkuan Song, Long Guo, Heng Tao Shen	MathDQN: Solving Arithmetic Word Problems via Deep Reinforcement Learning
681	Dual Transfer Learning for Neural Machine Translation with Marginal Distribution Regularization	Yijun Wang, Yingce Xia, Li Zhao, Jiang Bian, Tao Qin, Guiquan Liu, Tie-Yan Liu	Inspired bythe law of total probability, which connects the probability ofa given target-side monolingual sentence to the conditionalprobability of translating from a source sentence to the targetone, we propose to explicitly exploit this connection tolearn from and regularize the training of NMT models usingmonolingual data.
682	A Neural Transition-Based Approach for Semantic Dependency Graph Parsing	Yuxuan Wang, Wanxiang Che, Jiang Guo, Ting Liu	In this paper, we propose a neural transition-based parser, using a variant of list-based arc-eager transition algorithm for dependency graph parsing.
683	StarSpace: Embed All The Things!	Ledell Yu Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, Jason Weston	We present StarSpace, a general-purpose neural embedding model that can solve a wide variety of problems: labeling tasks such as text classification,ranking tasks such as information retrieval/web search,collaborative filtering-based or content-based recommendation,embedding of multi-relational graphs, and learning word, sentence or document level embeddings.In each case the model works by embedding those entities comprised of discrete features and comparing them against each other — learning similarities dependent on the task.Empirical results on a number of tasks show that StarSpace is highly competitive with existing methods, whilst also being generally applicable to new cases where those methods are not.
684	Word Attention for Sequence to Sequence Text Understanding	Lijun Wu, Fei Tian, Li Zhao, Jianhuang Lai, Tie-Yan Liu	We in this paper, show that an additional attention mechanism called word attention, that builds itself upon word level representations, significantly enhances the performance of sequence to sequence learning.
685	Knowledge Enhanced Hybrid Neural Network for Text Matching	Yu Wu, Wei Wu, Can Xu, Zhoujun Li	In this paper, we focus on exploring the use of taxonomy knowledge for text matching.
686	Neural Response Generation With Dynamic Vocabularies	Yu Wu, Wei Wu, Dejian Yang, Can Xu, Zhoujun Li	We propose a dynamic vocabulary sequence-to-sequence (DVS2S) model which allows each input to possess their own vocabulary in decoding.
687	Learning to Extract Coherent Summary via Deep Reinforcement Learning	Yuxiang Wu, Baotian Hu	As an effort towards extracting coherent summaries, we propose a neural coherence model to capture the cross-sentence semantic and syntactic coherence patterns.
688	Hierarchical Recurrent Attention Network for Response Generation	Chen Xing, Yu Wu, Wei Wu, Yalou Huang, Ming Zhou	We propose a hierarchical recurrent attention network (HRAN) to model both the hierarchy and the importance variance in a unified framework.
689	How Images Inspire Poems: Generating Classical Chinese Poetry from Images with Memory Networks	Linli Xu, Liang Jiang, Chuan Qin, Zhe Wang, Dongfang Du	In this paper, we propose a memory based neural model which exploits images to generate poems.
690	Learning Multi-Modal Word Representation Grounded in Visual Context	éloi Zablocki, Benjamin Piwowarski, Laure Soulier, Patrick Gallinari	We propose to unify text-based techniques with vision-based techniques by simultaneously leveraging textual and visual context to learn multimodal word embeddings.
691	Memory Fusion Network for Multi-view Sequential Learning	Amir Zadeh, Paul Pu Liang, Navonil Mazumder, Soujanya Poria, Erik Cambria, Louis-Philippe Morency	In this paper, we present a new neural architecture for multi-view sequential learning called the Memory Fusion Network (MFN) that explicitly accounts for both interactions in a neural architecture and continuously models them through time.
692	Multi-attention Recurrent Network for Human Communication Comprehension	Amir Zadeh, Paul Pu Liang, Soujanya Poria, Prateek Vij, Erik Cambria, Louis-Philippe Morency	In this paper, we present a novel neural architecture for understanding human communication called the Multi-attention Recurrent Network (MARN).
693	Chinese LIWC Lexicon Expansion via Hierarchical Classification of Word Embeddings with Sememe Attention	Xiangkai Zeng, Cheng Yang, Cunchao Tu, Zhiyuan Liu, Maosong Sun	To address this issue, we propose to expand the LIWC lexicon automatically.
694	Large Scaled Relation Extraction With Reinforcement Learning	Xiangrong Zeng, Shizhu He, Kang Liu, Jun Zhao	To solve this problem, we propose a novel model with reinforcement learning.
695	End-to-End Quantum-like Language Models with Application to Question Answering	Peng Zhang, Jiabin Niu, Zhan Su, Benyou Wang, Liqun Ma, Dawei Song	In this paper, we aim to broaden the theoretical and practical basis of QLM.
696	Adaptive Co-attention Network for Named Entity Recognition in Tweets	Qi Zhang, Jinlan Fu, Xiaoyu Liu, Xuanjing Huang	In this study, we investigate the problem of named entity recognition for tweets.
697	Neural Networks Incorporating Dictionaries for Chinese Word Segmentation	Qi Zhang, Xiaoyu Liu, Jinlan Fu	In this paper, we seek to address the problem of incorporating dictionaries into neural networks for the Chinese word segmentation task.
698	Addressee and Response Selection in Multi-Party Conversations With Speaker Interaction RNNs	Rui Zhang, Honglak Lee, Lazaros Polymenakos, Dragomir Radev	In this paper, we study the problem of addressee and response selection in multi-party conversations.
699	Asynchronous Bidirectional Decoding for Neural Machine Translation	Xiangwen Zhang, Jinsong Su, Yue Qin, Yang Liu, Rongrong Ji, Hongji Wang	In this paper, we equip the conventional attentional encoder-decoder NMT framework with a backward decoder, in order to explore bidirectional decoding for NMT.
700	Medical Exam Question Answering with Large-scale Reading Comprehension	Xiao Zhang, Ji Wu, Zhiyang He, Xien Liu, Ying Su	In this work, we introduce a question-answering task called MedQA to study answering questions in clinical medicine using knowledge in a large-scale document collection.
701	CoLink: An Unsupervised Framework for User Identity Linkage	Zexuan Zhong, Yong Cao, Yong Cao, Mu Guo, Mu Guo, Zaiqing Nie, Zaiqing Nie	In this paper we propose CoLink, a general unsupervised framework for the UIL problem.
702	Tree-Structured Neural Machine for Linguistics-Aware Sentence Generation	Ganbin Zhou, Ping Luo, Rongyu Cao, Yijun Xiao, Fen Lin, Bo Chen, Qing He	For training acceleration, we propose a tree canonicalization method, which transforms trees into equivalent ternary trees.
703	Elastic Responding Machine for Dialog Generation with Dynamically Mechanism Selecting	Ganbin Zhou, Ping Luo, Yijun Xiao, Fen Lin, Bo Chen, Qing He	To address this issue, we propose the elastic responding machine (ERM), which is based on a proposed encoder-diverter-filter-decoder framework.
704	RNN-Based Sequence-Preserved Attention for Dependency Parsing	Yi Zhou, Junying Zhou, Lu Liu, Jiangtao Feng, Haoyuan Peng, Xiaoqing Zheng	In this study, we propose an RNN-based attention to capture the relevant and sequence-preserved features from a sentence, and use the derived features to perform the dependency parsing.
705	Generative Adversarial Network Based Heterogeneous Bibliographic Network Representation for Personalized Citation Recommendation	Xiaoyan Cai, Junwei Han, Libin Yang	In this paper, we propose a deep network representation model that integrates network structure and the vertex content information into a unified framework by exploiting generative adversarial network, and represents different types of vertices in the heterogeneous network in a continuous and common vector space.
706	A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction	Shamil Chollampatt, Hwee Tou Ng	We improve automatic correction of grammatical, orthographic, and collocation errors in text using a multilayer convolutional encoder-decoder neural network.
707	Weakly Supervised Induction of Affective Events by Optimizing Semantic Consistency	Haibo Ding, Ellen Riloff	Our research investigates the prevalence of affective events in a personal story corpus, and introduces a weakly supervised method for large scale induction of affective events.
708	Cross-Lingual Propagation for Deep Sentiment Analysis	Xin Dong, Gerard de Melo	In this work, we present a cross-lingual propagation algorithm that yields sentiment embedding vectors for numerous languages.
709	Reinforcement Learning for Relation Classification From Noisy Data	Jun Feng, Minlie Huang, Li Zhao, Yang Yang, Xiaoyan Zhu	In this paper, we propose a novel model for relation classification at the sentence level from noisy data.
710	Twitter Summarization Based on Social Network and Sparse Reconstruction	Ruifang He, Xingyi Duan	In this paper, we study extractive topic-oriented Twitter summarization as a solution to address this problem. Due to the lack of public corpus, we construct the gold standard twitter summary datasets for 12 different topics.
711	SEE: Syntax-Aware Entity Embedding for Neural Relation Extraction	Zhengqiu He, Wenliang Chen, Zhenghua Li, Meishan Zhang, Wei Zhang, Min Zhang	In this paper, we propose to learn syntax-aware entity embedding for neural relation extraction.
712	Semi-Distantly Supervised Neural Model for Generating Compact Answers to Open-Domain Why Questions	Ryo Ishida, Kentaro Torisawa, Jong-Hoon Oh, Ryu Iida, Canasai Kruengkrai, Julien Kloetzer	This paper proposes a neural network-based method for generating compact answers to open-domain why-questions (e.g., “Why was Mr. Trump elected as the president of the US?”) We also automatically generate training data using a large number of causal relations automatically extracted from 4 billion web pages by an existing supervised causality recognizer.
713	Task-Specific Representation Learning for Web-Scale Entity Disambiguation	Rijula Kar, Susmija Reddy, Sourangshu Bhattacharya, Anirban Dasgupta, Soumen Chakrabarti	Specifically, we propose a task-sensitive representation learning framework that learns mention dependent representations, followed by a common classifier.
714	Byte-Level Machine Reading Across Morphologically Varied Languages	Tom Kenter, Llion Jones, Daniel Hewlett	In this work, we investigate whether bytes are suitable as input units across morphologically varied languages. To test this, we introduce two large-scale machine reading datasets in morphologically rich languages, Turkish and Russian.
715	A Question-Focused Multi-Factor Attention Network for Question Answering	Souvik Kundu, Hwee Tou Ng	In this paper, we propose a novel end-to-end question-focused multi-factor attention network for answer extraction.
716	Training and Evaluating Improved Dependency-Based Word Embeddings	Chen Li, Jianxin Li, Yangqiu Song, Ziwei Lin	In this paper, we focus on learning word embeddings through selective higher-order relationships in sentences to improve the embeddings to be less sensitive to local context and more accurate in capturing semantic compositionality.
717	Inference on Syntactic and Semantic Structures for Machine Comprehension	Chenrui Li, Yuanbin Wu, Man Lan	Here we introduce linguistic structures to help capturing global evidence in hidden variable modeling.
718	Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification	Zheng Li, Ying Wei, Yu Zhang, Qiang Yang	In order to solve this problem, we propose a Hierarchical Attention Transfer Network (HATN) for cross-domain sentiment classification.
719	Dynamic User Profiling for Streams of Short Texts	Shangsong Liang	In this paper, we aim at tackling the problem of dynamic user profiling in the context of streams of short texts.
720	Multi-Task Medical Concept Normalization Using Multi-View Convolutional Neural Network	Yi Luo, Guojie Song, Pengyu Li, Zhongang Qi	In this paper, we focus on normalizing diagnostic and procedure names in Chinese discharge summaries to standard entities, which is formulated as a semantic matching problem.
721	Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM	Yukun Ma, Haiyun Peng, Erik Cambria	In this paper, we propose a novel solution to targeted aspect-based sentiment analysis, which tackles the challenges of both aspect-based sentiment analysis and targeted sentiment analysis by exploiting commonsense knowledge.
722	Cognition-Cognizant Sentiment Analysis With Multitask Subjectivity Summarization Based on Annotators’ Gaze Behavior	Abhijit Mishra, Srikanth Tamilselvam, Riddhiman Dasgupta, Seema Nagar, Kuntal Dey	We propose a multi-task deep neural framework for document level sentiment analysis that learns to predict the overall sentiment expressed in the given input document, by simultaneously learning to predict human gaze behavior and auxiliary linguistic tasks like part-of-speech and syntactic properties of words in the document.
723	Argument Mining for Improving the Automated Scoring of Persuasive Essays	Huy V. Nguyen, Diane J. Litman	In this paper we identify a set of desiderata for evaluating the use of argument mining for AES, introduce an end-to-end argument mining system and associated argumentative feature sets, and present the results of several studies that both satisfy the desiderata and demonstrate the value-added of argument mining for scoring persuasive essays.
724	Graph Convolutional Networks With Argument-Aware Pooling for Event Detection	Thien Huu Nguyen, Ralph Grishman	In this work, we investigate a convolutional neural network based on dependency trees to perform event detection.
725	Mention and Entity Description Co-Attention for Entity Disambiguation	Feng Nie, Yunbo Cao, Jinpeng Wang, Chin-Yew Lin, Rong Pan	In this paper, we propose a type-aware co-attention model for entity disambiguation, which tries to identify the most discriminative words from mention contexts and most relevant sentences from corresponding entity descriptions simultaneously.
726	Jointly Extracting Event Triggers and Arguments by Dependency-Bridge RNN and Tensor-Based Argument Interaction	Lei Sha, Feng Qian, Baobao Chang, Zhifang Sui	In this paper, we propose a novel dependency bridge recurrent neural network (dbRNN) for event extraction.
727	Content and Context: Two-Pronged Bootstrapped Learning for Regex-Formatted Entity Extraction	Stanley Simoes, Deepak P, Munu Sairamesh, Deepak Khemani, Sameep Mehta	In this paper, we propose a bootstrapped approach to improve the recall for extraction of regex-formatted entities, with the only source of supervision being the seed regex.
728	Towards a Neural Conversation Model With Diversity Net Using Determinantal Point Processes	Yiping Song, Rui Yan, Yansong Feng, Yaoyuan Zhang, Dongyan Zhao, Ming Zhang	In this paper, we investigate the diversity issue in two different aspects, namely query-level and system-level diversity.
729	S-Net: From Answer Extraction to Answer Synthesis for Machine Reading Comprehension	Chuanqi Tan, Furu Wei, Nan Yang, Bowen Du, Weifeng Lv, Ming Zhou	In this paper, we present a novel approach to machine reading comprehension for the MS-MARCO dataset.
730	SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text Scoring	Yi Tay, Minh C. Phan, Luu Anh Tuan, Siu Cheung Hui	In this paper, we describe a new neural architecture that enhances vanilla neural network models with auxiliary neural coherence features.
731	Learning to Attend via Word-Aspect Associative Fusion for Aspect-Based Sentiment Analysis	Yi Tay, Luu Anh Tuan, Siu Cheung Hui	In this paper, we propose a novel method for integrating aspect information into the neural model.
732	Investigating Inner Properties of Multimodal Representation and Semantic Compositionality With Brain-Based Componential Semantics	Shaonan Wang, Jiajun Zhang, Nan Lin, Chengqing Zong	To that end, we propose simple interpretation methods based on brain-based componential semantics.
733	Learning Multimodal Word Representation via Dynamic Fusion Methods	Shaonan Wang, Jiajun Zhang, Chengqing Zong	To that end, we propose three novel dynamic fusion methods to assign importance weights to each modality, in which weights are learned under the weak supervision of word association pairs.
734	R	Shuohang Wang, Mo Yu, Xiaoxiao Guo, Zhiguo Wang, Tim Klinger, Wei Zhang, Shiyu Chang, Gerry Tesauro, Bowen Zhou, Jing Jiang	In this paper, we present a novel open-domain QA system called Reinforced Ranker-Reader (R3), based on two algorithmic innovations.
735	Improving Review Representations With User Attention and Product Attention for Sentiment Classification	Zhen Wu, Xin-Yu Dai, Cunyan Yin, Shujian Huang, Jiajun Chen	In this paper, we propose a novel framework to encode user and product information.
736	Improving Neural Fine-Grained Entity Typing With Knowledge Attention	Ji Xin, Yankai Lin, Zhiyuan Liu, Maosong Sun	To address these issues, we take information from KBs into consideration to bridge entity mentions and their context together, and thereby propose Knowledge-Attention Neural Fine-Grained Entity Typing.
737	Diagnosing and Improving Topic Models by Analyzing Posterior Variability	Linzi Xing, Michael J. Paul	In this work, we explore other rich information that can be obtained by analyzing the posterior distributions in topic models.
738	Dual Attention Network for Product Compatibility and Function Satisfiability Analysis	Hu Xu, Sihong Xie, Lei Shu, Philip S. Yu	In this paper, we address two closely related problems: product compatibility analysis and function satisfiability analysis, where the second problem is a generalization of the first problem (e.g., whether a product works with another product can be considered as a special function).
739	Assertion-Based QA With Question-Aware Open Information Extraction	Zhao Yan, Duyu Tang, Nan Duan, Shujie Liu, Wendi Wang, Daxin Jiang, Ming Zhou, Zhoujun Li	We present assertion based question answering (ABQA), an open domain question answering task that takes a question and a passage as inputs, and outputs a semi-structured assertion consisting of a subject, a predicate and a list of arguments. To remedy this, we introduce a new dataset called WebAssertions, which includes hand-annotated QA labels for 358,427 assertions in 55,960 web passages.
740	Multi-Entity Aspect-Based Sentiment Analysis With Context, Entity and Aspect Memory	Jun Yang, Runqi Yang, Chongjun Wang, Junyuan Xie	To address the task, we propose an innovative method that models Context memory, Entity memory and Aspect memory, called CEA method.
741	OTyper: A Neural Architecture for Open Named Entity Typing	Zheng Yuan, Doug Downey	In this work, we introduce the task of Open Named Entity Typing (ONET), which is NET when the set of target types is not known in advance.
742	Scale Up Event Extraction Learning via Automatic Training Data Generation	Ying Zeng, Yansong Feng, Rong Ma, Zheng Wang, Rui Yan, Chongde Shi, Dongyan Zhao	Our work develops an automatic approach for generating training data for event extraction.
743	Learning Structured Representation for Text Classification via Reinforcement Learning	Tianyang Zhang, Minlie Huang, Li Zhao	Unlike most existing representation models that either use no structure or rely on pre-specified structures, we propose a reinforcement learning (RL) method to learn sentence representation by discovering optimized structures automatically.
744	Duplicate Question Identification by Integrating FrameNet With Neural Networks	Xiaodong Zhang, Xu Sun, Houfeng Wang	In this paper, we focus on the essential constituents matching problem and use FrameNet-style semantic parsing to tackle it.
745	Variational Reasoning for Question Answering With Knowledge Graph	Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander J. Smola, Le Song	To address these challenges, we propose a novel and unified deep learning architecture, and an end-to-end variational learning algorithm which can handle noise in questions, and learn multi-hop reasoning simultaneously.
746	Hierarchical Attention Flow for Multiple-Choice Reading Comprehension	Haichao Zhu, Furu Wei, Bing Qin, Ting Liu	In this paper, we focus on multiple-choice reading comprehension which aims to answer a question given a passage and multiple candidate options.
747	Resource-Constrained Scheduling for Maritime Traffic Management	Lucas Agussurja, Akshat Kumar, Hoong Chuin Lau	Our contributions are: 1) We formulate the maritime traffic management problem based on the real case study of Singapore waters; 2) We model the problem as a variant of the resource-constrained project scheduling problem (RCPSP), and formulate mixed-integer and constraint programming (MIP/CP) formulations; 3) To improve the scalability, we develop a combinatorial Benders (CB) approach that is significantly more effective than standard MIP and CP formulations. We also develop symmetry breaking constraints and optimality cuts that further enhance the CB approach’s effectiveness; 4) We develop a realistic maritime traffic simulator using electronic navigation charts of Singapore Straits.
748	Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary	Masataro Asai, Alex Fukunaga	The contribution of this paper is twofold: (1) State Autoencoder, which finds a propositional state representation of the environment using a Variational Autoencoder.
749	Planning With Pixels in (Almost) Real Time	Wilmer Bandres, Blai Bonet, Hector Geffner	In this work, we consider the same planning problem but using the screen instead.
750	totSAT – Totally-Ordered Hierarchical Planning Through SAT	Gregor Behnke, Daniel Höller, Susanne Biundo	In this paper, we propose a novel SAT-based planning approach for hierarchical planning by introducing the SAT-based planner totSAT for the class of totally-ordered HTN planning problems.
751	Sublinear Search Spaces for Shortest Path Planning in Grid and Road Networks	Johannes Blum, Stefan Funke, Sabine Storandt	In this paper, we use the very intuitive notion of bounded growth graphs to describe road networks and also grid graphs.
752	Scheduling in Visual Fog Computing: NP-Completeness and Practical Efficient Solutions	Hong-Min Chu, Shao-Wen Yang, Padmanabhan Pillai, Yen-Kuang Chen	This paper focuses on the visual fog scheduling problem of assigning the visual computing tasks to various devices to optimize network utilization.
753	Fat- and Heavy-Tailed Behavior in Satisficing Planning	Eldan Cohen, J. Christopher Beck	In this work, we study the runtime distribution of satisficing planning in ensembles of random planning problems and in multiple runs of a randomized heuristic search on a single planning instance.
754	Finite Sample Analyses for TD(0) With Function Approximation	Gal Dalal, Balázs Szörényi, Gugan Thoppe, Shie Mannor	We provide convergence rates both in expectation and with high-probability.
755	Synthesis of Orchestrations of Transducers for Manufacturing	Giuseppe De Giacomo, Moshe Y. Vardi, Paolo Felli, Natasha Alechina, Brian Logan	In this paper, we model manufacturing processes and facilities as transducers (automata with output).
756	Meta-Search Through the Space of Representations and Heuristics on a Problem by Problem Basis	Raquel Fuentetaja, Michael Barley, Daniel Borrajo, Jordan Douglas, Santiago Franco, Patricia Riddle	This paper describes a meta-reasoning system that searches through the space of combinations of representations and heuristics to find one suitable for optimally solving the specific problem.
757	Expressive Real-Time Intersection Scheduling	Rick Goldstein, Stephen F. Smith	We present Expressive Real-time Intersection Scheduling (ERIS), a schedule-driven control strategy for adaptive intersection control to reduce traffic congestion.
758	Planning and Learning for Decentralized MDPs With Event Driven Rewards	Tarun Gupta, Akshat Kumar, Praveen Paruchuri	Algorithmically, we contribute—1) A nonlinear programming (NLP) formulation for such event-based planning model; 2) A probabilistic inference based approach that scales much better than NLP solvers for a large number of agents; 3) A policy gradient based multiagent reinforcement learning approach that scales well even for exponential state-spaces.
759	A Recursive Algorithm to Generate Balanced Weekend Tournaments	Richard Hoshino	In this paper, we construct a Balanced Weekend Tournament, motivated by the real-life problem of scheduling an n-team double round-robin season schedule for a Canadian university soccer league.
760	Plan Recognition in Continuous Domains	Gal A. Kaminka, Mor Vered, Noa Agmon	We provide formal arguments for the usefulness of mirroring, and empirically evaluate mirroring in more than a thousand recognition problems in three continuous domains and six classical planning domains.
761	Semi-Black Box: Rapid Development of Planning Based Solutions	Michael Katz, Dany Moshkovich, Erez Karpas	We propose an approach that we baptize as semi-black box (SBB) that combines the strength of both.
762	Multiagent Simple Temporal Problem: The Arc-Consistency Approach	Shufeng Kong, Jae Hee Lee, Sanjiang Li	In this paper we present a novel approach that is based on enforcing arc-consistency (AC) on the input (multiagent) simple temporal network.
763	Load Scheduling of Simple Temporal Networks Under Dynamic Resource Pricing	T. K. Satish Kumar, Zhi Wang, Anoop Kumar, Craig Milo Rogers, Craig A. Knoblock	We provide a polynomial-time algorithm for solving the load scheduling problem when f(t) is piecewise constant.
764	On the Relationship Between State-Dependent Action Costs and Conditional Effects in Planning	Robert Mattmüller, Florian Geißer, Benedict Wright, Bernhard Nebel	In this paper, we show how this issue can be avoided by representing state-dependent costs and conditional effects uniformly, both as edge-valued multi-valued decision diagrams (EVMDDs) over different sets of edge values, and then working with their product diagram.
765	Generalized Value Iteration Networks:Life Beyond Lattices	Sufeng Niu, Siheng Chen, Hanyu Guo, Colin Targonski, Melissa C. Smith, Jelena Kovačević	In this paper, we introduce a generalized value iteration network (GVIN), which is an end-to-end neural network planning module.
766	Linear and Integer Programming-Based Heuristics for Cost-Optimal Numeric Planning	Chiara Piacentini, Margarita P. Castro, Andre A. Cire, J. Christopher Beck	In this work, we extend linear programming-based heuristics for classical planning to support numeric state variables.
767	Sensor-Based Activity Recognition via Learning From Distributions	Hangwei Qian, Sinno Jialin Pan, Chunyan Miao	Therefore, in this paper, we propose a new method, denoted by SMMAR, based on learning from distributions for sensor-based activity recognition.
768	Knowledge-Based Policies for Qualitative Decentralized POMDPs	Abdallah Saffidine, François Schwarzentruber, Bruno Zanuttini	We propose and investigate a new representation for joint policies in QDec-POMDPs, which we call Multi-Agent Knowledge-Based Programs (MAKBPs), and which uses epistemic logic for compactly representing conditions on histories.
769	Risk-Aware Proactive Scheduling via Conditional Value-at-Risk	Wen Song, Donghun Kang, Jie Zhang, Hui Xi	In this paper, we consider the challenging problem of riskaware proactive scheduling with the objective of minimizing robust makespan.
770	Stackelberg Planning: Towards Effective Leader-Follower State Space Search	Patrick Speicher, Marcel Steinmetz, Michael Backes, Jörg Hoffmann, Robert Künnemann	Inspired by work on Stackelberg security games, we introduce Stackelberg planning, where a leader player in a classical planning task chooses a minimum-cost action sequence aimed at maximizing the plan cost of a follower player in the same task.
771	Action Schema Networks: Generalised Policies With Deep Learning	Sam Toyer, Felipe Trevizan, Sylvie Thiébaux, Lexing Xie	In this paper, we introduce the Action Schema Network (ASNet): a neural network architecture for learning generalised policies for probabilistic planning problems.
772	Learning Conditional Generative Models for Temporal Point Processes	Shuai Xiao, Hongteng Xu, Junchi Yan, Mehrdad Farajtabar, Xiaokang Yang, Le Song, Hongyuan Zha	Our new model builds upon the sequence to sequence (seq2seq) prediction network.
773	Combining Experts’ Causal Judgments	Dalal Alrajeh, Hana Chockler, Joseph Yehuda Halpern	We define a notion of two causal models being compatible, and show how compatible causal models can be combined.
774	An Experimental Study of Advice in Sequential Decision-Making Under Uncertainty	Florian Benavent, Bruno Zanuttini	We give a unified view of a number of proposals made in the literature, and propose a new notion of advice, which corresponds to a user telling why she would take a given action in a given state.
775	Optimal Approximation of Random Variables for Estimating the Probability of Meeting a Plan Deadline	Liat Cohen, Tal Grinshpoun, Gera Weiss	In this paper, as a main motivating example, we elaborate on the case of estimating probabilities of meeting deadlines in hierarchical plans.
776	Generalized Adjustment Under Confounding and Selection Biases	Juan D. Correa, Jin Tian, Elias Bareinboim	We introduce the notion of adjustment pair and present complete graphical conditions for identifying causal effects by adjustment.
777	Armstrong’s Axioms and Navigation Strategies	Kaya Deuser, Pavel Naumov	The paper investigates navigability with imperfect information.
778	Lifted Generalized Dual Decomposition	Nicholas Gallo, Alexander Ihler	In this paper, we present a method based on a lifted variant of the generalized dual decomposition (GenDD) for marginal MAP inference which provides a principled way to exploit symmetric sub-structures in a graphical model.
779	Learning Mixtures of MLNs	Mohammad Maminur Islam, Somdeb Sarkhel, Deepak Venugopal	In this paper, we propose a novel, intuitive approach for learning MLNs discriminatively by utilizing approximate symmetries.
780	RelNN: A Deep Neural Model for Relational Learning	Seyed Mehran Kazemi, David Poole	In this paper, we develop relational neural networks (RelNNs) by adding hidden layers to relational logistic regression (the relational counterpart of logistic regression).
781	Approximate Inference via Weighted Rademacher Complexity	Jonathan Kuck, Ashish Sabharwal, Stefano Ermon	We leverage this observation and introduce a new technique for estimating the size of an arbitrary weighted set, defined as the sum of weights of all elements in the set.
782	Relational Marginal Problems: Theory and Estimation	Ondřej Kuželka, Yuyi Wang, Jesse Davis, Steven Schockaert	We study this problem in a relational setting and make the following contributions.
783	Anytime Anyspace AND/OR Best-First Search for Bounding Marginal MAP	Qi Lou, Rina Dechter, Alexander Ihler	In this paper, we propose a best-first search algorithm that provides anytime upper bounds for marginal MAP in graphical models.
784	A Neural Stochastic Volatility Model	Rui Luo, Weinan Zhang, Xiaojun Xu, Jun Wang	In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation of time series) models that have been widely used in time series analysis and prediction in finance.
785	Learning Robust Options	Daniel J. Mankowitz, Timothy A. Mann, Pierre-Luc Bacon, Doina Precup, Shie Mannor	In this paper, we propose robust methods for learning temporally abstract actions, in the framework of options.
786	Efficient-UCBV: An Almost Optimal Algorithm Using Variance Estimates	Subhojyoti Mukherjee, K. P. Naveen, Nandan Sudarsanam, Balaraman Ravindran	We propose a novel variant of the UCB algorithm (referred to as Efficient-UCB-Variance (EUCBV)) for minimizing cumulative regret in the stochastic multi-armed bandit (MAB) setting.
787	Hawkes Process Inference With Missing Data	Christian R. Shelton, Zhen Qin, Chandini Shetty	In this paper we develop a method to sample over the posterior distribution of unobserved events in a multivariate Hawkes process.
788	Conditional PSDDs: Modeling and Learning With Modular Knowledge	Yujia Shen, Arthur Choi, Adnan Darwiche	In this paper, we propose a variant on PSDDs, called conditional PSDDs, for representing a family of distributions that are conditioned on the same set of variables.
789	Information Acquisition Under Resource Limitations in a Noisy Environment	Matvey Soloviev, Joseph Y. Halpern	We introduce a theoretical model of information acquisition under resource limitations in a noisy environment.
790	Risk-Sensitive Submodular Optimization	Bryan Wilder	We give a (1 – 1/e)-approximation algorithm for maximizing the CVaR of a monotone continuous submodular function.
791	Towards Training Probabilistic Topic Models on Neuromorphic Multi-Chip Systems	Zihao Xiao, Jianfei Chen, Jun Zhu	We present three SNNs to train topic models.The first SNN is a batch algorithm combining the conventional collapsed Gibbs sampling (CGS) algorithm and an inference SNN to train LDA.
792	IONet: Learning to Cure the Curse of Drift in Inertial Odometry	Changhao Chen, Xiaoxuan Lu, Andrew Markham, Niki Trigoni	We propose to break the cycle of continuous integration, and instead segment inertial data into independent windows.
793	Improved Results for Minimum Constraint Removal	Eduard Eiben, Jonathan Gemmell, Iyad Kanj, Andrew Youngdahl	In this work, we extend the study of Minimum Constraint Removal.
794	Safe Reinforcement Learning via Formal Methods: Toward Safe Control Through Proof and Learning	Nathan Fulton, André Platzer	This paper presents an approach for provably safe learning that provides the best of both worlds: the exploration and optimization capabilities of learning along with the safety guarantees of formal verification.
795	Iterative Continuous Convolution for 3D Template Matching and Global Localization	Vitor Guizilini, Fabio Ramos	This paper introduces a novel methodology for 3D template matching that is scalable to higher-dimensional spaces and larger kernel sizes.
796	Learning Integrated Holism-Landmark Representations for Long-Term Loop Closure Detection	Fei Han, Hua Wang, Hao Zhang	In this paper, we introduce a novel formulation to learn an integrated long-term representation based upon both holistic and landmark information, which integrates two previous insights under a unified framework: (1) holistic representations outperform keypoint-based representations, and (2) landmarks as an intermediate representation provide informative cues to detect challenging locations.
797	Guiding Search in Continuous State-Action Spaces by Learning an Action Sampler From Off-Target Search Experience	Beomjoon Kim, Leslie Pack Kaelbling, Tomás Lozano-Pérez	We introduce a new technique, based on an importance-ratio estimation method, for using samples from a non-target distribution to make GAN learning more data-efficient.
798	Unsupervised Selection of Negative Examples for Grounded Language Learning	Nisha Pillai, Cynthia Matuszek	We describe an unsupervised system that learns language by training visual classifiers, first selecting important terms from object descriptions, then automatically choosing negative examples from a paired corpus of perceptual and linguistic data.
799	From Virtual Demonstration to Real-World Manipulation Using LSTM and MDN	Rouhollah Rahmatizadeh, Pooya Abolghasemi, Aman Behal, Ladislau Bölöni	In this paper we describe a solution to the challenging problem of behavior transfer from virtual demonstration to a physical robot.
800	Building Continuous Occupancy Maps With Moving Robots	Ransalu Senanayake, Fabio Ramos	In this work, we provide a theoretical analysis to compare and contrast the two major branches of Bayesian continuous occupancy mapping techniques—Gaussian process occupancy maps and Bayesian Hilbert maps—considering the fact that both utilize kernel functions to operate in a rich high-dimensional implicit feature space and use variational inference to learn parameters.
801	Phase-Parametric Policies for Reinforcement Learning in Cyclic Environments	Arjun Sharma, Kris M. Kitani	To better model cyclic environments, we propose phase-parameterized policies and value function approximators that explicitly enforce a cyclic structure to the policy or value space.
802	Safe Exploration and Optimization of Constrained MDPs Using Gaussian Processes	Akifumi Wachi, Yanan Sui, Yisong Yue, Masahiro Ono	We propose a novel approach to balance this trade-off.
803	Sweep-Based Propagation for String Constraint Solving	Roberto Amadini, Graeme Gange, Peter J. Stuckey	In this paper, we present a more efficient algorithm for propagating equality (and related constraints) over dashed strings.
804	MaxSAT Resolution With the Dual Rail Encoding	Maria Luisa Bonet, Sam Buss, Alexey Ignatiev, Joao Marques-Silva, Antonio Morgado	Recent work proposed an approach for solving SAT by reduction to Horn MaxSAT.
805	A SAT+CAS Method for Enumerating Williamson Matrices of Even Order	Curtis Bright, Ilias Kotsireas, Vijay Ganesh	We present for the first time an exhaustive enumeration of Williamson matrices of even order n < 65.
806	Exact MAP-Inference by Confining Combinatorial Search With LP Relaxation	Stefan Haller, Paul Swoboda, Bogdan Savchynskyy	We propose a family of relaxations (different from the famous Sherali-Adams hierarchy), which naturally define lower bounds for its optimum.
807	Community-Based Trip Sharing for Urban Commuting	Mohd. Hafiz Hasan, Pascal Van Hentenryck, Ceren Budak, Jiayu Chen, Chhavi Chaudhry	It introduces the Commuting Trip Sharing Problem (CTSP) and proposes an optimization approach to maximize trip sharing.
808	Schur Number Five	Marijn J. H. Heule	We present the solution of a century-old problem known as Schur Number Five: What is the largest (natural) number n such that there exists a five-coloring of the positive numbers up to n without a monochromatic solution of the equation a + b = c?
809	Towards Generalization in QBF Solving via Machine Learning	Mikoláš Janota	This paper argues that a solver benefits from generalizing a set of individual wins into a strategy.
810	Verifying Properties of Binarized Deep Neural Networks	Nina Narodytska, Shiva Kasiviswanathan, Leonid Ryzhyk, Mooly Sagiv, Toby Walsh	In this paper, we take a step in this direction by proposing a rigorous way of verifying properties of a popular class of neural networks, Binarized Neural Networks, using the well-developed means of Boolean satisfiability.
811	Parallel Algorithms for Operations on Multi-Valued Decision Diagrams	Guillaume Perez, Jean-Charles Régin	In this paper, we introduce such algorithms.
812	Premise Set Caching for Enumerating Minimal Correction Subsets	Alessandro Previti, Carlos Mencía, Matti Järvisalo, Joao Marques-Silva	In this work, we propose a novel approach to speeding up MCS enumeration over conjunctive normal form propositional formulas by caching of so-called premise sets (PSes) seen during the enumeration process.
813	On Cryptographic Attacks Using Backdoors for SAT	Alexander Semenov, Oleg Zaikin, Ilya Otpuschennikov, Stepan Kochemazov, Alexey Ignatiev	This paper proposes a new class of backdoor sets for SAT used in the context of cryptographic attacks, namely guess-and-determine attacks.
814	Enhancing Constraint-Based Multi-Objective Combinatorial Optimization	Miguel Terra-Neves, Inês Lynce, Vasco Manquinho	Minimal Correction Subsets (MCSs) have been successfully applied to find approximate solutions to several real-world single-objective optimization problems.
815	Learning Robust Search Strategies Using a Bandit-Based Approach	Wei Xia, Roland H. C. Yap	In this paper, rather than manually choosing/designing search heuristics, we propose the use of bandit-based learning techniques to automatically select search heuristics.
816	Learning Spatio-Temporal Features With Partial Expression Sequences for On-the-Fly Prediction	Wissam J. Baddar, Yong Man Ro	In this paper, we propose a new spatio-temporal feature learning method, which would allow prediction with partial sequences.
817	SEE: Towards Semi-Supervised End-to-End Scene Text Recognition	Christian Bartz, Haojin Yang, Christoph Meinel	In this paper we present SEE, a step towards semi-supervised neural networks for scene text detection and recognition, that can be optimized end-to-end.
818	Asymmetric Joint Learning for Heterogeneous Face Recognition	Bing Cao, Nannan Wang, Xinbo Gao, Jie Li	This paper proposes an asymmetric joint learning (AJL) approach to handle this issue.
819	Lateral Inhibition-Inspired Convolutional Neural Network for Visual Attention and Saliency Detection	Chunshui Cao, Yongzhen Huang, Zilei Wang, Liang Wang, Ninglong Xu, Tieniu Tan	In this paper, we propose to formulate lateral inhibition inspired by the related studies from neurobiology, and embed it into the top-down gradient computation of a general CNN for classification, i.e. only category-level information is used.
820	Transfer Adversarial Hashing for Hamming Space Retrieval	Zhangjie Cao, Mingsheng Long, Chao Huang, Jianmin Wang	This paper presents Transfer Adversarial Hashing (TAH), a new hybrid deep architecture that incorporates a pairwise t-distribution cross-entropy loss to learn concentrated hash codes and an adversarial network to align the data distributions between the source and target domains.
821	Temporal-Difference Learning With Sampling Baseline for Image Captioning	Hui Chen, Guiguang Ding, Sicheng Zhao, Jungong Han	In this paper, we utilize reinforcement learning method to train the image captioning model.
822	Order-Free RNN With Visual Attention for Multi-Label Classification	Shang-Fu Chen, Yi-Chen Chen, Chih-Kuan Yeh, Yu-Chiang Frank Wang	We propose a recurrent neural network (RNN) based model for image multi-label classification.
823	Learning a Wavelet-Like Auto-Encoder to Accelerate Deep Neural Networks	Tianshui Chen, Liang Lin, Wangmeng Zuo, Xiaonan Luo, Lei Zhang	In this work, aiming at a general and comprehensive way for neural network acceleration, we develop a Wavelet-like Auto-Encoder (WAE) that decomposes the original input image into two low-resolution channels (sub-images) and incorporate the WAE into the classification neural networks for joint training.
824	Recurrent Attentional Reinforcement Learning for Multi-Label Image Recognition	Tianshui Chen, Zhouxia Wang, Guanbin Li, Liang Lin	To resolve these issues, this paper proposes a recurrent attention reinforcement learning framework to iteratively discover a sequence of attentional and informative regions that are related to different semantic objects and further predict label scores conditioned on these regions.
825	MixedPeds: Pedestrian Detection in Unannotated Videos Using Synthetically Generated Human-Agents for Training	Ernest Cheung, Anson Wong, Aniket Bera, Dinesh Manocha	We present a new method for training pedestrian detectors on an unannotated set of images.
826	Self-View Grounding Given a Narrated 360° Video	Shih-Han Chou, Yi-Chun Chen, Kuo-Hao Zeng, Hou-Ning Hu, Jianlong Fu, Min Sun	We propose a novel Visual Grounding Model (VGM) to implicitly and efficiently predict the NFoVs given the video content and subtitles. To evaluate our method, we collect the first narrated 360° videos dataset and achieve state-of-the-art NFoV-grounding performance.
827	Using Syntax to Ground Referring Expressions in Natural Images	Volkan Cirik, Taylor Berg-Kirkpatrick, Louis-Philippe Morency	We introduce GroundNet, a neural network for referring expression recognition—the task of localizing (or grounding) in an image the object referred to by a natural language expression.
828	Acquiring Common Sense Spatial Knowledge Through Implicit Spatial Templates	Guillem Collell, Luc Van Gool, Marie-Francine Moens	We present two simple neural-based models that leverage annotated images and structured text to learn this task.
829	PixelLink: Detecting Scene Text via Instance Segmentation	Dan Deng, Haifeng Liu, Xuelong Li, Deng Cai	In this paper, PixelLink, a novel scene text detection algorithm based on instance segmentation, is proposed.
830	ExprGAN: Facial Expression Editing With Controllable Expression Intensity	Hui Ding, Kumar Sricharan, Rama Chellappa	To address these limitations, we propose an Expression Generative Adversarial Network (ExprGAN) for photo-realistic facial expression editing with controllable expression intensity.
831	A Deep Cascade Network for Unaligned Face Attribute Classification	Hui Ding, Hao Zhou, Shaohua Kevin Zhou, Rama Chellappa	In this paper, we propose a cascade network that simultaneously learns to localize face regions specific to attributes and performs attribute classification without alignment.
832	Auto-Balanced Filter Pruning for Efficient Convolutional Neural Networks	Xiaohan Ding, Guiguang Ding, Jungong Han, Sheng Tang	In this paper, we propose an iterative approach named Auto-balanced Filter Pruning, where we pre-train the network in an innovative auto-balanced way to transfer the representational capacity of its convolutional layers to a fraction of the filters, prune the redundant ones, then re-train it to restore the accuracy.
833	Hierarchical Nonlinear Orthogonal Adaptive-Subspace Self-Organizing Map Based Feature Extraction for Human Action Recognition	Yang Du, Chunfeng Yuan, Bing Li, Weiming Hu, Hao Yang, Zhikang Fu, Lili Zhao	In this paper, we propose a new hierarchical Nonlinear Orthogonal Adaptive-Subspace Self-Organizing Map(NOASSOM) to adaptively and learn effective features from data without supervision.
834	Self-Reinforced Cascaded Regression for Face Alignment	Xin Fan, Risheng Liu, Kang Huyan, Yuyao Feng, Zhongxuan Luo	In this paper, we propose a self-reinforced strategy that iteratively expands the quantity and improves the quality of training examples, thus upgrading the performance of cascaded regression itself.
835	Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation	Hao-Shu Fang, Yuanlu Xu, Wenguan Wang, Xiaobai Liu, Song-Chun Zhu	In this paper, we propose a pose grammar to tackle the problem of 3D human pose estimation. In learning, we develop a pose sample simulator to augment training samples in virtual camera views, which further improves our model generalizability.
836	Unravelling Robustness of Deep Learning Based Face Recognition Against Adversarial Attacks	Gaurav Goswami, Nalini Ratha, Akshay Agarwal, Richa Singh, Mayank Vatsa	In this paper, we attempt to unravel three aspects related to the robustness of DNNs for face recognition: (i) assessing the impact of deep architectures for face recognition in terms of vulnerabilities to attacks inspired by commonly observed distortions in the real world that are well handled by shallow learning methods along with learning based adversaries; (ii) detecting the singularities by characterizing abnormal filter response behavior in the hidden layers of deep networks; and (iii) making corrections to the processing pipeline to alleviate the problem.
837	Stack-Captioning: Coarse-to-Fine Learning for Image Captioning	Jiuxiang Gu, Jianfei Cai, Gang Wang, Tsuhan Chen	In this paper, we propose a coarse-to-fine multi-stage prediction framework for image captioning, composed of multiple decoders each of which operates on the output of the previous stage, producing increasingly refined image descriptions.
838	Hierarchical LSTM for Sign Language Translation	Dan Guo, Wengang Zhou, Houqiang Li, Meng Wang	To solve the issue, this paper proposes a hierarchical-LSTM (HLSTM) encoder-decoder model with visual content and word embedding for SLT.
839	Learning Coarse-to-Fine Structured Feature Embedding for Vehicle Re-Identification	Haiyun Guo, Chaoyang Zhao, Zhiwei Liu, Jinqiao Wang, Hanqing Lu	In this paper, we learn a structured feature embedding for vehicle re-ID with a novel coarse-to-fine ranking loss to pull images of the same vehicle as close as possible and achieve discrimination between images from different vehicles as well as vehicles from different vehicle models.
840	Residual Encoder Decoder Network and Adaptive Prior for Face Parsing	Tianchu Guo, Youngsung Kim, Hui Zhang, Deheng Qian, ByungIn Yoo, Jingtao Xu, Dongqing Zou, Jae-Joon Han, Changkyu Choi	In this paper, we propose a novel pixel-wise face parsing method called Residual Encoder Decoder Network (RED-Net), which combines a feature-rich encoder-decoder framework with adaptive prior mechanism.
841	Zero-Shot Learning With Attribute Selection	Yuchen Guo, Guiguang Ding, Jungong Han, Sheng Tang	Based on this observation, in this paper we propose to use a subset of attributes, instead of the whole set, for building ZSL models.
842	Doing the Best We Can With What We Have: Multi-Label Balancing With Selective Learning for Attribute Prediction	Emily M. Hand, Carlos Castillo, Rama Chellappa	To address this problem, we introduce a novel Selective Learning method for deep networks which adaptively balances the data in each batch according to the desired distribution for each label.
843	CMCGAN: A Uniform Framework for Cross-Modal Visual-Audio Mutual Generation	Wangli Hao, Zhaoxiang Zhang, He Guan	In this paper, we propose a Cross-Modal Cycle Generative Adversarial Network (CMCGAN) to handle cross-modal visual-audio mutual generation.
844	Integrating Both Visual and Audio Cues for Enhanced Video Caption	Wangli Hao, Zhaoxiang Zhang, He Guan	We propose three multimodal deep fusion strategies to maximize the benefits of visual-audio resonance information.
845	Merge or Not? Learning to Group Faces via Imitation Learning	Yue He, Kaidi Cao, Cheng Li, Chen Change Loy	In this study, we formulate a novel face grouping framework that learns clustering strategy from ground-truth simulated behavior.
846	Unsupervised Deep Learning of Mid-Level Video Representation for Action Recognition	Jingyi Hou, Xinxiao Wu, Jin Chen, Jiebo Luo, Yunde Jia	In this paper, we propose an unsupervised deep learning method which employs unlabeled local spatial-temporal volumes extracted from action videos to learn midlevel video representation for action recognition.
847	Dual-Reference Face Retrieval	BingZhang Hu, Feng Zheng, Ling Shao	To tackle this problem, we propose a dual reference face retrieval framework in this paper, where the system takes two inputs: an identity reference image which indicates the target identity and an age reference image which reflects the target age.
848	Facial Landmarks Detection by Self-Iterative Regression Based Landmarks-Attention Network	Tao Hu, Honggang Qi, Jizheng Xu, Qingming Huang	In this paper, we develop a Self-Iterative Regression (SIR) framework to improve the model efficiency.
849	Learning Adaptive Hidden Layers for Mobile Gesture Recognition	Ting-Kuei Hu, Yen-Yu Lin, Pi-Cheng Hsiu	We tackle these issues by introducing a new network layer, called an adaptive hidden layer (AHL), to generalize a hidden layer in deep neural networks and dynamically generate an activation map conditioned on the input.
850	Recurrently Aggregating Deep Features for Salient Object Detection	Xiaowei Hu, Lei Zhu, Jing Qin, Chi-Wing Fu, Pheng-Ann Heng	In this paper, we develop a novel deep saliency network equipped with recurrently aggregated deep features (RADF) to more accurately detect salient objects from an image by fully exploiting the complementary saliency information captured in different layers.
851	SAP: Self-Adaptive Proposal Model for Temporal Action Detection Based on Reinforcement Learning	Jingjia Huang, Nannan Li, Tao Zhang, Ge Li, Tiejun Huang, Wen Gao	In this paper, we propose a Self-Adaptive Proposal (SAP) model that learns to find actions through continuously adjusting the temporal bounds in a self-adaptive way.
852	Learning to Guide Decoding for Image Captioning	Wenhao Jiang, Lin Ma, Xinpeng Chen, Hanwang Zhang, Wei Liu	In this paper, we propose an extension of the encoder-decoder framework by adding a component called guiding network.
853	Deep Low-Resolution Person Re-Identification	Jiening Jiao, Wei-Shi Zheng, Ancong Wu, Xiatian Zhu, Shaogang Gong	In this work, we address this problem by developing a novel approach called Super-resolution and Identity joiNt learninG (SING) to simultaneously optimise image super-resolution and person re-id matching.
854	Co-Domain Embedding Using Deep Quadruplet Networks for Unseen Traffic Sign Recognition	Junsik Kim, Seokju Lee, Tae-Hyun Oh, In So Kweon	We propose a novel feature embedding scheme for unseen class classification when the representative class template is given.
855	Multispectral Transfer Network: Unsupervised Depth Estimation for All-Day Vision	Namil Kim, Yukyung Choi, Soonmin Hwang, In So Kweon	Beyond these limitations, the innovation introduced here is a multispectral solution in the form of depth estimation from a thermal sensor without an additional depth sensor.Based on an analysis of multispectral properties and the relevance to depth predictions, we propose an efficient and novel multi-task framework called the Multispectral Transfer Network (MTN) to estimate a depth image from a single thermal image.
856	Generating Triples With Adversarial Networks for Scene Graph Construction	Matthew Klawonn, Eric Heim	In this paper we propose a method, based on recent advancements in Generative Adversarial Networks, to overcome these deficiencies.
857	Action Prediction From Videos via Memorizing Hard-to-Predict Samples	Yu Kong, Shangqian Gao, Bin Sun, Yun Fu	In this paper, we propose a mem-LSTM model to predict actions in the early stage, in which a memory module is introduced to record several “hard-to-predict” samples and a variety of early observations.
858	Robust Collaborative Discriminative Learning for RGB-Infrared Tracking	Xiangyuan Lan, Mang Ye, Shengping Zhang, Pong C. Yuen	To address these issues, this paper proposes a novel and optimal discriminative learning framework for multi-modality tracking.
859	End-to-End United Video Dehazing and Detection	Boyi Li, Xiulian Peng, Zhangyang Wang, Jizheng Xu, Dan Feng	In this paper, we propose an End-to-End Video Dehazing Network (EVD-Net), to exploit the temporal consistency between consecutive video frames.
860	Weakly Supervised Salient Object Detection Using Image Labels	Guanbin Li, Yuan Xie, Liang Lin	In this paper, we note that superior salient object detection can be obtained by iteratively mining and correcting the labeling ambiguity on saliency maps from traditional unsupervised methods.
861	Brute-Force Facial Landmark Analysis With a 140,000-Way Classifier	Mengtian Li, Laszlo Jeni, Deva Ramanan	We propose a simple approach to visual alignment, focusing on the illustrative task of facial landmark estimation.
862	DF	Yabei Li, Junge Zhang, Yanhua Cheng, Kaiqi Huang, Tieniu Tan	To address these problems, this paper proposes a Discriminative Feature Learning and Fusion Network (DF2Net) with two-stage training.
863	Deep Semantic Structural Constraints for Zero-Shot Learning	Yan Li, Zhen Jia, Junge Zhang, Kaiqi Huang, Tieniu Tan	In this paper, we propose an end-to-end trainable Deep Semantic Structural Constraints model to address this issue.
864	Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification	Yi Li, Lingxiao Song, Xiang Wu, Ran He, Tieniu Tan	This paper proposes a learning from generation approach for makeup-invariant face verification by introducing a bi-level adversarial network (BLAN).
865	Video Generation From Text	Yitong Li, Martin Renqiang Min, Dinghan Shen, David Carlson, Lawrence Carin	We tackle this problem by training a conditional generative model to extract both static and dynamic information from text.
866	R-FCN++: Towards Accurate Region-Based Fully Convolutional Networks for Object Detection	Zeming Li, Yilun Chen, Gang Yu, Yangdong Deng	In this paper, we propose R-FCN++ to address this issue in two-fold: first we involve Global Context Module to improve the classification score maps by adopting large, separable convolutional kernels.
867	Multi-Rate Gated Recurrent Convolutional Networks for Video-Based Pedestrian Re-Identification	Zhihui Li, Lina Yao, Feiping Nie, Dingwen Zhang, Min Xu	Matching pedestrians across multiple camera views has attracted lots of recent research attention due to its apparent importance in surveillance and security applications.While most existing works address this problem in a still-image setting, we consider the more informative and challenging video-based person re-identification problem, where a video of a pedestrian as seen in one camera needs to be matched to a gallery of videos captured by other non-overlapping cameras.
868	Cross-View Person Identification by Matching Human Poses Estimated With Confidence on Each Body Joint	Guoqiang Liang, Xuguang Lan, Kang Zheng, Song Wang, Nanning Zheng	In this paper, we introduce a new metric of confidence to the 3D human pose estimation and show that the combination of the inaccurately estimated human pose and the inferred confidence metric can be used to boost the CVPI performance—the estimated pose information can be integrated to the appearance and motion features to achieve the new state-of-the-art CVPI performance.
869	Visual Relationship Detection With Deep Structural Ranking	Kongming Liang, Yuhong Guo, Hong Chang, Xilin Chen	In this paper, we propose a novel framework, called Deep Structural Ranking, for visual relationship detection.
870	Tracking Occluded Objects and Recovering Incomplete Trajectories by Reasoning About Containment Relations and Human Actions	Wei Liang, Yixin Zhu, Song-Chun Zhu	By explicitly distinguishing these two causes of occlusions, the proposed algorithm formulates tracking problem as a network flow representation encoding containment relations and their changes. To quantitatively evaluate our algorithm, we collect a new occluded object dataset captured by Kinect sensor, including a set of RGB-D videos and human skeletons with multiple actors, various objects, and different changes of containment relations.
871	Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction	Chen-Hsuan Lin, Chen Kong, Simon Lucey	In this paper, we propose a novel 3D generative modeling framework to efficiently generate object shapes in the form of dense point clouds.
872	Multi-Scale Face Restoration With Sequential Gating Ensemble Network	Jianxin Lin, Tiankuang Zhou, Zhibo Chen	In this paper, we present a Sequential Gating Ensemble Network (SGEN) for multi-scale face restoration issue.
873	Action Recognition With Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion	Weiyao Lin, Chongyang Zhang, Ke Lu, Bin Sheng, Jianxin Wu, Bingbing Ni, Xin Liu, Hongkai Xiong	In this paper, we propose a novel deep-based framework for action recognition, which improves the recognition accuracy by: 1) deriving more precise features for representing actions, and 2) reducing the asynchrony between different information streams.
874	T-C3D: Temporal Convolutional 3D Network for Real-Time Action Recognition	Kun Liu, Wu Liu, Chuang Gan, Mingkui Tan, Huadong Ma	To address these problems, we propose a new real-time action recognition architecture, called Temporal Convolutional 3D Network (T-C3D), which learns video action representations in a hierarchical multi-granularity manner.
875	Cross-Domain Human Parsing via Adversarial Feature and Label Adaptation	Si Liu, Yao Sun, Defa Zhu, Guanghui Ren, Yu Chen, Jiashi Feng, Jizhong Han	In this paper, we explore a new and challenging cross-domain human parsing problem: taking the benchmark dataset with extensive pixel-wise labeling as the source domain, how to obtain a satisfactory parser on a new target domain without requiring any additional manual labeling?
876	Char-Net: A Character-Aware Neural Network for Distorted Scene Text Recognition	Wei Liu, Chaofeng Chen, Kwan-Yee K. Wong	In this paper, we present a Character-Aware Neural Network (Char-Net) for recognizing distorted scene text.
877	Semi-Supervised Bayesian Attribute Learning for Person Re-Identification	Wenhe Liu, Xiaojun Chang, Ling Chen, Yi Yang	To overcome these limitations, we propose a novel algorithm for person re-ID, called semi-supervised Bayesian attribute learning.
878	A Cascaded Inception of Inception Network With Attention Modulated Feature Fusion for Human Pose Estimation	Wentao Liu, Jie Chen, Cheng Li, Chen Qian, Xiao Chu, Xiaolin Hu	This paper presents three novel techniques step by step to efficiently utilize different levels of features for human pose estimation.
879	Dictionary Learning Inspired Deep Network for Scene Recognition	Yang Liu, Qingchao Chen, Wei Chen, Ian Wassell	In this paper, we replace the conventional FCL and ReLu with a new dictionary learning layer, that is composed of a finite number of recurrent units to simultaneously enhance the sparse representation and discriminative abilities of features via the determination of optimal dictionaries.
880	PoseHD: Boosting Human Detectors Using Human Pose Information	Zhijian Liu, Bowen Pan, Yuliang Xiu, Cewu Lu	In order to address the two main challenges in precision improvement, i.e., i) hard background instances and ii) redundant partial proposals, we propose the novel PoseHD framework, a top-down pose-based approach on the basis of an arbitrary state-of-the-art human detector.
881	SqueezedText: A Real-Time Scene Text Recognition by Binary Convolutional Encoder-Decoder Network	Zichuan Liu, Yixing Li, Fengbo Ren, Wang Ling Goh, Hao Yu	A new approach for real-time scene text recognition is proposed in this paper.
882	Multimodal Keyless Attention Fusion for Video Classification	Xiang Long, Chuang Gan, Gerard de Melo, Xiao Liu, Yandong Li, Fu Li, Shilei Wen	We propose Keyless Attention as an elegant and efficient means to more effectively account for the sequential nature of the data.
883	Towards Affordable Semantic Searching: Zero-Shot Retrieval via Dominant Attributes	Yang Long, Li Liu, Yuming Shen, Ling Shao	Given the above challenges, this paper studies the Zero-shot Retrieval problem that aims for instance-level image search using only a few dominant attributes.
884	Co-Attending Free-Form Regions and Detections With Multi-Modal Multiplicative Feature Embedding for Visual Question Answering	Pan Lu, Hongsheng Li, Wei Zhang, Jianyong Wang, Xiaogang Wang	In this paper, we propose a novel deep neural network for VQA that integrates both attention mechanisms.
885	Unsupervised Articulated Skeleton Extraction From Point Set Sequences Captured by a Single Depth Camera	Xuequan Lu, Honghua Chen, Sai-Kit Yeung, Zhigang Deng, Wenzhi Chen	To address this issue, we propose a novel, unsupervised approach consisting of three contributions (steps): (i) a non-rigid point set registration algorithm to first build one-to-one point correspondences among the frames of a sequence; (ii) a skeletal structure extraction algorithm to generate a skeleton with reasonable numbers of joints and bones; (iii) a skeleton joints estimation algorithm to achieve accurate joints.
886	Curve-Structure Segmentation From Depth Maps: A CNN-Based Approach and Its Application to Exploring Cultural Heritage Objects	Yuhang Lu, Jun Zhou, Jing Wang, Jun Chen, Karen Smith, Colin Wilder, Song Wang	Different from most classical low-level image segmentation methods that are known to be very sensitive to the noise and occlusions, we propose a new supervised learning algorithm based on Convolutional Neural Network (CNN) to implicitly learn and utilize more curve geometry and pattern information for addressing this challenging problem.
887	Multi-Channel Pyramid Person Matching Network for Person Re-Identification	Chaojie Mao, Yingming Li, Yaqing Zhang, Zhongfei Zhang, Xi Li	In this work, we present a Multi-Channel deep convolutional Pyramid Person Matching Network (MC-PPMN) based on the combination of the semantic-components and the color-texture distributions to address the problem of person re-identification.
888	UnFlow: Unsupervised Learning of Optical Flow With a Bidirectional Census Loss	Simon Meister, Junhwa Hur, Stefan Roth	Inspired by classical energy-based optical flow methods, we design an unsupervised loss based on occlusion-aware bidirectional flow estimation and the robust census transform to circumvent the need for ground truth flow.
889	Weakly Supervised Collective Feature Learning From Curated Media	Yusuke Mukuta, Akisato Kimura, David B. Adrian, Zoubin Ghahramani	We instead present a new paradigm for learning discriminative features by making full use of the human curation process on social networking services (SNSs).
890	Asking Friendly Strangers: Non-Semantic Attribute Transfer	Nils Murrugarra-Llerena, Adriana Kovashka	Instead, we propose to perform non-semantic transfer from attributes that may be in different domains, hence they have no semantic relation to the target attributes.
891	Spatial as Deep: Spatial CNN for Traffic Scene Understanding	Xingang Pan, Jianping Shi, Ping Luo, Xiaogang Wang, Xiaoou Tang	In this paper, we propose Spatial CNN (SCNN), which generalizes traditional deep layer-by-layer convolutions to slice-by-slice convolutions within feature maps, thus enabling message passings between pixels across rows and columns in a layer.
892	Adaptive Feature Abstraction for Translating Video to Text	Yunchen Pu, Martin Renqiang Min, Zhe Gan, Lawrence Carin	We propose a new approach for generating adaptive spatiotemporal representations of videos for the captioning task.
893	Scene-Centric Joint Parsing of Cross-View Videos	Hang Qi, Yuanlu Xu, Tao Yuan, Tianfu Wu, Song-Chun Zhu	In this paper, we introduce a joint parsing framework that integrates view-centric proposals into scene-centric parse graphs that represent a coherent scene-centric understanding of cross-view scenes.
894	Exploring Human-Like Attention Supervision in Visual Question Answering	Tingting Qiao, Jianfeng Dong, Duanqing Xu	In this work, we aim to address the problem of adding attention supervision to VQA models.
895	RAN4IQA: Restorative Adversarial Nets for No-Reference Image Quality Assessment	Hongyu Ren, Diqi Chen, Yizhou Wang	Inspired by the free-energy brain theory, which implies that human visual system (HVS) tends to reduce uncertainty and restore perceptual details upon seeing a distorted image, we propose restorative adversarial net (RAN), a GAN-based model for no-reference image quality assessment (NR-IQA).
896	Extreme Low Resolution Activity Recognition With Multi-Siamese Embedding Learning	Michael S. Ryoo, Kiyoon Kim, Hyun Jong Yang	This paper presents an approach for recognizing human activities from extreme low resolution (e.g., 16×12) videos.
897	Top-Down Feedback for Crowd Counting Convolutional Neural Network	Deepak Babu Sam, R. Venkatesh Babu	Hence, we propose top-down feedback to correct the initial prediction of the CNN.
898	Game of Sketches: Deep Recurrent Models of Pictionary-Style Word Guessing	Ravi Kiran Sarvadevabhatla, Shiv Surya, Trisha Mittal, R. Venkatesh Babu	To mimic Pictionary-style guessing, we propose a deep neural model which generates guess-words in response to temporally evolving human-drawn sketches.
899	DLPaper2Code: Auto-Generation of Code From Deep Learning Research Papers	Akshay Sethi, Anush Sankaran, Naveen Panwar, Shreya Khare, Senthil Mani	To address these challenges, we propose a novel extensible approach, DLPaper2Code, to extract and understand deep learning design flow diagrams and tables available in a research paper and convert them to an abstract computational graph. To evaluate our approach, we create a simulated dataset with over 216,000 valid deep learning design flow diagrams using a manually defined grammar.
900	Region-Based Quality Estimation Network for Large-Scale Person Re-Identification	Guanglu Song, Biao Leng, Yu Liu, Congrui Hetang, Shaofan Cai	To achieve this, we propose a novel Region-based Quality Estimation Network (RQEN), in which an ingenious training mechanism enables the effective learning to extract the complementary region-based information between different frames.
901	Adversarial Discriminative Heterogeneous Face Recognition	Lingxiao Song, Man Zhang, Xiang Wu, Ran He	This paper proposes an adversarial discriminative feature learning framework to close the sensing gap via adversarial learning on both raw-pixel space and compact feature space.
902	Learning Binary Residual Representations for Domain-Specific Video Streaming	Yi-Hsuan Tsai, Ming-Yu Liu, Deqing Sun, Ming-Hsuan Yang, Jan Kautz	Based on this hypothesis, we propose a novel video compression pipeline.
903	Diverse Beam Search for Improved Description of Complex Scenes	Ashwin K. Vijayakumar, Michael Cogswell, Ramprasaath R. Selvaraju, Qing Sun, Stefan Lee, David Crandall, Dhruv Batra	To address this shortcoming, we propose Diverse Beam Search (DBS), a diversity promoting alternative to BS for approximate inference.
904	Movie Question Answering: Remembering the Textual Cues for Layered Visual Contents	Bo Wang, Youjiang Xu, Yahong Han, Richang Hong	In this paper, for answering questions about movies, we put forward a Layered Memory Network (LMN) that represents frame-level and clip-level movie content by the Static Word Memory module and the Dynamic Subtitle Memory module, respectively.
905	Supervised Deep Hashing for Hierarchical Labeled Data	Dan Wang, Heyan Huang, Chi Lu, Bo-Si Feng, Guihua Wen, Liqiang Nie, Xian-Ling Mao	To tackle the aforementioned problems, in this paper, we propose a novel deep hashing method, called supervised hierarchical deep hashing (SHDH), to perform hash code learning for hierarchical labeled data.
906	Show, Reward and Tell: Automatic Generation of Narrative Paragraph From Photo Stream by Adversarial Training	Jing Wang, Jianlong Fu, Jinhui Tang, Zechao Li, Tao Mei	To deal with these challenges, we propose a sequence-to-sequence modeling approach with reinforcement learning and adversarial training.
907	Cooperative Training of Deep Aggregation Networks for RGB-D Action Recognition	Pichao Wang, Wanqing Li, Jun Wan, Philip Ogunbona, Xinwang Liu	The proposed method was extensively evaluated on two large RGB-D action recognition datasets, ChaLearn LAP IsoGD and NTU RGB+D datasets, and one small dataset, SYSU 3D HOI, and achieved state-of-the-art results.
908	Temporal-Enhanced Convolutional Network for Person Re-Identification	Yang Wu, Jie Qiu, Jun Takamatsu, Tsukasa Ogasawara	We propose a new neural network called Temporal-enhanced Convolutional Network (T-CN) for video-based person reidentification.
909	Transferable Semi-Supervised Semantic Segmentation	Huaxin Xiao, Yunchao Wei, Yu Liu, Maojun Zhang, Jiashi Feng	In this paper, we propose a novel transferable semi-supervised semantic segmentation model that can transfer the learned segmentation knowledge from a few strong categories with pixel-level annotations to unseen weak categories with only image-level annotations, significantly broadening the applicable territory of deep segmentation models.
910	Emphasizing 3D Properties in Recurrent Multi-View Aggregation for 3D Shape Retrieval	Cheng Xu, Biao Leng, Cheng Zhang, Xiaochen Zhou	To resolve this problem, we propose an encoder-decoder recurrent feature aggregation network (ERFA-Net) to emphasize the 3D properties of 3D shapes in multi-view features aggregation.
911	Unsupervised Part-Based Weighting Aggregation of Deep Convolutional Features for Image Retrieval	Jian Xu, Cunzhao Shi, Chengzuo Qi, Chunheng Wang, Baihua Xiao	In this paper, we propose a simple but effective semantic part-based weighting aggregation (PWA) for image retrieval.
912	Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition	Sijie Yan, Yuanjun Xiong, Dahua Lin	In this work, we propose a novel model of dynamic skeletons called Spatial-Temporal Graph Convolutional Networks (ST-GCN), which moves beyond the limitations of previous methods by automatically learning both the spatial and temporal patterns from data.
913	Domain-Shared Group-Sparse Dictionary Learning for Unsupervised Domain Adaptation	Baoyao Yang, Andy J. Ma, Pong C. Yuen	In this paper, we propose a new criterion of domain-shared group-sparsity that is an equivalent condition for conditional distribution alignment.
914	Multi-Scale Bidirectional FCN for Object Skeleton Extraction	Fan Yang, Xin Li, Hong Cheng, Yuxiao Guo, Leiting Chen, Jianping Li	Therefore, we propose a novel network architecture, Multi-Scale Bidirectional Fully Convolutional Network (MSB-FCN), to better capture and consolidate multi-scale high-level context information for object skeleton detection.
915	Understanding Image Impressiveness Inspired by Instantaneous Human Perceptual Cues	Jufeng Yang, Yan Sun, Jie Liang, Yong-Liang Yang, Ming-Ming Cheng	In this paper, we propose a novel image property, called impressiveness, that measures how images impress people with a short-term contact. To achieve this, we first collect three datasets in various domains, which are labeled according to the instantaneous sensation of the annotators.
916	Exploring Temporal Preservation Networks for Precise Temporal Action Localization	Ke Yang, Peng Qiao, Dongsheng Li, Shaohe Lv, Yong Dou	In this paper, we propose an elegant and powerful Temporal Preservation Convolutional (TPC) Network that equips 3D ConvNets with TPC filters.
917	Towards Perceptual Image Dehazing by Physics-Based Disentanglement and Adversarial Training	Xitong Yang, Zheng Xu, Jiebo Luo	In this work, we propose Disentangled Dehazing Network, an end-to-end model that generates realistic haze-free images using only unpaired supervision.
918	Unsupervised Learning of Geometry From Videos With Edge-Aware Depth-Normal Consistency	Zhenheng Yang, Peng Wang, Wei Xu, Liang Zhao, Ramakant Nevatia	In this paper, we propose to use surface normal representation for unsupervised depth estimation framework.
919	Hierarchical Discriminative Learning for Visible Thermal Person Re-Identification	Mang Ye, Xiangyuan Lan, Jiawei Li, Pong C. Yuen	Therefore, we propose a hierarchical cross-modality matching model by jointly optimizing the modality-specific and modality-shared metrics.
920	Co-Saliency Detection Within a Single Image	Hongkai Yu, Kang Zheng, Jianwu Fang, Hao Guo, Wei Feng, Song Wang	In this paper, we investigate a new problem of co-saliency detection within a single image, i.e., detecting within-image co-saliency. In the experiment, we collect a new dataset of 364 color images with within-image cosaliency.
921	Deep Stereo Matching With Explicit Cost Aggregation Sub-Architecture	Lidong Yu, Yucheng Wang, Yuwei Wu, Yunde Jia	In this paper, we present a learning-based cost aggregation method for stereo matching by a novel sub-architecture in the end-to-end trainable pipeline.
922	A Deep Ranking Model for Spatio-Temporal Highlight Detection From a 360◦ Video	Youngjae Yu, Sangho Lee, Joonil Na, Jaeyun Kang, Gunhee Kim	We propose a novel deep ranking model named as Composition View Score (CVS) model, which produces a spherical score map of composition per video segment, and determines which view is suitable for highlight via a sliding window kernel at inference.
923	Mix-and-Match Tuning for Self-Supervised Semantic Segmentation	Xiaohang Zhan, Ziwei Liu, Ping Luo, Xiaoou Tang, Chen Change Loy	In this study, we overcome this limitation by incorporating a “mix-and-match” (M&M) tuning stage in the self-supervision pipeline.
924	Audio Visual Attribute Discovery for Fine-Grained Object Recognition	Hua Zhang, Xiaochun Cao, Rui Wang	Different from these existing methods based on the visual supervisions, in this paper, we introduce a novel feature named audio visual attributes via discovering the correlations between the visual and audio representations.
925	Kill Two Birds With One Stone: Weakly-Supervised Neural Network for Image Annotation and Tag Refinement	Junjie Zhang, Qi Wu, Jian Zhang, Chunhua Shen, Jianfeng Lu	In this paper, we propose to learn an image annotation model and refine the user-provided tags simultaneously in a weakly-supervised manner.
926	Face Sketch Synthesis From Coarse to Fine	Mingjin Zhang, Nannan Wang, Yunsong Li, Ruxin Wang, Xinbo Gao	In this paper, by imitating the coarse-to-fine drawing process of artists, we propose a novel face sketch synthesis framework consisting of a coarse stage and a fine stage.
927	Accelerated Training for Massive Classification via Dynamic Class Selection	Xingcheng Zhang, Lei Yang, Junjie Yan, Dahua Lin	We present a new method to tackle this problem.
928	FLIC: Fast Linear Iterative Clustering With Active Search	Jiaxing Zhao, Bo Ren, Qibin Hou, Ming-Ming Cheng, Paul Rosin	In this paper, we reconsider the clustering problem for image over-segmentation from a new perspective.
929	Deep Reinforcement Learning for Unsupervised Video Summarization With Diversity-Representativeness Reward	Kaiyang Zhou, Yu Qiao, Tao Xiang	In this paper, we formulate video summarization as a sequential decision-making process and develop a deep summarization network (DSN) to summarize videos.
930	Towards Automatic Learning of Procedures From Web Instructional Videos	Luowei Zhou, Chenliang Xu, Jason J. Corso	To answer this question, we introduce the problem of procedure segmentation—to segment a video procedure into category-independent procedure segments. Given that no large-scale dataset is available for this problem, we collect a large-scale procedure segmentation dataset with procedure segments temporally localized and described; we use cooking videos and name the dataset YouCook2.
931	Graph Correspondence Transfer for Person Re-Identification	Qin Zhou, Heng Fan, Shibao Zheng, Hang Su, Xinzhe Li, Shuang Wu, Haibin Ling	In this paper, we propose a graph correspondence transfer (GCT) approach for person re-identification.
932	Progressive Cognitive Human Parsing	Bingke Zhu, Yingying Chen, Ming Tang, Jinqiao Wang	In this paper, we develop an end-to-end progressive cognitive network to segment human parts.
933	Learning Adversarial 3D Model Generation With 2D Image Enhancer	Jing Zhu, Jin Xie, Yi Fang	In this paper, we have developed a novel GAN-based deep neural network to obtain a better latent space for the generation of 3D models.
934	Deep Structured Learning for Visual Relationship Detection	Yaohui Zhu, Shuqiang Jiang	In this paper, we introduce deep structured learning for visual relationship detection.
935	HCVRD: A Benchmark for Large-Scale Human-Centered Visual Relationship Detection	Bohan Zhuang, Qi Wu, Chunhua Shen, Ian Reid, Anton van den Hengel	We propose a webly-supervised approach to these problems and demonstrate that the proposed model provides a strong baseline on our HCVRD dataset. In addressing this problem we first construct a large-scale human-centric visual relationship detection dataset (HCVRD), which provides many more types of relationship annotations (nearly 10K categories) than the previous released datasets.
936	3D Box Proposals From a Single Monocular Image of an Indoor Scene	Wei Zhuo, Mathieu Salzmann, Xuming He, Miaomiao Liu	In this paper, we therefore introduce an approach to generating 3D box proposals from a single monocular RGB image.
937	Understanding Human Behaviors in Crowds by Imitating the Decision-Making Process	Haosheng Zou, Hang Su, Shihong Song, Jun Zhu	In this paper, we propose a novel framework of Social-Aware Generative Adversarial Imitation Learning (SA-GAIL) to mimic the underlying decision-making process of pedestrians in crowds.