Paper Digest: IJCAI 2018 Highlights

July 12, 2018October 9, 2019 admin

International Joint Conference on Artificial Intelligence (IJCAI) is one of the top artificial intelligence conferences in the world. In 2018, it is to be held in Stockholm, Sweden. There were more than 3,470 paper submissions, of which 710 were accepted.

To help AI community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights / summaries to quickly get the main idea of each paper.

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

Paper Digest Team
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TABLE 1: IJCAI 2018 Papers

	Title	Authors	Highlight
1	Robust Norm Emergence by Revealing and Reasoning about Context: Socially Intelligent Agents for Enhancing Privacy	Nirav Ajmeri, Hui Guo, Pradeep K. Murukannaiah, Munindar P. Singh	We investigate via simulation the benefits of enriched interactions where deviating agents share selected elements of their contexts.
2	Synthesis of Controllable Nash Equilibria in Quantitative Objective Game	Shaull Almagor, Orna Kupferman, Giuseppe Perelli	In this paper, we extend rational synthesis to LTL[F] — an extension of LTL by quality operators.
3	Comparing Approximate Relaxations of Envy-Freeness	Georgios Amanatidis, Georgios Birmpas, Vangelis Markakis	We focus on four such notions, namely envy-freeness up to one good (EF1), envy-freeness up to any good (EFX), maximin share fairness (MMS), and pairwise maximin share fairness (PMMS).
4	Reasoning about Consensus when Opinions Diffuse through Majority Dynamics	Vincenzo Auletta, Diodato Ferraioli, Gianluigi Greco	Reasoning about Consensus when Opinions Diffuse through Majority Dynamics
5	Egalitarian Committee Scoring Rules	Haris Aziz, Piotr Faliszewski, Bernard Grofman, Arkadii Slinko, Nimrod Talmon	We introduce and study the class of egalitarian variants of committee scoring rules, where instead of summing up the scores that voters assign to committees—as is done in the utilitarian variants—the score of a committee is taken to be the lowest score assigned to it by any voter.
6	Truthful Fair Division without Free Disposal	Xiaohui Bei, Guangda Huzhang, Warut Suksompong	In the present work, we remove this assumption and focus on mechanisms that always allocate the entire resource.
7	Accountable Approval Sorting	Khaled Belahcene, Yann Chevaleyre, Christophe Labreuche, Nicolas Maudet, Vincent Mousseau, Wassila Ouerdane	We consider decision situations in which a set of points of view (voters, criteria) are to sort a set of candidates to ordered categories (Good/Bad).
8	Alternating-time Temporal Logic on Finite Traces	Francesco Belardinelli, Alessio Lomuscio, Aniello Murano, Sasha Rubin	We introduce a semantics for Alternating-time Temporal Logic (for both perfect and imperfect recall) and its branching-time fragments in which paths are finite instead of infinite.
9	Managing Communication Costs under Temporal Uncertainty	Nikhil Bhargava, Christian Muise, Tiago Vaquero, Brian Williams	In this paper, we address the question of how to choose communication protocols that guarantee the feasibility of the original temporal plan subject to some cost associated with that communication.
10	Fair Division Under Cardinality Constraints	Arpita Biswas, Siddharth Barman	We consider the problem of fairly allocating indivisible goods, among agents, under cardinality constraints and additive valuations.
11	Big City vs. the Great Outdoors: Voter Distribution and How It Affects Gerrymandering	Allan Borodin, Omer Lev, Nisarg Shah, Tyrone Strangway	Along the way, we propose a definition of the gerrymandering power of a party, and an algorithmic approach for near-optimal gerrymandering in large instances.
12	Non-decreasing Payment Rules for Combinatorial Auctions	Vitor Bosshard, Ye Wang, Sven Seuken	In this paper, we introduce non-decreasing payment rules.
13	An Analytical and Experimental Comparison of Maximal Lottery Schemes	Florian Brandl, Felix Brandt, Christian Stricker	We prove that C2-ML schemes are the only Pareto efficient—but also among the most manipulable—ML schemes.
14	Solving Patrolling Problems in the Internet Environment	Tomas Brazdil, Antonin Kucera, Vojtech Rehak	We propose an algorithm for constructing efficient patrolling strategies in the Internet environment, where the protected targets are nodes connected to the network and the patrollers are software agents capable of detecting/preventing undesirable activities on the nodes.
15	Combinatorial Auctions via Machine Learning-based Preference Elicitation	Gianluca Brero, Benjamin Lubin, Sven Seuken	We address this problem by introducing a machine learning-based elicitation algorithm to identify which values to query from the bidders.
16	Pairwise Liquid Democracy	Markus Brill, Nimrod Talmon	Based on the framework of distance rationalization, we introduce novel variants of voting rules that are tailored to the liquid democracy context.
17	Multiwinner Voting with Fairness Constraints	L. Elisa Celis, Lingxiao Huang, Nisheeth K. Vishnoi	We introduce an algorithmic framework for multiwinner voting problems when there is an additional requirement that the selected subset should be “fair” with respect to a given set of attributes.
18	An FPTAS for Computing Nash Equilibrium in Resource Graph Games	Hau Chan, Albert Xin Jiang	We consider the problem of computing a mixed-strategy Nash equilibrium (MSNE) in resource graph games (RGGs), a compact representation for games with an exponential number of strategies.
19	Vocabulary Alignment for Collaborative Agents: a Study with Real-World Multilingual How-to Instructions	Paula Chocron, Paolo Pareti	Previous work proposed a technique to infer alignments between different vocabularies that uses only information about the tasks being executed, without any external resource.
20	Bidding in Periodic Double Auctions Using Heuristics and Dynamic Monte Carlo Tree Search	Moinul Morshed Porag Chowdhury, Christopher Kiekintveld, Son Tran, William Yeoh	We present a general bidding strategy for PDAs based on forecasting clearing prices and using Monte Carlo Tree Search (MCTS) to plan a bidding strategy across multiple time periods.
21	When Does Diversity of Agent Preferences Improve Outcomes in Selfish Routing?	Richard Cole, Thanasis Lianeas, Evdokia Nikolova	Our main contributions are: 1) A participant-oriented measure of cost in the presence of agent diversity; 2) A full characterization of those network topologies for which diversity always helps, for all latency functions and demands.
22	Computing the Schulze Method for Large-Scale Preference Data Sets	Theresa Csar, Martin Lackner, Reinhard Pichler	In this paper, we develop a highly optimised algorithm for computing the Schulze method with Pregel, a framework for massively parallel computation of graph problems, and demonstrate its applicability for large preference data sets.
23	Facility Reallocation on the Line	Bart de Keijzer, Dominik Wojtczak	We consider a multi-stage facility reallocation problems on the real line, where a facility is being moved between stages based on the locations reported by n agents.
24	An Operational Semantics for a Fragment of PRS	Lavindra de Silva, Felipe Meneguzzi, Brian Logan	We prove key properties of the semantics relating to PRS-specific programming constructs, and show that even the fragment of PRS we consider is strictly more expressive than the plan constructs found in typical BDI languages.
25	A Structural Approach to Activity Selection	Eduard Eiben, Robert Ganian, Sebastian Ordyniak	Here we introduce and study the Comprehensive Activity Selection Problem, which naturally generalizes both of these problems.
26	Negotiation Strategies for Agents with Ordinal Preferences	Sefi Erlich, Noam Hazon, Sarit Kraus	In this work we concentrate on negotiation with ordinal preferences over a finite set of outcomes.
27	Opinion Diffusion and Campaigning on Society Graphs	Piotr Faliszewski, Rica Gonen, Martin Koutecký, Nimrod Talmon	We study the effects of campaigning, where the society is partitioned into voter clusters and a diffusion process propagates opinions in a network connecting those clusters.
28	On the Complexity of Chore Division	Alireza Farhadi, MohammadTaghi Hajiaghayi	In this paper, we show that chore division and cake cutting problems are closely related to each other and provide a tight lower bound for proportional chore division.
29	Trembling-Hand Perfection in Extensive-Form Games with Commitment	Gabriele Farina, Alberto Marchesi, Christian Kroer, Nicola Gatti, Tuomas Sandholm	We initiate the study of equilibrium refinements based on trembling-hand perfection in extensive-form games with commitment strategies, that is, where one player commits to a strategy first.
30	Probabilistic Verification for Obviously Strategyproof Mechanisms	Diodato Ferraioli, Carmine Ventre	To this aim, we define a model of probabilistic verification wherein agents are caught misbehaving with a certain probability, and show how OSP mechanisms can implement every social choice function at the cost of either imposing very large fines or verifying a linear number of agents.
31	On Fair Price Discrimination in Multi-Unit Markets	Michele Flammini, Manuel Mauro, Matteo Tonelli	In this work we propose a framework for capturing the setting of “fair” discriminatory pricing and study its application to multi-unit markets, in which many copies of the same item are on sale.
32	An Axiomatic View of the Parimutuel Consensus Mechanism	Rupert Freeman, David M. Pennock	We consider an axiomatic view of the Parimutuel Consensus Mechanism defined by Eisenberg and Gale (1959).
33	Deep Learning for Multi-Facility Location Mechanism Design	Noah Golowich, Harikrishna Narasimhan, David C. Parkes	Our goal is to design strategy-proof, multi-facility mechanisms that minimize expected social cost.
34	Balancing Two-Player Stochastic Games with Soft Q-Learning	Jordi Grau-Moya, Felix Leibfried, Haitham Bou-Ammar	In this paper, we enable such tuneable behaviour by generalising soft Q-learning to stochastic games, where more than one agent interact strategically.
35	When Rigging a Tournament, Let Greediness Blind You	Sushmita Gupta, Sanjukta Roy, Saket Saurabh, Meirav Zehavi	We present a fresh, purely combinatorial greedy solution.
36	Winning a Tournament by Any Means Necessary	Sushmita Gupta, Sanjukta Roy, Saket Saurabh, Meirav Zehavi	For this problem, our contribution is fourfold.
37	Fostering Cooperation in Structured Populations Through Local and Global Interference Strategies	The Anh Han, Simon Lynch, Long Tran-Thanh, Francisco C. Santos	We study the situation of an exogenous decision-maker aiming to encourage a population of autonomous, self-regarding agents to follow a desired behaviour at a minimal cost.
38	Payoff Control in the Iterated Prisoner’s Dilemma	Dong Hao, Kai Li, Tao Zhou	Under the conventional iterated prisoner’s dilemma, we develop a general framework for controlling the feasible region where the players’ payoff pairs lie.
39	Ceteris paribus majority for social ranking	Adrian Haret, Hossein Khani, Stefano Moretti, Meltem Öztürk	We study the problem of finding a social ranking over individuals given a ranking over coalitions formed by them.
40	Computational Aspects of the Preference Cores of Supermodular Two-Scenario Cooperative Games	Daisuke Hatano, Yuichi Yoshida	To resolve this issue, we focus on supermodular two-scenario cooperative games in which the number of scenarios is two and the characteristic functions are supermodular and study the computational aspects of a major solution concept called the preference core.
41	Computational Social Choice Meets Databases	Benny Kimelfeld, Phokion G. Kolaitis, Julia Stoyanovich	We develop a novel framework that aims to create bridges between the computational social choice and the database management communities.
42	Symbolic Synthesis of Fault-Tolerance Ratios in Parameterised Multi-Agent Systems	Panagiotis Kouvaros, Alessio Lomuscio, Edoardo Pirovano	We introduce a procedure to synthesise automatically the maximal ratio of faulty agents that may be present at runtime for a specification to be satisfied in a multi-agent system.
43	Explaining Multi-Criteria Decision Aiding Models with an Extended Shapley Value	Christophe Labreuche, Simon Fossier	We present an explanation approach usable with any hierarchical multi-criteria model, based on an influence index of each attribute on the decision.
44	Approval-Based Multi-Winner Rules and Strategic Voting	Martin Lackner, Piotr Skowron	In this paper, we systematically analyze multiwinner rules based on these axioms and provide a fine-grained picture of their resilience to strategic voting.
45	Combining Opinion Pooling and Evidential Updating for Multi-Agent Consensus	Chanelle Lee, Jonathan Lawry, Alan Winfield	We consider a possible Bayesian interpretation of probability pooling and then explore properties for pooling operators governing the extent to which direct evidence is diluted, preserved or amplified by the pooling process.
46	Service Exchange Problem	Julien Lesca, Taiki Todo	In this paper, we study the service exchange problem where each agent is willing to provide her service in order to receive in exchange the service of someone else.
47	Tractable (Simple) Contests	Priel Levy, David Sarne, Yonatan Aumann	In this paper we examine a variation of the classic contest that alleviates this problem by having contestants make the decisions sequentially rather than in parallel.
48	Customer Sharing in Economic Networks with Costs	Bin Li, Dong Hao, Dengji Zhao, Tao Zhou	To tackle this problem, we develop a novel mechanism called customer sharing mechanism (CSM) which incentivizes all sellers to share each other’s sale information to their private customer groups.
49	Dynamic Fair Division Problem with General Valuations	Bo Li, Wenyang Li, Yingkai Li	In this paper, we focus on how to dynamically allocate a divisible resource fairly among n players who arrive and depart over time.
50	Integrating Demand Response and Renewable Energy In Wholesale Market	Chaojie Li, Chen Liu, Xinghuo Yu, Ke Deng, Tingwen Huang, Liangchen Liu	In this paper, a new two-stage stochastic game model is introduced to operate the electricity market, where Stochastic Stackelberg-Cournot-Nash (SSCN) equilibrium is applied to characterize the optimal energy bidding strategy of the forward market and the optimal energy trading strategy of the spot market.
51	Equilibrium Behavior in Competing Dynamic Matching Markets	Zhuoshu Li, Neal Gupta, Sanmay Das, John P. Dickerson	This paper provides the first analysis of equilibrium behavior in dynamic competing matching market systems—first from the points of view of individual participants when market policies are fixed, and then from the points of view of markets when agents are stochastic.
52	What Game Are We Playing? End-to-end Learning in Normal and Extensive Form Games	Chun Kai Ling, Fei Fang, J. Zico Kolter	We propose a differentiable, end-to-end learning framework for addressing this task.
53	Verifying Emergence of Bounded Time Properties in Probabilistic Swarm Systems	Alessio Lomuscio, Edoardo Pirovano	We introduce algorithms for solving the related decision problems and show their correctness.
54	Maximin Share Allocations on Cycles	Zbigniew Lonc, Miroslaw Truszczynski	We present cases when maximin share allocations of goods on cycles exist and provide results on allocations guaranteeing each agent a certain portion of her maximin share.
55	Multi-Agent Path Finding with Deadlines	Hang Ma, Glenn Wagner, Ariel Felner, Jiaoyang Li, T. K. Satish Kumar, Sven Koenig	We formalize Multi-Agent Path Finding with Deadlines (MAPF-DL).
56	Optimal Bidding Strategy for Brand Advertising	Takanori Maehara, Atsuhiro Narita, Jun Baba, Takayuki Kawabata	In this study, we consider a real-time biding strategy for brand advertising.
57	Preference Orders on Families of Sets – When Can Impossibility Results Be Avoided?	Jan Maly, Miroslaw Truszczynski, Stefan Woltran	In this paper, we consider families of sets that induce connected subgraphs in graphs.
58	Online Pricing for Revenue Maximization with Unknown Time Discounting Valuations	Weichao Mao, Zhenzhe Zheng, Fan Wu, Guihai Chen	In this paper, we study the problem of revenue maximization in online auctions with unknown time discounting valuations, and model it as non-stationary multi-armed bandit optimization.
59	Leadership in Singleton Congestion Games	Alberto Marchesi, Stefano Coniglio, Nicola Gatti	In this paper, we focus on congestion games in which each player can choose a single resource (a.k.a. singleton congestion games) and a player acts as leader.
60	The Price of Usability: Designing Operationalizable Strategies for Security Games	Sara Marie Mc Carthy, Corine M. Laan, Kai Wang, Phebe Vayanos, Arunesh Sinha, Milind Tambe	To mitigate this predictability, the game-theoretic security game model was proposed which randomizes over pure (deterministic) strategies, causing confusion in the adversary.
61	Dynamically Forming a Group of Human Forecasters and Machine Forecaster for Forecasting Economic Indicators	Takahiro Miyoshi, Shigeo Matsubara	To overcome this drawback, we propose an ensemble method for estimating the expected error of a machine forecast and dynamically determining the optimal number of humans included in the ensemble.
62	Goal-Based Collective Decisions: Axiomatics and Computational Complexity	Arianna Novaro, Umberto Grandi, Dominique Longin, Emiliano Lorini	We study agents expressing propositional goals over a set of binary issues to reach a collective decision.
63	Deontic Sensors	Julian Padget, Marina De Vos, Charlie Ann Page	We use a resource-oriented architecture (ROA) pattern, that we call deontic sensors, to make normative reasoning part of an open MAS architecture.
64	Democratic Fair Allocation of Indivisible Goods	Erel Segal-Halevi, Warut Suksompong	We introduce the concept of democratic fairness, which aims to satisfy a certain fraction of the agents in each group.
65	Double Auctions in Markets for Multiple Kinds of Goods	Erel Segal-Halevi, Avinatan Hassidim, Yonatan Aumann	We extend the random-sampling technique used in earlier works to multi-good markets where traders have gross-substitute valuations.
66	Redividing the Cake	Erel Segal-Halevi	Redividing the Cake
67	Ex-post IR Dynamic Auctions with Cost-per-Action Payments	Weiran Shen, Zihe Wang, Song Zuo	In this paper, we use a structure that we call credit accounts to enable a general reduction from any incentive compatible and ex-ante individual rational dynamic auction to an approximate incentive compatible and ex-post individually rational dynamic auction with credit accounts.
68	Designing the Game to Play: Optimizing Payoff Structure in Security Games	Zheyuan Ryan Shi, Ziye Tang, Long Tran-Thanh, Rohit Singh, Fei Fang	For the case of weighted L^1-norm constraint, we present (i) a mixed integer linear program-based algorithm with approximation guarantee; (ii) a branch-and-bound based algorithm with improved efficiency achieved by effective pruning; (iii) a polynomial time approximation scheme for a special but practical class of problems.
69	Efficient Computation of Approximate Equilibria in Discrete Colonel Blotto Games	Dong Quan Vu, Patrick Loiseau, Alonso Silva	In this paper, we propose an algorithm to compute very efficiently an approximate equilibrium for the discrete Colonel Blotto game with many battlefields.
70	A Decentralised Approach to Intersection Traffic Management	Huan Vu, Samir Aknine, Sarvapali D. Ramchurn	This paper presents a decentralised traffic management mechanism for intersections using a distributed constraint optimisation approach (DCOP).
71	Extended Increasing Cost Tree Search for Non-Unit Cost Domains	Thayne T. Walker, Nathan R. Sturtevant, Ariel Felner	In this paper we introduce a new definition of the MAPF problem for non-unit cost and non-unit time step domains along with new multiagent state successor generation schemes for these domains.
72	A Cloaking Mechanism to Mitigate Market Manipulation	Xintong Wang, Yevgeniy Vorobeychik, Michael P. Wellman	We propose a cloaking mechanism to deter spoofing, a form of manipulation in financial markets.
73	Budget-feasible Procurement Mechanisms in Two-sided Markets	Weiwei Wu, Xiang Liu, Minming Li	Our main contribution is a random mechanism that guarantees various desired theoretical guarantees like the budget feasibility, the truthfulness on the sellers’ side and the buyers’ side simultaneously, and constant approximation to the optimal total procured value of buyers.
74	Exact Algorithms and Complexity of Kidney Exchange	Mingyu Xiao, Xuanbei Wang	This paper mainly contributes to algorithms from theory for this problem with and without length constraints (restricted and free versions).
75	Keeping in Touch with Collaborative UAVs: A Deep Reinforcement Learning Approach	Bo Yang, Min Liu	In this paper, we leverage the deep reinforcement learning (DRL) technique to address the UAVs’ optimal links discovery and selection problem in uncertain environments.
76	Recurrent Deep Multiagent Q-Learning for Autonomous Brokers in Smart Grid	Yaodong Yang, Jianye Hao, Mingyang Sun, Zan Wang, Changjie Fan, Goran Strbac	In this paper, we develop an effective pricing strategy for brokers in local electricity retail market based on recurrent deep multiagent reinforcement learning and sequential clustering.
77	Multiwinner Voting with Restricted Admissible Sets: Complexity and Strategyproofness	Yongjie Yang, Jianxin Wang	In this paper, we study admissible sets with combinatorial restrictions.
78	Socially Motivated Partial Cooperation in Multi-agent Local Search	Tal Ze’evi, Roie Zivan, Omer Lev	Distributed local search algorithms were proposed in order to solve asymmetric distributed constraint optimization problems (ADCOPs) in which agents are partially cooperative.
79	Strategyproof and Fair Matching Mechanism for Union of Symmetric M-convex Constraints	Yuzhe Zhang, Kentaro Yahiro, Nathanaël Barrot, Makoto Yokoo	In this paper, we identify a new class of distributional constraints defined as a union of symmetric M-convex sets, which can represent a variety of real-life constraints in two-sided matching settings.
80	MEnet: A Metric Expression Network for Salient Object Segmentation	Shulian Cai, Jiabin Huang, Delu Zeng, Xinghao Ding, John Paisley	In this paper, we propose an end-to-end generic salient object segmentation model called Metric Expression Network (MEnet) to overcome this drawback.
81	Show, Observe and Tell: Attribute-driven Attention Model for Image Captioning	Hui Chen, Guiguang Ding, Zijia Lin, Sicheng Zhao, Jungong Han	In this paper, we focus on training a good attribute-inference model via the recurrent neural network (RNN) for image captioning, where the co-occurrence dependencies among attributes can be maintained.
82	Learning Deep Unsupervised Binary Codes for Image Retrieval	Junjie Chen, William K. Cheung, Anran Wang	In this paper, we propose a novel deep unsupervised method called DeepQuan for hashing.
83	Deep View-Aware Metric Learning for Person Re-Identification	Pu Chen, Xinyi Xu, Cheng Deng	In this paper, we propose a deep view-aware metric learning (DVAML) model, where image pairs with similar and dissimilar views are projected into different feature subspaces, which can discover the intrinsic relevance between image pairs from different aspects.
84	Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition	Tianshui Chen, Liang Lin, Riquan Chen, Yang Wu, Xiaonan Luo	In this work, we investigate how to unify rich professional knowledge with deep neural network architectures and propose a Knowledge-Embedded Representation Learning (KERL) framework for handling the problem of fine-grained image recognition.
85	Sharing Residual Units Through Collective Tensor Factorization To Improve Deep Neural Networks	Yunpeng Chen, Xiaojie Jin, Bingyi Kang, Jiashi Feng, Shuicheng Yan	In this work, we revisit the standard residual function as well as its several successful variants and propose a unified framework based on tensor Block Term Decomposition (BTD) to explain these apparently different residual functions from the tensor decomposition view.
86	Scanpath Prediction for Visual Attention using IOR-ROI LSTM	Zhenzhong Chen, Wanjie Sun	This paper presents a model that integrates convolutional neural network and long short-term memory (LSTM) to generate realistic scanpaths.
87	Multi-scale and Discriminative Part Detectors Based Features for Multi-label Image Classification	Gong Cheng, Decheng Gao, Yang Liu, Junwei Han	This paper proposes a new and effective framework built upon CNNs to learn Multi-scale and Discriminative Part Detectors (MsDPD)-based feature representations for multi-label image classification.
88	Anonymizing k Facial Attributes via Adversarial Perturbations	Saheb Chhabra, Richa Singh, Mayank Vatsa, Gaurav Gupta	This research presents a novel algorithm for anonymizing selective attributes which an individual does not want to share without affecting the visual quality of images.
89	Dual Adversarial Networks for Zero-shot Cross-media Retrieval	Jingze Chi, Yuxin Peng	Inspired by zero-shot learning, this paper proposes zeroshot cross-media retrieval for addressing the above problem, which aims to retrieve data of new categories across different media types.
90	Siamese CNN-BiLSTM Architecture for 3D Shape Representation Learning	Guoxian Dai, Jin Xie, Yi Fang	In this paper, by employing recurrent neural network to efficiently capture features across different views, we propose a siamese CNN-BiLSTM network for 3D shape representation learning.
91	Cross-Modality Person Re-Identification with Generative Adversarial Training	Pingyang Dai, Rongrong Ji, Haibin Wang, Qiong Wu, Yuyu Huang	In this paper, we tackle the above two challenges by proposing a novel cross-modality generative adversarial network (termed cmGAN).
92	R³Net: Recurrent Residual Refinement Network for Saliency Detection	Zijun Deng, Xiaowei Hu, Lei Zhu, Xuemiao Xu, Jing Qin, Guoqiang Han, Pheng-Ann Heng	We propose a novel recurrent residual refinement network (R^3Net) equipped with residual refinement blocks (RRBs) to more accurately detect salient regions of an input image.
93	Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss	Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, Pheng-Ann Heng	In this paper, we propose an unsupervised domain adaptation framework with adversarial learning for cross-modality biomedical image segmentations.
94	Enhanced-alignment Measure for Binary Foreground Map Evaluation	Deng-Ping Fan, Cheng Gong, Yang Cao, Bo Ren, Ming-Ming Cheng, Ali Borji	In this paper, we take a detailed look at current binary FM evaluation measures and propose a novel and effective E-measure (Enhanced-alignment measure).
95	Watching a Small Portion could be as Good as Watching All: Towards Efficient Video Classification	Hehe Fan, Zhongwen Xu, Linchao Zhu, Chenggang Yan, Jianjun Ge, Yi Yang	Differently, we propose an end-to-end deep reinforcement approach which enables an agent to classify videos by watching a very small portion of frames like what we do.
96	Age Estimation Using Expectation of Label Distribution Learning	Bin-Bin Gao, Hong-Yu Zhou, Jianxin Wu, Xin Geng	To alleviate these issues, we design a lightweight network architecture and propose a unified framework which can jointly learn age distribution and regress age.
97	Coarse-to-fine Image Co-segmentation with Intra and Inter Rank Constraints	Lianli Gao, Jingkuan Song, Dongxiang Zhang, Heng Tao Shen	To alleviate these limitations, we propose a novel coarse-to-fine co-segmentation (CFC) framework, which utilizes the coarse foreground and background proposals to learn a robust similarity measure of the features in an unsupervised way, and then devises a simple objective function based on the definition of image co-segmentation.
98	View-Volume Network for Semantic Scene Completion from a Single Depth Image	Yuxiao Guo, Xin Tong	We introduce a View-Volume convolutional neural network (VVNet) for inferring the occupancy and semantic labels of a volumetric 3D scene from a single depth image.
99	Harnessing Synthesized Abstraction Images to Improve Facial Attribute Recognition	Keke He, Yanwei Fu, Wuhao Zhang, Chengjie Wang, Yu-Gang Jiang, Feiyue Huang, Xiangyang Xue	With the facial parts localized by the abstraction images, our method improves facial attributes recognition, especially the attributes located on small face regions.
100	StackDRL: Stacked Deep Reinforcement Learning for Fine-grained Visual Categorization	Xiangteng He, Yuxin Peng, Junjie Zhao	To address the “which” and “how many” problems adaptively and intelligently, this paper proposes a stacked deep reinforcement learning approach (StackDRL).
101	Co-attention CNNs for Unsupervised Object Co-segmentation	Kuang-Jui Hsu, Yen-Yu Lin, Yung-Yu Chuang	This paper presents a CNN-based method that is unsupervised and end-to-end trainable to better solve this task.
102	Human Motion Generation via Cross-Space Constrained Sampling	Zhongyue Huang, Jingwei Xu, Bingbing Ni	We aim to automatically generate human motion sequence from a single input person image, with some specific action label.
103	Semantic Locality-Aware Deformable Network for Clothing Segmentation	Wei Ji, Xi Li, Yueting Zhuang, Omar El Farouk Bourahla, Yixin Ji, Shihao Li, Jiabao Cui	To deal with these points, we propose a semantic locality-aware segmentation model, which adaptively attaches an original clothing image with a semantically similar (e.g., appearance or pose) auxiliary exemplar by search.
104	Deep CNN Denoiser and Multi-layer Neighbor Component Embedding for Face Hallucination	Junjun Jiang, Yi Yu, Jinhui Hu, Suhua Tang, Jiayi Ma	To solve this problem, in this paper we propose a general face hallucination method that can integrate model-based optimization and discriminative inference.
105	Feature Integration with Adaptive Importance Maps for Visual Tracking	Aishi Li, Ming Yang, Wanqi Yang	This paper investigates an effective method of feature integration for correlation filters, which jointly learns filters, as well as importance maps in each frame.
106	Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation	Chao Li, Qiaoyong Zhong, Di Xie, Shiliang Pu	In this paper we propose an end-to-end convolutional co-occurrence feature learning framework.
107	Image Cationing with Visual-Semantic LSTM	Nannan Li, Zhenzhong Chen	In this paper, a novel image captioning approach is proposed to describe the content of images.
108	Nonrigid Points Alignment with Soft-weighted Selection	Xuelong Li, Jian Yang, Qi Wang	In this paper, we present a self-selected regularized Gaussian fields criterion for nonrigid point matching.
109	Deeply-Supervised CNN Model for Action Recognition with Trainable Feature Aggregation	Yang Li, Kan Li, Xinxin Wang	In this paper, we propose a deeply-supervised CNN model for action recognition that fully exploits powerful hierarchical features of CNNs.
110	Live Face Verification with Multiple Instantialized Local Homographic Parameterization	Chen Lin, Zhouyingcheng Liao, Peng Zhou, Jianguo Hu, Bingbing Ni	This work directly addresses this issue via proposing a patch-wise motion parameterization based verification network infrastructure.
111	Multi-Level Policy and Reward Reinforcement Learning for Image Captioning	Anan Liu, Ning Xu, Hanwang Zhang, Weizhi Nie, Yuting Su, Yongdong Zhang	To this end, we propose a novel multi-level policy and reward RL framework for image captioning.
112	Cross-Domain 3D Model Retrieval via Visual Domain Adaption	Anan Liu, Shu Xiang, Wenhui Li, Weizhi Nie, Yuting Su	To address this problem, we propose a novel crossdomain 3D model retrieval method by visual domain adaptation.
113	Deep Attribute Guided Representation for Heterogeneous Face Recognition	Decheng Liu, Nannan Wang, Chunlei Peng, Jie Li, Xinbo Gao	In this paper, we propose a novel deep attribute guided representation based heterogeneous face recognition method (DAG-HFR) without labeling attributes manually.
114	When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach	Ding Liu, Bihan Wen, Xianming Liu, Zhangyang Wang, Thomas Huang	In this paper, we cope with the two jointly and explore the mutual influence between them.
115	Crowd Counting using Deep Recurrent Spatial-Aware Network	Lingbo Liu, Hongjun Wang, Guanbin Li, Wanli Ouyang, Liang Lin	In this paper, we propose a unified neural network framework, named Deep Recurrent Spatial-Aware Network, which adaptively addresses the two issues in a learnable spatial transform module with a region-wise refinement process.
116	H-Net: Neural Network for Cross-domain Image Patch Matching	Weiquan Liu, Xuelun Shen, Cheng Wang, Zhihong Zhang, Chenglu Wen, Jonathan Li	In this paper, we propose to incorporate AutoEncoder into the Siamese network, named as H-Net, of which the structural shape resembles the letter H. As there is no benchmark dataset including cross-domain images, we made a cross-domain image dataset which consists of camera images, rendering images from UAV 3D model, and images generated by CycleGAN algorithm.
117	DEL: Deep Embedding Learning for Efficient Image Segmentation	Yun Liu, Peng-Tao Jiang, Vahan Petrosyan, Shi-Jie Li, Jiawang Bian, Le Zhang, Ming-Ming Cheng	In this paper, we propose a novel method called DEL (deep embedding learning) which can efficiently transform superpixels into image segmentation.
118	Progressive Generative Hashing for Image Retrieval	Yuqing Ma, Yue He, Fan Ding, Sheng Hu, Jun Li, Xianglong Liu	In this paper, we propose a novel progressive generative hashing (PGH) framework to help learn a discriminative hashing network in an unsupervised way.
119	MEGAN: Mixture of Experts of Generative Adversarial Networks for Multimodal Image Generation	David Keetae Park, Seungjoo Yoo, Hyojin Bahng, Jaegul Choo, Noseong Park	To mitigate this problem, we present a novel approach called mixture of experts GAN (MEGAN), an ensemble approach of multiple generator networks.
120	Dilated Convolutional Network with Iterative Optimization for Continuous Sign Language Recognition	Junfu Pu, Wengang Zhou, Houqiang Li	This paper presents a novel deep neural architecture with iterative optimization strategy for real-world continuous sign language recognition.
121	Cross-media Multi-level Alignment with Relation Attention Network	Jinwei Qi, Yuxin Peng, Yuxin Yuan	To address the above issue, we propose Cross-media Relation Attention Network (CRAN) with multi-level alignment.
122	Dual Conditional GANs for Face Aging and Rejuvenation	Jingkuan Song, Jingqiu Zhang, Lianli Gao, Xianglong Liu, Heng Tao Shen	To tackle these issues, in this paper, we develop a novel dual conditional GAN (DCGAN) mechanism, which enables face aging and rejuvenation to be trained from multiple sets of unlabeled face images with different ages.
123	From Pixels to Objects: Cubic Visual Attention for Visual Question Answering	Jingkuan Song, Pengpeng Zeng, Lianli Gao, Heng Tao Shen	In this paper we propose a Cubic Visual Attention (CVA) model by successfully applying a novel channel and spatial attention on object regions to improve VQA task.
124	Hierarchical Graph Structure Learning for Multi-View 3D Model Retrieval	Yuting Su, Wenhui Li, Anan Liu, Weizhi Nie	In this paper, we propose an hierarchical graph structure learning method (HGS) for 3D model retrieval.
125	Learning to Write Stylized Chinese Characters by Reading a Handful of Examples	Danyang Sun, Tongzheng Ren, Chongxuan Li, Hang Su, Jun Zhu	In this paper, we develop a novel framework of Style-Aware Variational Auto-Encoder (SA-VAE), which disentangles the content-relevant and style-relevant components of a Chinese character feature with a novel intercross pair-wise optimization method.
126	Image-level to Pixel-wise Labeling: From Theory to Practice	Tiezhu Sun, Wei Zhang, Zhijie Wang, Lin Ma, Zequn Jie	In this paper, we advocate tackling the pixel-wise segmentation problem by considering the image-level classification labels.
127	Long-Term Human Motion Prediction by Modeling Motion Context and Enhancing Motion Dynamics	Yongyi Tang, Lin Ma, Wei Liu, Wei-Shi Zheng	In this work, we propose a motion context modeling by summarizing the historical human motion with respect to the current prediction.
128	CR-GAN: Learning Complete Representations for Multi-view Generation	Yu Tian, Xi Peng, Long Zhao, Shaoting Zhang, Dimitris N. Metaxas	We evaluate our approach on a wide range of datasets.
129	Representation Learning for Scene Graph Completion via Jointly Structural and Visual Embedding	Hai Wan, Yonghao Luo, Bo Peng, Wei-Shi Zheng	In RLSV model, we provide a fully-convolutional module to extract the visual embeddings of a visual triple and apply hierarchical projection to combine the structural and visual embeddings of a visual triple.
130	Collaborative and Attentive Learning for Personalized Image Aesthetic Assessment	Guolong Wang, Junchi Yan, Zheng Qin	Specifically, we collect user-image textual reviews in addition with visual images from the public dataset to organize a review-augmented benchmark.
131	Uncertainty Sampling for Action Recognition via Maximizing Expected Average Precision	Hanmo Wang, Xiaojun Chang, Lei Shi, Yi Yang, Yi-Dong Shen	In this paper, we propose a novel uncertainty sampling algorithm for action recognition using expected AP.
132	Collaborative Learning for Weakly Supervised Object Detection	Jiajie Wang, Jiangchao Yao, Ya Zhang, Rui Zhang	Nevertheless, the save in labeling expense is usually at the cost of model accuracy.In this paper, we propose a simple but effective weakly supervised collaborative learning framework to resolve this problem, which trains a weakly supervised learner and a strongly supervised learner jointly by enforcing partial feature sharing and prediction consistency.
133	DRPose3D: Depth Ranking in 3D Human Pose Estimation	Min Wang, Xipeng Chen, Wentao Liu, Chen Qian, Liang Lin, Lizhuang Ma	In this paper, we propose a two-stage depth ranking based method (DRPose3D) to tackle the problem of 3D human pose estimation.
134	Do not Lose the Details: Reinforced Representation Learning for High Performance Visual Tracking	Qiang Wang, Mengdan Zhang, Junliang Xing, Jin Gao, Weiming Hu, Steve Maybank	This work presents a novel end-to-end trainable CNN model for high performance visual object tracking.
135	Ensemble Soft-Margin Softmax Loss for Image Classification	Xiaobo Wang, Shifeng Zhang, Zhen Lei, Si Liu, Xiaojie Guo, Stan Z. Li	We hence introduce a soft-margin softmax function to explicitly encourage the discrmination between different classes.
136	Deep Propagation Based Image Matting	Yu Wang, Yi Niu, Peiyong Duan, Jianwei Lin, Yuanjie Zheng	In this paper, we propose a deep propagation based image matting framework by introducing deep learning into learning an alpha matte propagation principal.
137	Densely Cascaded Shadow Detection Network via Deeply Supervised Parallel Fusion	Yupei Wang, Xin Zhao, Yin Li, Xuecai Hu, Kaiqi Huang	To this end, this paper presents a novel model characterized by a deeply supervised parallel fusion (DSPF) network and a densely cascaded learning scheme.
138	HCR-Net: A Hybrid of Classification and Regression Network for Object Pose Estimation	Zairan Wang, Weiming Li, Yueying Kao, Dongqing Zou, Qiang Wang, Minsu Ahn, Sunghoon Hong	In this paper, a hybrid CNN model, which we call it HCR-Net that integrates both a classification network and a regression network, is proposed to deal with these issues.
139	Deep Reasoning with Knowledge Graph for Social Relationship Understanding	Zhouxia Wang, Tianshui Chen, Jimmy Ren, Weihao Yu, Hui Cheng, Liang Lin	We found that the interplay between these two factors can be effectively modeled by a novel structured knowledge graph with proper message propagation and attention.
140	Multi-modal Circulant Fusion for Video-to-Language and Backward	Aming Wu, Yahong Han	In this paper, we put forward a new approach of multi-modal fusion, namely Multi-modal Circulant Fusion (MCF).
141	Annotation-Free and One-Shot Learning for Instance Segmentation of Homogeneous Object Clusters	Zheng Wu, Ruiheng Chang, Jiaxu Ma, Cewu Lu, Chi Keung Tang	We propose a novel approach for instance segmentation given an image of homogeneous object cluster (HOC). We build a dataset consisting of pixel-level annotated images of HOC.
142	Fine-grained Image Classification by Visual-Semantic Embedding	Huapeng Xu, Guilin Qi, Jingjing Li, Meng Wang, Kang Xu, Huan Gao	Specifically, we propose a visual-semanticembedding model which explores semanticembedding from knowledge bases and text, andfurther trains a novel end-to-end CNN frameworkto linearly map image features to a rich semanticembedding space.
143	Evaluating Brush Movements for Chinese Calligraphy: A Computer Vision Based Approach	Pengfei Xu, Lei Wang, Ziyu Guan, Xia Zheng, Xiaojiang Chen, Zhanyong Tang, Dingyi Fang, Xiaoqing Gong, Zheng Wang	This paper presents a novel approach to help Chinese calligraphy learners to quantify the quality of brush movements without expert involvement.
144	Multi-task Layout Analysis for Historical Handwritten Documents Using Fully Convolutional Networks	Yue Xu, Fei Yin, Zhaoxiang Zhang, Cheng-Lin Liu	In this work, we propose a multi-task layout analysis method that use a single FCN model to solve the above three problems simultaneously.
145	Semantic Structure-based Unsupervised Deep Hashing	Erkun Yang, Cheng Deng, Tongliang Liu, Wei Liu, Dacheng Tao	To address this problem, we propose a novel unsupervised deep framework called Semantic Structure-based unsupervised Deep Hashing (SSDH).
146	IncepText: A New Inception-Text Module with Deformable PSROI Pooling for Multi-Oriented Scene Text Detection	Qiangpeng Yang, Mengli Cheng, Wenmeng Zhou, Yan Chen, Minghui Qiu, Wei Lin	To solve this problem, we propose a novel end-to-end scene text detector IncepText from an instance-aware segmentation perspective.
147	SSR-Net: A Compact Soft Stagewise Regression Network for Age Estimation	Tsun-Yi Yang, Yi-Hsuan Huang, Yen-Yu Lin, Pi-Cheng Hsiu, Yung-Yu Chuang	This paper presents a novel CNN model called Soft Stagewise Regression Network (SSR-Net) for age estimation from a single image with a compact model size.
148	Extracting Privileged Information from Untagged Corpora for Classifier Learning	Yazhou Yao, Jian Zhang, Fumin Shen, Wankou Yang, Xian-Sheng Hua, Zhenmin Tang	To address this issue, we propose to enhance classifier learning by extracting PI from untagged corpora, which can effectively eliminate the dependency on manually labeled data.
149	Visible Thermal Person Re-Identification via Dual-Constrained Top-Ranking	Mang Ye, Zheng Wang, Xiangyuan Lan, Pong C. Yuen	In this paper, we propose a dual-path network with a novel bi-directional dual-constrained top-ranking loss to learn discriminative feature representations.
150	Adversarial Attribute-Image Person Re-identification	Zhou Yin, Wei-Shi Zheng, Ancong Wu, Hong-Xing Yu, Hai Wan, Xiaowei Guo, Feiyue Huang, Jianhuang Lai	In this work, we present this challenge and leverage adversarial learning to formulate the attribute-image cross-modality person Re-ID model.
151	Exploiting Images for Video Recognition with Hierarchical Generative Adversarial Networks	Feiwu Yu, Xinxiao Wu, Yuchao Sun, Lixin Duan	In this paper, we propose a novel method, called Hierarchical Generative Adversarial Networks (HiGAN), to enhance recognition in videos (i.e., target domain) by transferring knowledge from images (i.e., source domain).
152	Rethinking Diversified and Discriminative Proposal Generation for Visual Grounding	Zhou Yu, Jun Yu, Chenchao Xiang, Zhou Zhao, Qi Tian, Dacheng Tao	In this paper, we rethink the problem of what properties make a good proposal generator.
153	SafeNet: Scale-normalization and Anchor-based Feature Extraction Network for Person Re-identification	Kun Yuan, Qian Zhang, Chang Huang, Shiming Xiang, Chunhong Pan	In this paper, we show the advantage of jointly utilizing multi-scale abstract information to learn powerful features over full body and parts.
154	Visual Data Synthesis via GAN for Zero-Shot Video Classification	Chenrui Zhang, Yuxin Peng	In this paper, we propose a visual data synthesis framework via GAN to address these problems.
155	Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph Distillation for Video Classification	Chenrui Zhang, Yuxin Peng	In this paper, we propose a graph-based distillation framework to address these problems: (1) We propose logits graph and representation graph to transfer knowledge from multiple self-supervised tasks, where the former distills classifier-level knowledge by solving a multi-distribution joint matching problem, and the latter distills internal feature knowledge from pairwise ensembled representations with tackling the challenge of heterogeneity among different features; (2) The proposal that adopts a teacher-student framework can reduce the redundancy of knowledge learned from teachers dramatically, leading to a lighter student model that solves classification task more efficiently.
156	Markov Random Neural Fields for Face Sketch Synthesis	Mingjin Zhang, Nannan Wang, Xinbo Gao, Yunsong Li	In this paper, we propose a novel face sketch synthesis based on the Markov random neural fields including two structures.
157	Salient Object Detection by Lossless Feature Reflection	Pingping Zhang, Wei Liu, Huchuan Lu, Chunhua Shen	Inspired by the intrinsic reflection of natural images, in this paper we propose a novel feature learning framework for large-scale salient object detection.
158	High Resolution Feature Recovering for Accelerating Urban Scene Parsing	Rui Zhang, Sheng Tang, Luoqi Liu, Yongdong Zhang, Jintao Li, Shuicheng Yan	To tackle this issue, we propose a High Resolution Feature Recovering (HRFR) framework to accelerate a given parsing network.
159	Robust Face Sketch Synthesis via Generative Adversarial Fusion of Priors and Parametric Sigmoid	Shengchuan Zhang, Rongrong Ji, Jie Hu, Yue Gao, Chia-Wen Lin	In this paper, we propose a novel generative adversarial network termed pGAN, which can generate face sketches efficiently using training data under fixed conditions and handle the aforementioned uncontrolled conditions.
160	Layered Optical Flow Estimation Using a Deep Neural Network with a Soft Mask	Xi Zhang, Di Ma, Xu Ouyang, Shanshan Jiang, Lin Gan, Gady Agam	In this paper, we learn to estimate optical flow by combining a layered motion representation with deep learning.
161	Video Captioning with Tube Features	Bin Zhao, Xuelong Li, Xiaoqiang Lu	To tackle this problem, in this paper, we design an object-aware feature for video captioning, denoted as tube feature.
162	3D-Aided Deep Pose-Invariant Face Recognition	Jian Zhao, Lin Xiong, Yu Cheng, Yi Cheng, Jianshu Li, Li Zhou, Yan Xu, Jayashree Karlekar, Sugiri Pranata, Shengmei Shen, Junliang Xing, Shuicheng Yan, Jiashi Feng	To mitigate this gap, we propose a 3D-Aided Deep Pose-Invariant Face Recognition Model (3D-PIM), which automatically recovers realistic frontal faces from arbitrary poses through a 3D face model in a novel way.
163	Hi-Fi: Hierarchical Feature Integration for Skeleton Detection	Kai Zhao, Wei Shen, Shanghua Gao, Dandan Li, Ming-Ming Cheng	To address this issue, we present a new convolutional neural network (CNN) architecture by introducing a novel hierarchical feature integration mechanism, named Hi-Fi, to address the object skeleton detection problem.
164	Distortion-aware CNNs for Spherical Images	Qiang Zhao, Chen Zhu, Feng Dai, Yike Ma, Guoqing Jin, Yongdong Zhang	In this paper, we present distortion-aware convolutional network for spherical images.
165	A Multi-task Learning Approach for Image Captioning	Wei Zhao, Benyou Wang, Jianbo Ye, Min Yang, Zhou Zhao, Ruotian Luo, Yu Qiao	In this paper, we propose a Multi-task Learning Approach for Image Captioning (MLAIC ), motivated by the fact that humans have no difficulty performing such task because they possess capabilities of multiple domains.
166	A Comparative Study of Transactional and Semantic Approaches for Predicting Cascades on Twitter	Yunwei Zhao, Can Wang, Chi-Hung Chi, Kwok-Yan Lam, Sen Wang	This paper conducts a comparative study of both approaches in predicting information diffusion with three mechanisms: retweet cascade, url cascade, and hashtag cascade.
167	Learning Robust Gaussian Process Regression for Visual Tracking	Linyu Zheng, Ming Tang, Jinqiao Wang	In this paper, we propose a novel Gaussian Process Regression based tracker (GPRT) which is a conceptually natural tracking approach.
168	Centralized Ranking Loss with Weakly Supervised Localization for Fine-Grained Object Retrieval	Xiawu Zheng, Rongrong Ji, Xiaoshuai Sun, Yongjian Wu, Feiyue Huang, Yanhua Yang	In this paper, we present a novel fine-grained object retrieval scheme that conquers these issues in a unified framework.
169	DehazeGAN: When Image Dehazing Meets Differential Programming	Hongyuan Zhu, Xi Peng, Vijay Chandrasekhar, Liyuan Li, Joo-Hwee Lim	Most existing methods employ a two-step pipeline to estimate these two parameters with heuristics which accumulate errors and compromise dehazing quality.
170	A Framework for Constraint Based Local Search using Essence	Özgür Akgün, Saad Attieh, Ian P. Gent, Christopher Jefferson, Ian Miguel, Peter Nightingale, András Z. Salamon, Patrick Spracklen, James Wetter	We have implemented SNS, together with automatic generation of neighbourhoods for high level structures, and report high quality results for several optimisation problems.
171	On the Satisfiability Threshold of Random Community-Structured SAT	Dina Barak-Pelleg, Daniel Berend	In this paper, we endeavor to study the satisfiability threshold for random industrial SAT.
172	Classification Transfer for Qualitative Reasoning Problems	Manuel Bodirsky, Peter Jonsson, Barnaby Martin, Antoine Mottet	We study formalisms for temporal and spatial reasoning in the modern context of Constraint Satisfaction Problems (CSPs).
173	Descriptive Clustering: ILP and CP Formulations with Applications	Thi-Bich-Hanh Dao, Chia-Tung Kuo, S. S. Ravi, Christel Vrain, Ian Davidson	We formulate the descriptive clustering problem as a bi-objective optimization to simultaneously find compact clusters using the features and to describe them using the tags.
174	Methods for off-line/on-line optimization under uncertainty	Allegra De Filippo, Michele Lombardi, Michela Milano	In this work we present two general techniques to deal with multi-stage optimization problems under uncertainty, featuring off-line and on-line decisions.
175	Machine Learning and Constraint Programming for Relational-To-Ontology Schema Mapping	Diego De Uña, Nataliia Rümmele, Graeme Gange, Peter Schachte, Peter J. Stuckey	In this paper we improve previous work by Taheriyan et al. [2016a] using Machine Learning (ML) to take into account inconsistencies in the data (unmatchable attributes) and encode the problem as a variation of the Steiner Tree, for which we use work by De Uña et al. [2016] in Constraint Programming (CP).
176	Unary Integer Linear Programming with Structural Restrictions	Eduard Eiben, Robert Ganian, Dušan Knop, Sebastian Ordyniak	In this paper, we target ILPs where neither the variable domains nor the coefficients are restricted by a fixed constant or parameter; instead, we only require that our instances can be encoded in unary.
177	Divide and Conquer: Towards Faster Pseudo-Boolean Solving	Jan Elffers, Jakob Nordström	We propose a modified approach to pseudo-Boolean solving based on division instead of the saturation rule used in [Chai and Kuehlmann ’05] and other PB solvers.
178	Seeking Practical CDCL Insights from Theoretical SAT Benchmarks	Jan Elffers, Jesús Giráldez-Cru, Stephan Gocht, Jakob Nordström, Laurent Simon	In this work we shed light on CDCL performance by using theoretical benchmarks, which have the attractive features of being a) scalable, b) extremal with respect to different proof search parameters, and c) theoretically easy in the sense of having short proofs in the resolution proof system underlying CDCL.
179	Boosting MCSes Enumeration	Éric Grégoire, Yacine Izza, Jean-Marie Lagniez	In the paper, a technique is introduced that boosts the currently most efficient practical approaches to enumerate MCSes.
180	Conflict Directed Clause Learning for Maximum Weighted Clique Problem	Emmanuel Hebrard, George Katsirelos	We introduce a new approach based on SAT and on the “Conflict-Driven Clause Learning” (CDCL) algorithm.
181	Simpler and Faster Algorithm for Checking the Dynamic Consistency of Conditional Simple Temporal Networks	Luke Hunsberger, Roberto Posenato	This paper (1) shows that the IR-DC semantics is also flawed; (2) shows that one of the constraint-propagation rules from the IR-DC-checking algorithm is not sound with respect to the IR-DC semantics; (3) presents a simpler algorithm, called the π-DC-checking algorithm; (4) proves that it is sound and complete with respect to the π-DC semantics; and (5) empirically evaluates the new algorithm.
182	DMC: A Distributed Model Counter	Jean-Marie Lagniez, Pierre Marquis, Nicolas Szczepanski	We present and evaluate DMC, a distributed model counter for propositional CNF formulae based on the state-of-the-art sequential model counter D4.
183	Solving Exist-Random Quantified Stochastic Boolean Satisfiability via Clause Selection	Nian-Ze Lee, Yen-Shi Wang, Jie-Hong R. Jiang	In this paper, we focus on exist-random quantified SSAT formulas, also known as E-MAJSAT, which is a special fragment of SSAT commonly applied in probabilistic conformant planning, posteriori hypothesis, and maximum expected utility.
184	Solving (Weighted) Partial MaxSAT by Dynamic Local Search for SAT	Zhendong Lei, Shaowei Cai	In this work, we propose a dynamic local search algorithm, which exploits the structure of (W)PMS by a carefully designed clause weighting scheme.
185	Core-Guided Minimal Correction Set and Core Enumeration	Nina Narodytska, Nikolaj Bjørner, Maria-Cristina Marinescu, Mooly Sagiv	In this work, we propose a new algorithm for extracting minimal unsatisfiable cores and correction sets simultaneously.
186	Learning Optimal Decision Trees with SAT	Nina Narodytska, Alexey Ignatiev, Filipe Pereira, Joao Marques-Silva	This paper develops a SAT-based model for computing smallest-size decision trees given training data.
187	Accelerated Difference of Convex functions Algorithm and its Application to Sparse Binary Logistic Regression	Duy Nhat Phan, Hoai Minh Le, Hoai An Le Thi	In this work, we present a variant of DCA (Difference of Convex function Algorithm) with the aim to improve its convergence speed.
188	Stratification for Constraint-Based Multi-Objective Combinatorial Optimization	Miguel Terra-Neves, Inês Lynce, Vasco Manquinho	New constraint-based algorithms have been recently proposed to solve Multi-Objective Combinatorial Optimization (MOCO) problems.
189	Compact-MDD: Efficiently Filtering (s)MDD Constraints with Reversible Sparse Bit-sets	Hélène Verhaeghe, Christophe Lecoutre, Pierre Schaus	Multi-Valued Decision Diagrams (MDDs) are instrumental in modeling combinatorial problems with Constraint Programming.In this paper, we propose a related data structure called sMDD (semi-MDD) where the central layer of the diagrams is non-deterministic.
190	A Reactive Strategy for High-Level Consistency During Search	Robert J. Woodward, Berthe Y. Choueiry, Christian Bessiere	We propose a simple and effective strategy that reactively triggers an HLC by monitoring search performance: When search starts thrashing, we trigger an HLC, then conservatively revert to GAC.
191	A Fast Algorithm for Generalized Arc Consistency of the Alldifferent Constraint	Xizhe Zhang, Qian Li, Weixiong Zhang	Based on this theoretical result, we present an efficient algorithm for processing alldifferent constraints.
192	A General Approach to Running Time Analysis of Multi-objective Evolutionary Algorithms	Chao Bian, Chao Qian, Ke Tang	In this paper, we propose a general approach to estimating upper bounds on the expected running time of multi-objective EAs (MOEAs), and then apply it to diverse situations, including bi-objective and many-objective optimization as well as exact and approximate analysis.
193	Improving Local Search for Minimum Weight Vertex Cover by Dynamic Strategies	Shaowei Cai, Wenying Hou, Jinkun Lin, Yuanjie Li	In this work, we propose two dynamic strategies that adjust the behavior of the algorithm during search, which are used to improve a state of the art local search for MWVC named FastWVC, resulting in two local search algorithms called DynWVC1 and DynWVC2.
194	Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari	Patryk Chrabąszcz, Ilya Loshchilov, Frank Hutter	Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari
195	The FastMap Algorithm for Shortest Path Computations	Liron Cohen, Tansel Uras, Shiva Jahangiri, Aliyah Arunasalam, Sven Koenig, T. K. Satish Kumar	We present a new preprocessing algorithm for embedding the nodes of a given edge-weighted undirected graph into a Euclidean space.
196	Anytime Focal Search with Applications	Liron Cohen, Matias Greco, Hang Ma, Carlos Hernandez, Ariel Felner, T. K. Satish Kumar, Sven Koenig	In this paper, we develop an anytime version of FS, called anytime FS (AFS), that is useful when deliberation time is limited.
197	Neural Networks for Predicting Algorithm Runtime Distributions	Katharina Eggensperger, Marius Lindauer, Frank Hutter	Many state-of-the-art algorithms for solving hard combinatorial problems in artificial intelligence (AI) include elements of stochasticity that lead to high variations in runtime, even for a fixed problem instance.
198	An Exact Algorithm for Maximum k-Plexes in Massive Graphs	Jian Gao, Jiejiang Chen, Minghao Yin, Rong Chen, Yiyuan Wang	The aim of this paper is to propose a novel exact k-plex algorithm that can deal with large-scaled graphs with millions of vertices and edges.
199	A Fast Algorithm for Optimally Finding Partially Disjoint Shortest Paths	Longkun Guo, Yunyun Deng, Kewen Liao, Qiang He, Timos Sellis, Zheshan Hu	In this paper, we consider the problem of finding k shortest paths which are edge disjoint but partially vertex disjoint.
200	Best-Case and Worst-Case Behavior of Greedy Best-First Search	Manuel Heusner, Thomas Keller, Malte Helmert	We study the impact of tie-breaking on the behavior of greedy best-first search with a fixed state space and fixed heuristic.
201	Knowledge-Guided Agent-Tactic-Aware Learning for StarCraft Micromanagement	Yue Hu, Juntao Li, Xi Li, Gang Pan, Mingliang Xu	In this paper, we propose a novel knowledge-guided agent-tactic-aware learning scheme, that is, opponent-guided tactic learning (OGTL), to cope with this micromanagement problem.
202	Approximation Guarantees of Stochastic Greedy Algorithms for Subset Selection	Chao Qian, Yang Yu, Ke Tang	Greedy algorithms are widely used for subset selection, and have shown good approximation performances in deterministic situations.
203	Sequence Selection by Pareto Optimization	Chao Qian, Chao Feng, Ke Tang	In this paper, we propose an anytime randomized iterative approach POSeqSel, which maximizes the given objective function and minimizes the sequence length simultaneously.
204	Distributed Pareto Optimization for Subset Selection	Chao Qian, Guiying Li, Chao Feng, Ke Tang	In this paper, we propose a distributed version of POSS (DPOSS) with a bounded approximation guarantee.
205	Meta-Level Control of Anytime Algorithms with Online Performance Prediction	Justin Svegliato, Kyle Hollins Wray, Shlomo Zilberstein	We formally introduce an online performance prediction framework that enables meta-level control to adapt to each instance of a problem without any preprocessing.
206	Understanding Subgoal Graphs by Augmenting Contraction Hierarchies	Tansel Uras, Sven Koenig	In this paper, we break down the differences between N-level subgoal graphs and contraction hierarchies, and augment contraction hierarchies with ideas from subgoal graphs to exploit R.
207	A Fast Local Search Algorithm for Minimum Weight Dominating Set Problem on Massive Graphs	Yiyuan Wang, Shaowei Cai, Jiejiang Chen, Minghao Yin	In this paper, we design a fast local search algorithm called FastMWDS for the MWDS problem, which aims to obtain a good solution on massive graphs within a short time.
208	Master-Slave Curriculum Design for Reinforcement Learning	Yuechen Wu, Wei Zhang, Ke Song	Instead, we present a novel curriculum learning strategy by introducing the concept of master-slave agents and enabling flexible action setting for agent training.
209	On the Cost Complexity of Crowdsourcing	Yili Fang, Hailong Sun, Pengpeng Chen, Jinpeng Huai	This work, for the first time, defines the cost complexity of crowdsourcing, and presents two theorems to compute the cost complexity.
210	A Novel Strategy for Active Task Assignment in Crowd Labeling	Zehong Hu, Jie Zhang	A Novel Strategy for Active Task Assignment in Crowd Labeling
211	Deep Learning Based Multi-modal Addressee Recognition in Visual Scenes with Utterances	Thao Le Minh, Nobuyuki Shimizu, Takashi Miyazaki, Koichi Shinoda	To the best of our knowledge, we are the first to introduce an end-to-end deep learning model that combines vision and transcripts of utterance for addressee recognition. Because previous studies typically focused only on pre-specified tasks with limited conversational situations such as controlling smart homes, we created a mock dataset called Addressee Recognition in Visual Scenes with Utterances (ARVSU) that contains a vast body of image variations in visual scenes with an annotated utterance and a corresponding addressee for each scenario.
212	Simultaneous Clustering and Ranking from Pairwise Comparisons	Jiyi Li, Yukino Baba, Hisashi Kashima	Our research question in this paper is whether the pairwise comparisons for clustering also help ranking (and vice versa).
213	A Novel Neural Network Model based on Cerebral Hemispheric Asymmetry for EEG Emotion Recognition	Yang Li, Wenming Zheng, Zhen Cui, Tong Zhang, Yuan Zong	In this paper, we propose a novel neural network model, called bi-hemispheres domain adversarial neural network (BiDANN), for EEG emotion recognition.
214	On the Efficiency of Data Collection for Crowdsourced Classification	Edoardo Manino, Long Tran-Thanh, Nicholas R. Jennings	In this paper we provide the first theoretical explanation of the accuracy gap between the most popular collection policies: the non-adaptive uniform allocation, and the adaptive uncertainty sampling and information gain maximisation.
215	Similarity-Based Reasoning, Raven’s Matrices, and General Intelligence	Can Serif Mekik, Ron Sun, David Yun Dai	This paper presents a model tackling a variant of the Raven’s Matrices family of human intelligence tests along with computational experiments.
216	NPE: Neural Personalized Embedding for Collaborative Filtering	ThaiBinh Nguyen, Atsuhiro Takasu	To address these problems, we propose a neural personalized embedding (NPE) model, which improves the recommendation performance for cold-users and can learn effective representations of items.
217	Algorithms for Fair Load Shedding in Developing Countries	Olabambo I. Oluwasuji, Obaid Malik, Jie Zhang, Sarvapali D. Ramchurn	In this paper, we present a number of optimization heuristics that focus on pairwise and groupwise fairness, such that households (i.e. agents) are fairly allocated electricity.
218	Jointly Learning Network Connections and Link Weights in Spiking Neural Networks	Yu Qi, Jiangrong Shen, Yueming Wang, Huajin Tang, Hang Yu, Zhaohui Wu, Gang Pan	This paper proposes a method to jointly learn network connections and link weights simultaneously.
219	A Simple Convolutional Neural Network for Accurate P300 Detection and Character Spelling in Brain Computer Interface	Hongchang Shan, Yu Liu, Todor Stefanov	To address these issues, we propose a novel and simple CNN which effectively learns feature representations from both raw temporal information and raw spatial information.
220	Cross-Domain Depression Detection via Harvesting Social Media	Tiancheng Shen, Jia Jia, Guangyao Shen, Fuli Feng, Xiangnan He, Huanbo Luan, Jie Tang, Thanassis Tiropanis, Tat-Seng Chua, Wendy Hall	In this paper, we study an interesting but challenging problem of enhancing detection in a certain target domain (e.g. Weibo) with ample Twitter data as the source domain.
221	Synthesizing Pattern Programs from Examples	Sunbeom So, Hakjoo Oh	In this paper, we present a method for synthesizing pattern programs from examples, allowing students to improve their programming skills efficiently.
222	Learning Sequential Correlation for User Generated Textual Content Popularity Prediction	Wen Wang, Wei Zhang, Jun Wang, Junchi Yan, Hongyuan Zha	In this paper, we go deeper into this problem based on the two observations for each user, i.e., sequential content correlation and sequential popularity correlation.
223	Neural Framework for Joint Evolution Modeling of User Feedback and Social Links in Dynamic Social Networks	Peizhi Wu, Yi Tu, Xiaojie Yuan, Adam Jatowt, Zhenglu Yang	This work proposes a comprehensive general neural framework with several optimal strategies to jointly model the evolution of user feedback and social links.
224	Memory Attention Networks for Skeleton-based Action Recognition	Chunyu Xie, Ce Li, Baochang Zhang, Chen Chen, Jungong Han, Jianzhuang Liu	In this work, we propose a temporal-then-spatial recalibration scheme to alleviate such complex variations, resulting in an end-to-end Memory Attention Networks (MANs) which consist of a Temporal Attention Recalibration Module (TARM) and a Spatio-Temporal Convolution Module (STCM).
225	CSNN: An Augmented Spiking based Framework with Perceptron-Inception	Qi Xu, Yu Qi, Hang Yu, Jiangrong Shen, Huajin Tang, Gang Pan	We propose a CNN-SNN (CSNN) model to combine feature learning ability of CNNs with cognition ability of SNNs.
226	Brain-inspired Balanced Tuning for Spiking Neural Networks	Tielin Zhang, Yi Zeng, Dongcheng Zhao, Bo Xu	Here we propose an alternative biological inspired balanced tuning approach to train SNNs.
227	Personality-Aware Personalized Emotion Recognition from Physiological Signals	Sicheng Zhao, Guiguang Ding, Jungong Han, Yue Gao	We propose to investigate the influence of personality on emotional behavior in a hypergraph learning framework.
228	Query Answering in Propositional Circumscription	Mario Alviano	Propositional circumscription defines a preference relation over the models of a propositional theory, so that models being subset-minimal on the interpretation of a set of objective atoms are preferred.The complexity of several computational tasks increase by one level in the polynomial hierarchy due to such a preference relation;among them there is query answering, which amounts to decide whether there is an optimal model satisfying the query.A complete algorithm for query answering is obtained by searching for a model, not necessarily an optimal one, that satisfies the query, and such that no model unsatisfying the query is more preferred.If the query or its complement are among the objective atoms, the algorithm has a simpler behavior, which is also described in the paper.Moreover, an incomplete algorithm is obtained by searching for a model satisfying both the query and an objective atom being unit-implied by the theory extended with the complement of the query.A prototypical implementation is tested on instances from the 2nd International Competition on Computational Models of Argumentation (ICCMA’17).
229	Enhancing Existential Rules by Closed-World Variables	Giovanni Amendola, Nicola Leone, Marco Manna, Pierfrancesco Veltri	In this paper, we enhance existential rules by closed-world variables to consciously reason on the properties of “known” (non-anonymous) and arbitrary individuals in different ways.
230	Explainable Certain Answers	Giovanni Amendola, Leonid Libkin	We present a general framework for reasoning about them, and show that for open and closed world relational databases, they are precisely the common intersection-based notions of certainty.
231	Compiling Model Representations for Querying Large ABoxes in Expressive DLs	Labinot Bajraktari, Magdalena Ortiz, Mantas Simkus	Towards narrowing the gap, we propose an algorithm to compile a representation of sets of models for ALCHI ontologies, which is sufficient for answering any monotone OMQ.
232	Abstraction of Agents Executing Online and their Abilities in the Situation Calculus	Bita Banihashemi, Giuseppe De Giacomo, Yves Lespérance	We develop a general framework for abstracting online behavior of an agent that may acquire new knowledge during execution (e.g., by sensing), in the situation calculus and ConGolog.
233	First-Order Rewritability of Frontier-Guarded Ontology-Mediated Queries	Pablo Barceló, Gerald Berger, Carsten Lutz, Andreas Pieris	We focus on ontology-mediated queries (OMQs) based on (frontier-)guarded existential rules and (unions of) conjunctive queries, and we investigate the problem of FO-rewritability, i.e., whether an OMQ can be rewritten as a first-order query.
234	Single-Shot Epistemic Logic Program Solving	Manuel Bichler, Michael Morak, Stefan Woltran	Single-Shot Epistemic Logic Program Solving
235	Inconsistency-Tolerant Ontology-Based Data Access Revisited: Taking Mappings into Account	Meghyn Bienvenu	After formalizing the problem, we perform a detailed analysis of the data complexity of inconsistency-tolerant OBDA for ontologies formulated in DL-Lite and other data-tractable description logics, considering three different semantics (AR, IAR, and brave), two notions of repairs (subset and symmetric difference), and two classes of global-as-view (GAV) mappings.
236	Actual Causality in a Logical Setting	Alexander Bochman	We provide a definition of actual causation in the logical framework of the causal calculus, which is based on a causal version of the well-known NESS (or INUS) condition.
237	Exploiting Justifications for Lazy Grounding of Answer Set Programs	Bart Bogaerts, Antonius Weinzierl	In this paper, we show how top-down query mechanisms can be used to analyze the situation, learn a new clause or nogood, and backjump further in the search tree.
238	Fast Compliance Checking in an OWL2 Fragment	Piero A. Bonatti	We provide a complete and tractable structural subsumption algorithm for compliance checking and prove the intractability of a natural generalization of the policy language.
239	Relevance in Structured Argumentation	AnneMarie Borg, Christian Straßer	In this paper we investigate properties of structured argumentation systems that warrant relevance desiderata.
240	Learning Conceptual Space Representations of Interrelated Concepts	Zied Bouraoui, Steven Schockaert	In this paper, we introduce a Bayesian model which addresses this problem by constructing informative priors from background knowledge about how the concepts of interest are interrelated with each other.
241	Embracing Change by Abstraction Materialization Maintenance for Large ABoxes	Markus Brenner, Birte Glimm	In this paper, we show how Abstraction Refinement can be adopted for incremental ABox materialization by combining it with the well-known DRed algorithm for materialization maintenance.
242	The Complexity of Limited Belief Reasoning—The Quantifier-Free Case	Yijia Chen, Abdallah Saffidine, Christoph Schwering	This paper investigates the computational complexity of reasoning with belief levels.
243	Belief Update in the Horn Fragment	Nadia Creignou, Adrian Haret, Odile Papini, Stefan Woltran	In line with recent work on belief change in fragments of propositional logic, we study belief update in the Horn fragment.
244	A Study of Argumentative Characterisations of Preferred Subtheories	Marcello D’Agostino, Sanjay Modgil	Two key contributions are made.
245	Game Description Language and Dynamic Epistemic Logic Compared	Thorsten Engesser, Robert Mattmüller, Bernhard Nebel, Michael Thielscher	In this paper, we formally study the expressiveness of GDL-III vs. DEL.
246	Probabilistic bipolar abstract argumentation frameworks: complexity results	Bettina Fazzinga, Sergio Flesca, Filippo Furfaro	Probabilistic Bipolar Abstract Argumentation Frameworks (prBAFs), combining the possibility of specifying supports between arguments with a probabilistic modeling of the uncertainty, are considered, and the complexity of the fundamentalproblem of computing extensions’ probabilities is addressed.The most popular semantics of supports and extensions are considered, as well as different paradigms for defining the probabilistic encoding of the uncertainty.Interestingly, the presence of supports, which does not alter the complexity of verifying extensions in the deterministic case, is shown to introduce a new source of complexity in some probabilistic settings, for which tractable cases are also identified.
247	From Conjunctive Queries to Instance Queries in Ontology-Mediated Querying	Cristina Feier, Carsten Lutz, Frank Wolter	We consider ontology-mediated queries (OMQs) based on expressive description logics of the ALC family and (unions) of conjunctive queries, studying the rewritability into OMQs based on instance queries (IQs).
248	An Empirical Study of Knowledge Tradeoffs in Case-Based Reasoning	Devi Ganesan, Sutanu Chakraborti	Case-Based Reasoning provides a framework for integrating domain knowledge with data in the form of four knowledge containers namely Case base, Vocabulary, Similarity and Adaptation.
249	Possibilistic ASP Base Revision by Certain Input	Laurent Garcia, Claire Lefèvre, Odile Papini, Igor Stéphan, Eric Würbel	The paper proposes two approaches of rule-based revision operators and presents their semantic characterization in terms of possibilistic distribution.
250	Finite Model Reasoning in Hybrid Classes of Existential Rules	Georg Gottlob, Marco Manna, Andreas Pieris	Closing this problem is the main goal of this work.
251	Computing Approximate Query Answers over Inconsistent Knowledge Bases	Sergio Greco, Cristian Molinaro, Irina Trubitsyna	To overcome this limitation, we propose a new notion of repair allowing values within facts to be updated for restoring consistency.
252	Reverse Engineering Queries in Ontology-Enriched Systems: The Case of Expressive Horn Description Logic Ontologies	Víctor Gutiérrez-Basulto, Jean Christoph Jung, Leif Sabellek	We introduce the query-by-example (QBE) paradigm for query answering in the presence of ontologies.
253	Two Sides of the Same Coin: Belief Revision and Enforcing Arguments	Adrian Haret, Johannes P. Wallner, Stefan Woltran	We study a type of change on knowledge bases inspired by the dynamics of formal argumentation systems, where the goal is to enforce acceptance of certain arguments.
254	Horn-Rewritability vs PTime Query Evaluation in Ontology-Mediated Querying	Andre Hernich, Carsten Lutz, Fabio Papacchini, Frank Wolter	We investigate in which cases (1) and (2) are equivalent, finding that the answer depends on whether the unique name assumption (UNA) is made, on the description logic under consideration, and on the nesting depth of quantifiers in the TBox.
255	On the Conditional Logic of Simulation Models	Duligur Ibeling, Thomas Icard	We propose analyzing conditional reasoning by appeal to a notion of intervention on a simulation program, formalizing and subsuming a number of approaches to conditional thinking in the recent AI literature.
256	Stratified Negation in Limit Datalog Programs	Mark Kaminski, Bernardo Cuenca Grau, Egor V. Kostylev, Boris Motik, Ian Horrocks	In this paper, we study an extension of limit programs with stratified negation-as-failure.
257	Counterfactual Resimulation for Causal Analysis of Rule-Based Models	Jonathan Laurent, Jean Yang, Walter Fontana	We provide a semantics of counterfactual statements that addresses this challenge by sampling counterfactual trajectories that are probabilistically as close to the factual trace as a given intervention permits them to be.
258	Pseudo-Boolean Constraints from a Knowledge Representation Perspective	Daniel Le Berre, Pierre Marquis, Stefan Mengel, Romain Wallon	We study pseudo-Boolean constraints (PBC) and their special case cardinality constraints (CARD) from the perspective of knowledge representation.
259	An Efficient Algorithm To Compute Distance Between Lexicographic Preference Trees	Minyi Li, Borhan Kazimipour	In this paper, we consider the well-known model, namely, lexicographic preference trees (LP-trees), for representing agents’ preferences in combinatorial domains.
260	Novel Algorithms for Abstract Dialectical Frameworks based on Complexity Analysis of Subclasses and SAT Solving	Thomas Linsbichler, Marco Maratea, Andreas Niskanen, Johannes P. Wallner, Stefan Woltran	In this paper, we tackle this issue by (i) analyzing the complexity of ADFs under structural restrictions, (ii) presenting novel algorithms which make use of these insights, and (iii) empirically evaluating a resulting implementation which relies on calls to SAT solvers.
261	Multi-agent Epistemic Planning with Common Knowledge	Qiang Liu, Yongmei Liu	Our work aims to extend an existing multi-agent epistemic planning framework based on higher-order belief change with the capability to deal with common knowledge.
262	Complexity of Approximate Query Answering under Inconsistency in Datalog+/-	Thomas Lukasiewicz, Enrico Malizia, Cristian Molinaro	In this paper, we analyze the complexity of conjunctive query answering under these two semantics for a wide range of Datalog+/- languages.
263	Incrementally Grounding Expressions for Spatial Relations between Objects	Tiago Mota, Mohan Sridharan	This paper describes an architecture for incrementally learning and revising the grounding of spatial relations between objects.
264	Leveraging Qualitative Reasoning to Improve SFL	Alexandre Perez, Rui Abreu	We propose an approach, named Q-SFL, that leverages qualitative reasoning to augment the information made available to SFL techniques.
265	Two Approaches to Ontology Aggregation Based on Axiom Weakening	Daniele Porello, Nicolas Troquard, Rafael Peñaloza, Roberto Confalonieri, Pietro Galliani, Oliver Kutz	We implement and compare these two approaches.
266	Argumentation-Based Recommendations: Fantastic Explanations and How to Find Them	Antonio Rago, Oana Cocarascu, Francesca Toni	We propose a hybrid method for calculating predicted ratings, built upon an item/aspect-based graph with users’ partially given ratings, that can be naturally used to provide explanations for recommendations, extracted from user-tailored Tripolar Argumentation Frameworks (TFs).
267	Abducing Relations in Continuous Spaces	Taisuke Sato, Katsumi Inoue, Chiaki Sakama	We propose a new approach to abduction, i.e., non-deductive inference to find a hypothesis H for an observation O such that H,KB \|- O where KB is background knowledge.
268	Reasoning about Betweenness and RCC8 Constraints in Qualitative Conceptual Spaces	Steven Schockaert, Sanjiang Li	After showing that this decision problem is PSPACE-hard in general, we introduce an important fragment for which deciding realizability is NP-complete.
269	Consequence-based Reasoning for Description Logics with Disjunction, Inverse Roles, Number Restrictions, and Nominals	David Tena Cucala, Bernardo Cuenca Grau, Ian Horrocks	We present a consequence-based calculus for concept subsumption and classification in the description logic ALCHOIQ, which extends ALC with role hierarchies, inverse roles, number restrictions, and nominals.
270	Inconsistency Measures for Repair Semantics in OBDA	Bruno Yun, Srdjan Vesic, Madalina Croitoru, Pierre Bisquert	In this paper, we place ourselves in the Ontology Based Data Access (OBDA) setting and investigate reasoning with inconsistent existential rules knowledge bases.
271	On Concept Forgetting in Description Logics with Qualified Number Restrictions	Yizheng Zhao, Renate Schmidt	This paper presents a practical method for computing solutions of concept forgetting in the description logic ALCOQ(neg,and,or), basic ALC extended with nominals, qualified number restrictions, role negation, role conjunction and role disjunction.
272	Finding Frequent Entities in Continuous Data	Ferran Alet, Rohan Chitnis, Leslie P. Kaelbling, Tomas Lozano-Perez	Rather than formalize this as a clustering problem, in which all detections must be grouped into hard or soft categories, we formalize it as an instance of the frequent items or heavy hitters problem, which finds groups of tightly clustered objects that have a high density in the feature space.
273	Small-Variance Asymptotics for Nonparametric Bayesian Overlapping Stochastic Blockmodels	Gundeep Arora, Anupreet Porwal, Kanupriya Agarwal, Avani Samdariya, Piyush Rai	In this work, we apply the small variance asymptotics idea to the non-parametric Bayesian LFRM, utilizing the connection between exponential families and Bregman divergence.
274	Convolutional Neural Networks based Click-Through Rate Prediction with Multiple Feature Sequences	Patrick P. K. Chan, Xian Hu, Lili Zhao, Daniel S. Yeung, Dapeng Liu, Lei Xiao	Two multi-sequence models are proposed to learn the information provided by different sequences.
275	Tri-net for Semi-Supervised Deep Learning	Dong-Dong Chen, Wei Wang, Wei Gao, Zhi-Hua Zhou	In this paper, we propose tri-net, a deep neural network which is able to use massive unlabeled data to help learning with limited labeled data.
276	Adversarial Metric Learning	Shuo Chen, Chen Gong, Jian Yang, Xiang Li, Yang Wei, Jun Li	To address this problem, the Adversarial Metric Learning (AML) is proposed in this paper, which automatically generates adversarial pairs to remedy the sampling bias and facilitate robust metric learning.
277	Distributed Primal-Dual Optimization for Non-uniformly Distributed Data	Minhao Cheng, Cho-Jui Hsieh	To resolve this issue, we propose a better way to merge local updates in the primal-dual optimization framework.
278	Solving Separable Nonsmooth Problems Using Frank-Wolfe with Uniform Affine Approximations	Edward Cheung, Yuying Li	In this paper, we propose a modified FW algorithm amenable to nonsmooth functions, subject to a separability assumption, by optimizing for approximation quality over all affine functions, given a neighborhood of interest.
279	Causal Inference in Time Series via Supervised Learning	Yoichi Chikahara, Akinori Fujino	This paper proposes a supervised learning framework that utilizes a classifier instead of regression models.
280	Unifying and Merging Well-trained Deep Neural Networks for Inference Stage	Yi-Min Chou, Yi-Ming Chan, Jia-Hong Lee, Chih-Yi Chiu, Chu-Song Chen	We propose a novel method to merge convolutional neural-nets for the inference stage.
281	Behavior of Analogical Inference w.r.t. Boolean Functions	Miguel Couceiro, Nicolas Hug, Henri Prade, Gilles Richard	We address the accuracy of analogical inference for arbitrary Boolean functions and show that if a function is epsilon-close to an affine function, then the probability of making a wrong prediction is upper bounded by 4 epsilon.
282	Adaptive Collaborative Similarity Learning for Unsupervised Multi-view Feature Selection	Xiao Dong, Lei Zhu, Xuemeng Song, Jingjing Li, Zhiyong Cheng	In this paper, we investigate the research problem of unsupervised multi-view feature selection.
283	Counterexample-Guided Data Augmentation	Tommaso Dreossi, Shromona Ghosh, Xiangyu Yue, Kurt Keutzer, Alberto Sangiovanni-Vincentelli, Sanjit A. Seshia	We present a novel framework for augmenting data sets for machine learning based on counterexamples.
284	Galaxy Network Embedding: A Hierarchical Community Structure Preserving Approach	Lun Du, Zhicong Lu, Yun Wang, Guojie Song, Yiming Wang, Wei Chen	Inspired by the hierarchical structure of galaxies, we propose the Galaxy Network Embedding (GNE) model, which formulates an optimization problem with spherical constraints to describe the hierarchical community structure preserving network embedding.
285	Dynamic Network Embedding : An Extended Approach for Skip-gram based Network Embedding	Lun Du, Yun Wang, Guojie Song, Zhicong Lu, Junshan Wang	In this paper, we propose a stable dynamic embedding framework with high efficiency.
286	Quantum Divide-and-Conquer Anchoring for Separable Non-negative Matrix Factorization	Yuxuan Du, Tongliang Liu, Yinan Li, Runyao Duan, Dacheng Tao	This paper investigates how the power of quantum computation can be capitalized to solve the non-negative matrix factorization with the separability assumption (SNMF) by devising a quantum algorithm based on the divide-and-conquer anchoring (DCA) scheme [Zhou et al., 2013].
287	A Novel Data Representation for Effective Learning in Class Imbalanced Scenarios	Sri Harsha Dumpala, Rupayan Chakraborty, Sunil Kumar Kopparapu	In this paper, we introduce a novel approach that is different from sampling or cost-sensitive learning based techniques, to address the class imbalance problem, where two samples are simultaneously considered to train the classifier.
288	Leveraging Latent Label Distributions for Partial Label Learning	Lei Feng, Bo An	In this paper, we formalize the different labeling confidence levels as the latent label distributions, and propose a novel unified framework to estimate the latent label distributions while training the model simultaneously.
289	Complementary Binary Quantization for Joint Multiple Indexing	Qiang Fu, Xu Han, Xianglong Liu, Jingkuan Song, Cheng Deng	To address the problems, this paper proposes a complementary binary quantization (CBQ) method to jointly learning multiple hash tables.
290	Joint Generative Moment-Matching Network for Learning Structural Latent Code	Hongchang Gao, Heng Huang	In this paper, we propose a novel Joint Generative Moment-Matching Network (JGMMN), which learns the structural latent code for unsupervised inference.
291	Stochastic Second-Order Method for Large-Scale Nonconvex Sparse Learning Models	Hongchang Gao, Heng Huang	In this paper, we propose a linearly convergent stochastic second-order method to optimize this nonconvex problem for large-scale datasets.
292	Cuckoo Feature Hashing: Dynamic Weight Sharing for Sparse Analytics	Jinyang Gao, Beng Chin Ooi, Yanyan Shen, Wang-Chien Lee	In this paper, we develop a feature hashing scheme called Cuckoo Feature Hashing(CCFH) based on the principle behind Cuckoo hashing, a hashing scheme designed to resolve collisions.
293	Active Discriminative Network Representation Learning	Li Gao, Hong Yang, Chuan Zhou, Jia Wu, Shirui Pan, Yue Hu	In this paper, we propose a novel method, termed as ANRMAB, to learn the active discriminative network representations with a multi-armed bandit mechanism in active learning setting.
294	Scalable Rule Learning via Learning Representation	Pouya Ghiasnezhad Omran, Kewen Wang, Zhe Wang	This paper presents a new approach RLvLR to learning rules from KGs by using the technique of embedding in representation learning together with a new sampling method.
295	Teaching Semi-Supervised Classifier via Generalized Distillation	Chen Gong, Xiaojun Chang, Meng Fang, Jian Yang	To address this issue, this paper formulates SSL as a Generalized Distillation (GD) problem, which treats existing SSL algorithm as a learner and introduces a teacher to guide the learner?s training process.
296	Faster Training Algorithms for Structured Sparsity-Inducing Norm	Bin Gu, Xingwang Ju, Xiang Li, Guansheng Zheng	To address this challenge, in this paper, we have developed a more efficient solution for $l_1/l_{\infty}$ group lasso with arbitrary group overlap using an Inexact Proximal-Gradient method.
297	Accelerated Asynchronous Greedy Coordinate Descent Algorithm for SVMs	Bin Gu, Yingying Shan, Xiang Geng, Guansheng Zheng	To address these issues, in this paper we propose an asynchronous accelerated greedy coordinate descent algorithm (AsyAGCD) for SVMs.
298	Regularizing Deep Neural Networks with an Ensemble-based Decorrelation Method	Shuqin Gu, Yuexian Hou, Lipeng Zhang, Yazhou Zhang	In this work, we propose a novel regularizer named Ensemble-based Decorrelation Method (EDM), which is motivated by the idea of the ensemble learning to improve generalization capacity of DNNs.
299	Energy-efficient Amortized Inference with Cascaded Deep Classifiers	Jiaqi Guan, Yang Liu, Qiang Liu, Jian Peng	We address this problem by proposing a novel framework that optimizes the prediction accuracy and energy cost simultaneously, thus enabling effective cost-accuracy trade-off at test time.
300	INITIATOR: Noise-contrastive Estimation for Marked Temporal Point Process	Ruocheng Guo, Jundong Li, Huan Liu	In this work, we propose INITIATOR – a novel training framework based on noise-contrastive estimation to resolve this problem.
301	Experimental Design under the Bradley-Terry Model	Yuan Guo, Peng Tian, Jayashree Kalpathy-Cramer, Susan Ostmo, J.Peter Campbell, Michael F.Chiang, Deniz Erdogmus, Jennifer Dy, Stratis Ioannidis	We study the following experimental design problem: given a budget of expert comparisons, and a set of existing sample labels, we determine the comparison labels to collect that lead to the highest classification improvement.
302	Replicating Active Appearance Model by Generator Network	Tian Han, Jiawen Wu, Ying Nian Wu	In this paper, we show that this behavior can be replicated by a deep generative model called the generator network, which assumes that the observed signals are generated by latent random variables via a top-down convolutional neural network.
303	MIXGAN: Learning Concepts from Different Domains for Mixture Generation	Guang-Yuan Hao, Hong-Xing Yu, Wei-Shi Zheng	In this work, we present an interesting attempt on mixture generation: absorbing different image concepts (e.g., content and style) from different domains and thus generating a new domain with learned concepts.
304	Differential Equations for Modeling Asynchronous Algorithms	Li He, Qi Meng, Wei Chen, Zhi-Ming Ma, Tie-Yan Liu	We introduce the approximation method and study the approximation error.
305	Outer Product-based Neural Collaborative Filtering	Xiangnan He, Xiaoyu Du, Xiang Wang, Feng Tian, Jinhui Tang, Tat-Seng Chua	In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering.
306	Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks	Yang He, Guoliang Kang, Xuanyi Dong, Yanwei Fu, Yi Yang	This paper proposed a Soft Filter Pruning (SFP) method to accelerate the inference procedure of deep Convolutional Neural Networks (CNNs).
307	Time-evolving Text Classification with Deep Neural Networks	Yu He, Jianxin Li, Yangqiu Song, Mutian He, Hao Peng	In this paper, we present the first attempt to explore evolutionary neural network models for time-evolving text classification.
308	Preventing Disparate Treatment in Sequential Decision Making	Hoda Heidari, Andreas Krause	We propose a general framework for post-processing predictions made by a black-box learning model, that guarantees the resulting sequence of outcomes is consistent.
309	Generative Adversarial Positive-Unlabelled Learning	Ming Hou, Brahim Chaib-draa, Chao Li, Qibin Zhao	In this work, we consider the task of classifying binary positive-unlabeled (PU) data.
310	Doubly Aligned Incomplete Multi-view Clustering	Menglei Hu, Songcan Chen	In this paper, we propose a Doubly Aligned Incomplete Multi-view Clustering algorithm (DAIMC) based on weighted semi-nonnegative matrix factorization (semi-NMF).
311	Summarizing Source Code with Transferred API Knowledge	Xing Hu, Ge Li, Xin Xia, David Lo, Shuai Lu, Zhi Jin	In this paper, we propose a novel approach, named TL-CodeSum, which successfully uses API knowledge learned in a different but related task to code summarization.
312	Experienced Optimization with Reusable Directional Model for Hyper-Parameter Search	Yi-Qi Hu, Yang Yu, Zhi-Hua Zhou	To tackle this issue, in this paper, we propose an experienced optimization approach, i.e., learning how to optimize better from a set of historical optimization processes.
313	A Normalized Convolutional Neural Network for Guided Sparse Depth Upsampling	Jiashen Hua, Xiaojin Gong	Inspired by the classical normalized convolution operation, this work proposes a normalized convolutional layer (NCL) implemented in CNNs.
314	Combinatorial Pure Exploration with Continuous and Separable Reward Functions and Its Applications	Weiran Huang, Jungseul Ok, Liang Li, Wei Chen	In this paper, we propose an adaptive learning algorithm for the CPE-CS problem, and analyze its sample complexity.
315	Efficient DNN Neuron Pruning by Minimizing Layer-wise Nonlinear Reconstruction Error	Chunhui Jiang, Guiying Li, Chao Qian, Ke Tang	In this paper, we propose a new layer-wise neuron pruning approach by minimizing the reconstruction error of nonlinear units, which might be more reasonable since the error before and after activation can change significantly.
316	Automatic Gating of Attributes in Deep Structure	Xiaoming Jin, Tao He, Cheng Wan, Lan Yi, Guiguang Ding, Dou Shen	In this paper, we move forward along this new direction by proposing a deep structure named Attribute Gated Deep Belief Network (AG-DBN) that includes a tunable attribute-layer gating mechanism and automatically learns the best way of connecting attributes to appropriate hidden layers.
317	Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification	Zhao Kang, Xiao Lu, Jinfeng Yi, Zenglin Xu	In this paper, we propose a novel MKL framework by following two intuitive assumptions: (i) each kernel is a perturbation of the consensus kernel; and (ii) the kernel that is close to the consensus kernel should be assigned a large weight.
318	Network Approximation using Tensor Sketching	Shiva Prasad Kasiviswanathan, Nina Narodytska, Hongxia Jin	In this paper, we study a fundamental question that arises when designing deep network architectures: Given a target network architecture can we design a `smaller’ network architecture that ‘approximates’ the operation of the target network?
319	Temporal Belief Memory: Imputing Missing Data during RNN Training	Yeo Jin Kim, Min Chi	We propose a bio-inspired approach named Temporal Belief Memory (TBM) for handling missing data with recurrent neural networks (RNNs).
320	Learning SMT(LRA) Constraints using SMT Solvers	Samuel Kolb, Stefano Teso, Andrea Passerini, Luc De Raedt	We introduce SMT(LRA) learning, the task of learning SMT(LRA) formulas from examples of feasible and infeasible instances, and we contribute INCAL, an exact non-greedy algorithm for this setting. We introduce the problem of learning SMT(LRA) constraints from data.
321	HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction	Dejiang Kong, Fei Wu	In this paper, we are interested in the location prediction problem in a weak real time condition and aim to predict users’ movement in next minutes or hours.
322	A Property Testing Framework for the Theoretical Expressivity of Graph Kernels	Nils M. Kriege, Christopher Morris, Anja Rey, Christian Sohler	We introduce a theoretical framework for investigating the expressive power of graph kernels, which is inspired by concepts from the area of property testing.
323	Geometric Enclosing Networks	Trung Le, Hung Vu, Tu Dinh Nguyen, Dinh Phung	We propose in this paper a new geometry-based optimization approach to address this problem.
324	Open Loop Execution of Tree-Search Algorithms	Erwan Lecarpentier, Guillaume Infantes, Charles Lesire, Emmanuel Rachelson	In the context of tree-search stochastic planning algorithms where a generative model is available, we consider on-line planning algorithms building trees in order to recommend an action.
325	Z-Transforms and its Inference on Partially Observable Point Processes	Young Lee, Thanh Vinh Vo, Kar Wai Lim, Harold Soh	This paper proposes an inference framework based on the Z-transform for a specific class of non-homogeneous point processes.
326	Generalization Bounds for Regularized Pairwise Learning	Yunwen Lei, Shao-Bo Lin, Ke Tang	In this paper, we establish a unified generalization error bound for regularized pairwise learning without either Bernstein conditions or capacity assumptions.
327	Optimization based Layer-wise Magnitude-based Pruning for DNN Compression	Guiying Li, Chao Qian, Chunhui Jiang, Xiaofen Lu, Ke Tang	In this paper, we propose an automatic tuning approach based on optimization, named OLMP.
328	Finite Sample Analysis of LSTD with Random Projections and Eligibility Traces	Haifang Li, Yingce Xia, Wensheng Zhang	We propose a new algorithm, LSTD(lambda)-RP, which leverages random projection techniques and takes eligibility traces into consideration to tackle the above two challenges.
329	Deep Joint Semantic-Embedding Hashing	Ning Li, Chao Li, Cheng Deng, Xianglong Liu, Xinbo Gao	In this paper, we propose a novel Deep Joint Semantic-Embedding Hashing (DSEH) approach that contains LabNet and ImgNet.
330	Variance Reduction in Black-box Variational Inference by Adaptive Importance Sampling	Ximing Li, Changchun Li, Jinjin Chi, Jihong Ouyang	This paper aims to investigate how to adaptively obtain better proposal distribution for lower variance.
331	R-SVM+: Robust Learning with Privileged Information	Xue Li, Bo Du, Chang Xu, Yipeng Zhang, Lefei Zhang, Dacheng Tao	This paper proposes a novel Robust SVM+ (RSVM+) algorithm based on a rigorous theoretical analysis.
332	Unsupervised Disentangled Representation Learning with Analogical Relations	Zejian Li, Yongchuan Tang, Yongxing He	In this paper, we propose the analogical training strategy for the unsupervised disentangled representation learning in generative models.
333	Accelerating Convolutional Networks via Global & Dynamic Filter Pruning	Shaohui Lin, Rongrong Ji, Yuchao Li, Yongjian Wu, Feiyue Huang, Baochang Zhang	In this paper, we propose a novel global & dynamic pruning (GDP) scheme to prune redundant filters for CNN acceleration.
334	Episodic Memory Deep Q-Networks	Zichuan Lin, Tianqi Zhao, Guangwen Yang, Lintao Zhang	In this paper, we present a simple yet effective biologically inspired RL algorithm called Episodic Memory Deep Q-Networks (EMDQN), which leverages episodic memory to supervise an agent during training.
335	UCBoost: A Boosting Approach to Tame Complexity and Optimality for Stochastic Bandits	Fang Liu, Sinong Wang, Swapna Buccapatnam, Ness Shroff	In this work, we address the open problem of finding low-complexity near-optimal multi-armed bandit algorithms for sequential decision making problems.
336	Structured Inference for Recurrent Hidden Semi-markov Model	Hao Liu, Lirong He, Haoli Bai, Bo Dai, Kun Bai, Zenglin Xu	To achieve both flexibility and tractability in modeling nonlinear dynamics of discrete variables, we present a structured and stochastic sequential neural network (SSNN), which composes with a generative network and an inference network.
337	High-Order Co-Clustering via Strictly Orthogonal and Symmetric L1-Norm Nonnegative Matrix Tri-Factorization	Kai Liu, Hua Wang	In this paper, we propose an L1 -norm symmetric nonnegative matrix tri-factorization method to solve the HOCC problem.
338	Contextual Outlier Interpretation	Ninghao Liu, Donghwa Shin, Xia Hu	To tackle the issues, in this paper, we propose a Contextual Outlier INterpretation (COIN) framework to explain the abnormality of outliers spotted by detectors.
339	Toward Designing Convergent Deep Operator Splitting Methods for Task-specific Nonconvex Optimization	Risheng Liu, Shichao Cheng, Yi He, Xin Fan, Zhongxuan Luo	To break through the above limits, we introduce a new algorithmic framework, called Learnable Bregman Splitting (LBS), to perform deep-architecture-based operator splitting for nonconvex optimization based on specific task model.
340	Exploiting Graph Regularized Multi-dimensional Hawkes Processes for Modeling Events with Spatio-temporal Characteristics	Yanchi Liu, Tan Yan, Haifeng Chen	In this paper, we introduce a framework to exploit MHP for modeling spatio-temporal events by considering both temporal and spatial information.
341	Learning with Adaptive Neighbors for Image Clustering	Yang Liu, Quanxue Gao, Zhaohua Yang, Shujian Wang	To solve these problems, in this paper, a novel learning model is proposed to learn a graph based on the given data graph such that the new obtained optimal graph is more suitable for the clustering task.
342	Zero Shot Learning via Low-rank Embedded Semantic AutoEncoder	Yang Liu, Quanxue Gao, Jin Li, Jungong Han, Ling Shao	In this paper, we formulate a novel framework named Low-rank Embedded Semantic AutoEncoder (LESAE) to jointly seek a low-rank mapping to link visual features with their semantic representations.
343	Fast Cross-Validation	Yong Liu, Hailun Lin, Lizhong Ding, Weiping Wang, Shizhong Liao	In this paper, we present an approximate approach to CV based on the theoretical notion of Bouligand influence function (BIF) and the Nystr\”{o}m method for kernel methods.
344	Exact Low Tubal Rank Tensor Recovery from Gaussian Measurements	Canyi Lu, Jiashi Feng, Zhouchen Lin, Shuicheng Yan	In this work, with a careful choice of the atomic set, we prove that TNN is a special atomic norm.
345	AAR-CNNs: Auto Adaptive Regularized Convolutional Neural Networks	Yao Lu, Guangming Lu, Yuanrong Xu, Bob Zhang	In order to address the overfitting problem caused by the small or simple training datasets and the large model’s size in Convolutional Neural Networks (CNNs), a novel Auto Adaptive Regularization (AAR) method is proposed in this paper.
346	SDMCH: Supervised Discrete Manifold-Embedded Cross-Modal Hashing	Xin Luo, Xiao-Ya Yin, Liqiang Nie, Xuemeng Song, Yongxin Wang, Xin-Shun Xu	To address these issues, in this paper, we present a novel cross-modal hashing method, named Supervised Discrete Manifold-Embedded Cross-Modal Hashing (SDMCH).
347	Online Heterogeneous Transfer Metric Learning	Yong Luo, Tongliang Liu, Yonggang Wen, Dacheng Tao	We formulate the problem in the online setting so that the optimization is efficient and the model can be adapted to new coming data.
348	Hierarchical Active Learning with Group Proportion Feedback	Zhipeng Luo, Milos Hauskrecht	In this work we solve this problem by exploring a new approach that actively learns classification models from groups, which are subpopulations of instances, and human feedback on the groups.
349	Self-Representative Manifold Concept Factorization with Adaptive Neighbors for Clustering	Sihan Ma, Lefei Zhang, Wenbin Hu, Yipeng Zhang, Jia Wu, Xuelong Li	To further improve the clustering performance, we propose a novel manifold concept factorization model with adaptive neighbor structure to learn a better affinity matrix and clustering indicator matrix at the same time.
350	On Q-learning Convergence for Non-Markov Decision Processes	Sultan Javed Majeed, Marcus Hutter	In this paper, we investigate the behavior of Q-learning when applied to non-MDP and non-ergodic domains which may have infinitely many underlying states.
351	Unpaired Multi-Domain Image Generation via Regularized Conditional GANs	Xudong Mao, Qing Li	In this paper, we study the problem of multi-domain image generation, the goal of which is to generate pairs of corresponding images from different domains.
352	Spectral Feature Scaling Method for Supervised Dimensionality Reduction	Momo Matsuda, Keiichi Morikuni, Tetsuya Sakurai	In this study, we propose new dimensionality reduction methods supervised using the feature scaling associated with the spectral clustering.
353	Interactive Optimal Teaching with Unknown Learners	Francisco S. Melo, Carla Guerra, Manuel Lopes	This paper introduces a new approach for machine teaching that partly addresses the (unavoidable) mismatch between what the teacher assumes about the learning process of the student and the actual process.
354	Neural Machine Translation with Key-Value Memory-Augmented Attention	Fandong Meng, Zhaopeng Tu, Yong Cheng, Haiyang Wu, Junjie Zhai, Yuekui Yang, Di Wang	To alleviate these issues, we propose a novel key-value memory-augmented attention model for NMT, called KVMEMATT.
355	An Information Theory based Approach to Multisource Clustering	Pierre-Alexandre Murena, Jérémie Sublime, Basarab Matei, Antoine Cornuéjols	In this paper, we consider this type of unsupervised ensemble learning as a compression problem and develop a theoretical framework based on algorithmic theory of information suitable for multi-view clustering and collaborative clustering applications.
356	CAGAN: Consistent Adversarial Training Enhanced GANs	Yao Ni, Dandan Song, Xi Zhang, Hao Wu, Lejian Liao	In this paper, we propose a novel approach of adversarial training between one generator and an exponential number of critics which are sampled from the original discriminative neural network via dropout.
357	A Degeneracy Framework for Graph Similarity	Giannis Nikolentzos, Polykarpos Meladianos, Stratis Limnios, Michalis Vazirgiannis	In this paper, we present a general framework for graph similarity which takes into account structure at multiple different scales.
358	Multinomial Logit Bandit with Linear Utility Functions	Mingdong Ou, Nan Li, Shenghuo Zhu, Rong Jin	In this paper, we consider the linear utility MNL choice model whose item utilities are represented as linear functions of d-dimension item features, and propose an algorithm, titled LUMB, to exploit the underlying structure.
359	Adversarially Regularized Graph Autoencoder for Graph Embedding	Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, Chengqi Zhang	In this paper, we propose a novel adversarial graph embedding framework for graph data.
360	Generalization-Aware Structured Regression towards Balancing Bias and Variance	Martin Pavlovski, Fang Zhou, Nino Arsov, Ljupco Kocarev, Zoran Obradovic	In this study, a novel bias-variance balancing objective function is introduced in order to improve generalization performance.
361	Label Embedding Based on Multi-Scale Locality Preservation	Cheng-Lun Peng, An Tao, Xin Geng	This paper proposes a specially designed approach MSLP that achieves label embedding for LDL by Multi-Scale Locality Preserving (MSLP).
362	Cross-modal Bidirectional Translation via Reinforcement Learning	Jinwei Qi, Yuxin Peng	Inspired by neural machine translation to establish a corresponding relationship between two entirely different languages, we attempt to treat images as a special kind of language to provide visual descriptions, so that translation can be conduct between bilingual pair of image and text to effectively explore cross-modal correlation.
363	Adversarial Constraint Learning for Structured Prediction	Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon	We propose a novel framework for simultaneously learning these constraints and using them for supervision, bypassing the difficulty of using domain expertise to manually specify constraints.
364	Robust Auto-Weighted Multi-View Clustering	Pengzhen Ren, Yun Xiao, Pengfei Xu, Jun Guo, Xiaojiang Chen, Xin Wang, Dingyi Fang	To solve this challenging problem, we propose a novel Robust Auto-weighted Multi-view Clustering (RAMC), which aims to learn an optimal graph with exactly k connected components, where k is the number of clusters.
365	Reachability Analysis of Deep Neural Networks with Provable Guarantees	Wenjie Ruan, Xiaowei Huang, Marta Kwiatkowska	We present a novel algorithm based on adaptive nested optimisation to solve the reachability problem.
366	Online Deep Learning: Learning Deep Neural Networks on the Fly	Doyen Sahoo, Quang Pham, Jing Lu, Steven C. H. Hoi	We present a new ODL framework that attempts to tackle the challenges by learning DNN models which dynamically adapt depth from a sequence of training data in an online learning setting.
367	A Bayesian Latent Variable Model of User Preferences with Item Context	Aghiles Salah, Hady W. Lauw	We propose to further leverage the item-item relationships that may reflect various aspects of items that guide users’ choices.
368	Deep Discrete Prototype Multilabel Learning	Xiaobo Shen, Weiwei Liu, Yong Luo, Yew-Soon Ong, Ivor W. Tsang	To fill this gap, this paper proposes a novel deep prototype compression, i.e., DBPC for fast multi-label prediction.
369	Refine or Represent: Residual Networks with Explicit Channel-wise Configuration	Yanyan Shen, Jinyang Gao	We define two types of channel-wise learning styles: Refine and Represent.
370	Positive and Unlabeled Learning via Loss Decomposition and Centroid Estimation	Hong Shi, Shaojun Pan, Jian Yang, Chen Gong	The state-of-the-art algorithms usually cast PU learning as a cost-sensitive learning problem and impose distinct weights to different training examples via a manual or automatic way.
371	Student-t Variational Autoencoder for Robust Density Estimation	Hiroshi Takahashi, Tomoharu Iwata, Yuki Yamanaka, Masanori Yamada, Satoshi Yagi	We propose a robust multivariate density estimator based on the variational autoencoder (VAE).
372	Incomplete Multi-View Weak-Label Learning	Qiaoyu Tan, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Zili Zhang	In this paper, we propose a novel model (iMVWL) to jointly address the two challenges.
373	Exploration by Distributional Reinforcement Learning	Yunhao Tang, Shipra Agrawal	We propose a framework based on distributional reinforcement learning and recent attempts to combine Bayesian parameter updates with deep reinforcement learning.
374	Algorithms or Actions? A Study in Large-Scale Reinforcement Learning	Anderson Rocha Tavares, Sivasubramanian Anbalagan, Leandro Soriano Marcolino, Luiz Chaimowicz	We present synthetic experiments to further study such systems.
375	Deep into Hypersphere: Robust and Unsupervised Anomaly Discovery in Dynamic Networks	Xian Teng, Muheng Yan, Ali Mert Ertugrul, Yu-Ru Lin	We propose a unified anomaly discovery framework “DeepSphere” that simultaneously meet the above two requirements — identifying the anomalous cases and further exploring the cases’ anomalous structure localized in spatial and temporal context.
376	Differentiable Submodular Maximization	Sebastian Tschiatschek, Aytunc Sahin, Andreas Krause	By interpreting the output of greedy maximization algorithms as distributions over sequences of items and smoothening these distributions, we obtain a differentiable objective.
377	Deterministic Binary Filters for Convolutional Neural Networks	Vincent W.-S. Tseng, Sourav Bhattacharya, Javier Fernández Marqués, Milad Alizadeh, Catherine Tong, Nicholas D. Lane	We propose Deterministic Binary Filters, an approach to Convolutional Neural Networks that learns weighting coefficients of predefined orthogonal binary basis instead of the conventional approach of learning directly the convolutional filters.
378	Efficient Adaptive Online Learning via Frequent Directions	Yuanyu Wan, Nan Wei, Lijun Zhang	In this paper, we propose ADA-FD, an efficient variant of ADA-FULL based on a deterministic matrix sketching technique called frequent directions.
379	Cascaded Low Rank and Sparse Representation on Grassmann Manifolds	Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Baocai Yin	In this paper, we propose a novel Cascaded Low Rank and Sparse Representation (CLRSR) method for subspace clustering, which seeks the sparse expression on the former learned low rank latent representation.
380	Minimizing Adaptive Regret with One Gradient per Iteration	Guanghui Wang, Dakuan Zhao, Lijun Zhang	Previous studies have proposed various algorithms to yield low adaptive regret under different scenarios.
381	Progressive Blockwise Knowledge Distillation for Neural Network Acceleration	Hui Wang, Hanbin Zhao, Xi Li, Xu Tan	In this paper, we propose a progressive blockwise learning scheme for teacher-student model distillation at the subnetwork block level.
382	Ranking Preserving Nonnegative Matrix Factorization	Jing Wang, Feng Tian, Weiwei Liu, Xiao Wang, Wenjie Zhang, Kenji Yamanishi	In this paper, we make the first attempt towards incorporating the ordinal relations and propose a novel ranking preserving nonnegative matrix factorization (RPNMF) approach, which enforces the learned representations to be ranked according to the relations.
383	Binary Coding based Label Distribution Learning	Ke Wang, Xin Geng	In this paper, a scalable LDL framework named Binary Coding based Label Distribution Learning (BC-LDL) is proposed for large-scale LDL.
384	Convolutional Memory Blocks for Depth Data Representation Learning	Keze Wang, Liang Lin, Chuangjie Ren, Wei Zhang, Wenxiu Sun	To address this issue, this paper proposes a novel memory network module, called Convolutional Memory Block (CMB), which empowers CNNs with the memory mechanism on handling depth data.
385	Adaptive Graph Guided Embedding for Multi-label Annotation	Lichen Wang, Zhengming Ding, Yun Fu	To this end, we propose a novel Adaptive Graph Guided Embedding (AG2E) approach for multi-label annotation in a semi-supervised fashion, which utilizes limited labeled data associating with large-scale unlabeled data to facilitate learning performance.
386	Iterative Metric Learning for Imbalance Data Classification	Nan Wang, Xibin Zhao, Yu Jiang, Yue Gao	In this paper, we propose a method named Iterative Metric Learning (IML) to explore the correlations among imbalance data and construct an effective data space for classification.
387	Feature Hashing for Network Representation Learning	Qixiang Wang, Shanfeng Wang, Maoguo Gong, Yue Wu	In this paper, we propose a novel algorithm called node2hash based on feature hashing for generating node embeddings.
388	Mixed Link Networks	Wenhai Wang, Xiang Li, Tong Lu, Jian Yang	To combine their advantages and avoid certain limitations on representation learning, we present a highly efficient and modularized Mixed Link Network (MixNet) which is equipped with flexible inner link and outer link modules.
389	New Balanced Active Learning Model and Optimization Algorithm	Xiaoqian Wang, Yijun Huang, Ji Liu, Heng Huang	To solve this challenging problem, we propose a novel active learning model for the early stage of experimental design.
390	Fast Factorization-free Kernel Learning for Unlabeled Chunk Data Streams	Yi Wang, Nan Xue, Xin Fan, Jiebo Luo, Risheng Liu, Bin Chen, Haojie Li, Zhongxuan Luo	This paper proposes a fast factorization-free kernel learning method to unify novelty detection and incremental learning for unlabeled chunk data streams in one framework.
391	Positive and Unlabeled Learning for Detecting Software Functional Clones with Adversarial Training	Hui-Hui Wei, Ming Li	In this paper, we argue that the clone detection task in the real-world should be formalized as a Positive-Unlabeled (PU) learning problem, and address this problem by proposing a novel positive and unlabeled learning approach, namely CDPU, to effectively detect software functional clones, i.e., pieces of codes with similar functionality but differing in both syntactical and lexical level, where adversarial training is employed to improve the robustness of the learned model to those non-clone pairs that look extremely similar but behave differently.
392	Does Tail Label Help for Large-Scale Multi-Label Learning	Tong Wei, Yu-Feng Li	In this paper, we disclose that whatever labels are randomly missing or misclassified, tail labels impact much less than common labels in terms of commonly used performance metrics (Top-$k$ precision and nDCG@$k$).
393	Unsupervised Deep Hashing via Binary Latent Factor Models for Large-scale Cross-modal Retrieval	Gengshen Wu, Zijia Lin, Jungong Han, Li Liu, Guiguang Ding, Baochang Zhang, Jialie Shen	In this paper, we propose a novel multimodal hashing framework, referred as Unsupervised Deep Cross-Modal Hashing (UDCMH), for multimodal data search in a self-taught manner via integrating deep learning and matrix factorization with binary latent factor models.
394	Efficient Attributed Network Embedding via Recursive Randomized Hashing	Wei Wu, Bin Li, Ling Chen, Chengqi Zhang	In this paper, we propose a simple yet effective algorithm, named NetHash, to solve this problem only with moderate computing capacity.
395	Towards Enabling Binary Decomposition for Partial Label Learning	Xuan Wu, Min-Ling Zhang	In this paper, a novel approach is proposed to solving partial label learning problem by adapting the popular one-vs-one decomposition strategy.
396	Cutting the Software Building Efforts in Continuous Integration by Semi-Supervised Online AUC Optimization	Zheng Xie, Ming Li	In this paper, we address these challenges by proposing a semi-supervised online AUC optimization method for CI build outcome prediction.
397	Multi-Label Co-Training	Yuying Xing, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Zili Zhang	To address these issues, we introduce an approach called Multi-Label Co-Training (MLCT).
398	De-biasing Covariance-Regularized Discriminant Analysis	Haoyi Xiong, Wei Cheng, Yanjie Fu, Wenqing Hu, Jiang Bian, Zhishan Guo	Inspired by the recent progress in de-biased Lasso, we propose a novel FLD classifier, DBLD, which improves classification accuracy of CRLD through de-biasing.
399	Deep Multi-View Concept Learning	Cai Xu, Ziyu Guan, Wei Zhao, Yunfei Niu, Quan Wang, Zhiheng Wang	In this work we present a semi-supervised deep multi-view factorization method, named Deep Multi-view Concept Learning (DMCL).
400	Online Continuous-Time Tensor Factorization Based on Pairwise Interactive Point Processes	Hongteng Xu, Dixin Luo, Lawrence Carin	A continuous-time tensor factorization method is developed for event sequences containing multiple “modalities.”
401	MUSCAT: Multi-Scale Spatio-Temporal Learning with Application to Climate Modeling	Jianpeng Xu, Xi Liu, Tyler Wilson, Pang-Ning Tan, Pouyan Hatami, Lifeng Luo	In this paper, we present a novel framework called MUSCAT for predictive modeling of multi-scale, spatio-temporal data.
402	Multi-Level Metric Learning via Smoothed Wasserstein Distance	Jie Xu, Lei Luo, Cheng Deng, Heng Huang	To tackle these problems, in this paper, we propose a multi-level metric learning method using a smoothed Wasserstein distance to characterize the errors between any two samples, where the ground distance is considered as a Mahalanobis distance.
403	Label Enhancement for Label Distribution Learning	Ning Xu, An Tao, Xin Geng	This paper proposes a novel LE algorithm called Graph Laplacian Label Enhancement (GLLE).
404	Convergence Analysis of Gradient Descent for Eigenvector Computation	Zhiqiang Xu, Xin Cao, Xin Gao	In this work, the convergence of the gradient descent solver for the leading eigenvector computation is shown to be at a global rate O(min{ (lambda_1/Delta_p)^2 log(1/epsilon), 1/epsilon }), where Delta_p=lambda_p-lambda_p+1>0 represents the generalized positive eigengap and always exists without loss of generality with lambda_i being the i-th largest eigenvalue of the given real symmetric matrix and p being the multiplicity of lambda_1.
405	PredCNN: Predictive Learning with Cascade Convolutions	Ziru Xu, Yunbo Wang, Mingsheng Long, Jianmin Wang	To tackle this problem, we introduce an entirely CNN-based architecture, PredCNN, that models the dependencies between the next frame and the sequential video inputs.
406	Improving Maximum Likelihood Estimation of Temporal Point Process via Discriminative and Adversarial Learning	Junchi Yan, Xin Liu, Liangliang Shi, Changsheng Li, Hongyuan Zha	This paper aims to improve MLE by discriminative and adversarial learning.
407	A Unified Analysis of Stochastic Momentum Methods for Deep Learning	Yan Yan, Tianbao Yang, Zhe Li, Qihang Lin, Yi Yang	This paper aims to bridge the gap between practice and theory by analyzing the stochastic gradient (SG) method, and the stochastic momentum methods including two famous variants, i.e., the stochastic heavy-ball (SHB) method and the stochastic variant of Nesterov?s accelerated gradient (SNAG) method.
408	Cost-Effective Active Learning for Hierarchical Multi-Label Classification	Yi-Fan Yan, Sheng-Jun Huang	In this paper, we propose a multi-label active learning approach to exploit the label hierarchies for cost-effective queries.
409	Semi-Supervised Optimal Transport for Heterogeneous Domain Adaptation	Yuguang Yan, Wen Li, Hanrui Wu, Huaqing Min, Mingkui Tan, Qingyao Wu	In this paper, we propose a novel semi-supervised algorithm for HDA by exploiting the theory of optimal transport (OT), a powerful tool originally designed for aligning two different distributions.
410	Spatio-Temporal Check-in Time Prediction with Recurrent Neural Network based Survival Analysis	Guolei Yang, Ying Cai, Chandan K Reddy	We introduce a novel check-in time prediction problem.
411	A Unified Approach for Multi-step Temporal-Difference Learning with Eligibility Traces in Reinforcement Learning	Long Yang, Minhao Shi, Qian Zheng, Wenjia Meng, Gang Pan	In this paper, we combine the original Q(σ) with eligibility traces and propose a new algorithm, called Qπ(σ,λ), where λ is trace-decay parameter.
412	Bandit Online Learning on Graphs via Adaptive Optimization	Peng Yang, Peilin Zhao, Xin Gao	To address this issue, we propose a bandit online algorithm on graphs.
413	Semi-Supervised Multi-Modal Learning with Incomplete Modalities	Yang Yang, De-Chuan Zhan, Xiang-Rong Sheng, Yuan Jiang	In this paper, the incomplete feature representation issues in multi-modal learning are named as incomplete modalities, and we propose a semi-supervised multi-modal learning method aimed at this incomplete modal issue (SLIM).
414	High-dimensional Similarity Learning via Dual-sparse Random Projection	Dezhong Yao, Peilin Zhao, Tuan-Anh Nguyen Pham, Gao Cong	In this paper, we propose a dual random projection framework for similarity learning to recover the original optimal solution from subspace optimal solution.
415	Distance Metric Facilitated Transportation between Heterogeneous Domains	Han-Jia Ye, Xiang-Rong Sheng, De-Chuan Zhan, Peng He	In this paper, we focus on transferring between heterogeneous domains, i.e., those with different feature spaces, and propose the Metric Transporation on HEterogeneous REpresentations (MapHere) approach.
416	Stochastic Fractional Hamiltonian Monte Carlo	Nanyang Ye, Zhanxing Zhu	In this paper, we propose a novel stochastic fractional Hamiltonian Monte Carlo approach which generalizes the Hamiltonian Monte Carlo method within the framework of fractional calculus and L\’evy diffusion.
417	Hashing over Predicted Future Frames for Informed Exploration of Deep Reinforcement Learning	Haiyan Yin, Jianda Chen, Sinno Jialin Pan	In this paper, we propose a novel informed exploration framework for deep RL, where we build the capability for an RL agent to predict over the future transitions and evaluate the frequentness for the predicted future frames in a meaningful manner.
418	Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels	Shujian Yu, Xiaoyang Wang, José C. Príncipe	In this paper, a novel Hierarchical Hypothesis Testing framework with Request-and-Reverify strategy is developed to detect concept drifts by requesting labels only when necessary.
419	A Generic Approach for Accelerating Stochastic Zeroth-Order Convex Optimization	Xiaotian Yu, Irwin King, Michael R. Lyu, Tianbao Yang	In this paper, we propose a generic approach for accelerating the convergence of existing algorithms to solve the problem of stochastic zeroth-order convex optimization (SZCO).
420	Mixture of GANs for Clustering	Yang Yu, Wen-Ji Zhou	In this paper, we propose to use the epsilon-expectation-maximization procedure for training GANMM.
421	FISH-MML: Fisher-HSIC Multi-View Metric Learning	Changqing Zhang, Yeqinq Liu, Yue Liu, Qinghua Hu, Xinwang Liu, Pengfei Zhu	This work presents a simple yet effective model for multi-view metric learning, which aims to improve the classification of data with multiple views, e.g., multiple modalities or multiple types of features.
422	Learning Environmental Calibration Actions for Policy Self-Evolution	Chao Zhang, Yang Yu, Zhi-Hua Zhou	We propose the POSEC (POlicy Self-Evolution by Calibration) approach, which learns the most informative calibration actions for policy self-evolution.
423	Learning to Design Games: Strategic Environments in Reinforcement Learning	Haifeng Zhang, Jun Wang, Zhiming Zhou, Weinan Zhang, Yin Wen, Yong Yu, Wenxin Li	In this paper, we extend this setting by considering the environment is not given, but controllable and learnable through its interaction with the agent at the same time.
424	Generative Warfare Nets: Ensemble via Adversaries and Collaborators	Honglun Zhang, Liqiang Xiao, Wenqing Chen, Yongkun Wang, Yaohui Jin	In this paper, we propose the Generative Warfare Nets (GWN) that involve multiple generators and multiple discriminators from two sides to exploit the advantages of Ensemble Learning.
425	Scalable Multiplex Network Embedding	Hongming Zhang, Liwei Qiu, Lingling Yi, Yangqiu Song	In this paper, we present a scalable multiplex network embedding model to represent information of multi-type relations into a unified embedding space.
426	Dynamically Hierarchy Revolution: DirNet for Compressing Recurrent Neural Network on Mobile Devices	Jie Zhang, Xiaolong Wang, Dawei Li, Yalin Wang	To guarantee minimum accuracy loss with higher compression rate and driven by the mobile resource requirement, we introduce a novel model compression approach DirNet based on an optimized fast dictionary learning algorithm, which 1) dynamically mines the dictionary atoms of the projection dictionary matrix within layer to adjust the compression rate 2) adaptively changes the sparsity of sparse codes cross the hierarchical layers.
427	Achieving Non-Discrimination in Prediction	Lu Zhang, Yongkai Wu, Xintao Wu	In this paper, we fill this gap by mathematically bounding the discrimination in prediction.
428	Semi-Supervised Optimal Margin Distribution Machines	Teng Zhang, Zhi-Hua Zhou	In this paper, we propose a novel approach SODM (Semi-supervised Optimal margin Distribution Machine), which tries to assign the label to unlabeled instances and achieve optimal margin distribution simultaneously.
429	Multi-modality Sensor Data Classification with Selective Attention	Xiang Zhang, Lina Yao, Chaoran Huang, Sen Wang, Mingkui Tan, Guodong Long, Can Wang	In this paper,to improve the adaptability of such classificationmethods across different application contexts, weturn this classification task into a game and applya deep reinforcement learning scheme to dynami-cally deal with complex situations.
430	Online Kernel Selection via Incremental Sketched Kernel Alignment	Xiao Zhang, Shizhong Liao	To address this issue, we propose a novel online kernel selection approach via the incremental sketched kernel alignment criterion, which meets all the new challenges.
431	Label-Sensitive Task Grouping by Bayesian Nonparametric Approach for Multi-Task Multi-Label Learning	Xiao Zhang, Wenzhong Li, Vu Nguyen, Fuzhen Zhuang, Hui Xiong, Sanglu Lu	In this paper, we propose a LABel-sensitive TAsk Grouping framework, named LABTAG, based on Bayesian nonparametric approach for multi-task multi-label classification.
432	Multi-Task Clustering with Model Relation Learning	Xiaotong Zhang, Xianchao Zhang, Han Liu, Jiebo Luo	In this paper, we propose a multi-task clustering with model relation learning (MTCMRL) method, which automatically learns the model parameter relatedness between each pair of tasks.
433	Self-Supervised Deep Low-Rank Assignment Model for Prototype Selection	Xingxing Zhang, Zhenfeng Zhu, Yao Zhao, Deqiang Kong	To alleviate this issue, we develop in this paper a Self-supervised Deep Low-rank Assignment model (SDLA).
434	Distributed Self-Paced Learning in Alternating Direction Method of Multipliers	Xuchao Zhang, Liang Zhao, Zhiqian Chen, Chang-Tien Lu	In this paper, we reformulate the self-paced learning problem into a distributed setting and propose a novel Distributed Self-Paced Learning method (DSPL) to handle large scale datasets.
435	ANRL: Attributed Network Representation Learning via Deep Neural Networks	Zhen Zhang, Hongxia Yang, Jiajun Bu, Sheng Zhou, Pinggang Yu, Jianwei Zhang, Martin Ester, Can Wang	In this paper, we propose a novel framework, named ANRL, to incorporate both the network structure and node attribute information in a principled way.
436	Dynamic Hypergraph Structure Learning	Zizhao Zhang, Haojie Lin, Yue Gao	In this method, given the originally generated hypergraph structure, the objective of our work is to simultaneously optimize the label projection matrix (the common task in hypergraph learning) and the hypergraph structure itself.
437	Deep Convolutional Neural Networks with Merge-and-Run Mappings	Liming Zhao, Mingjie Li, Depu Meng, Xi Li, Zhaoxiang Zhang, Yueting Zhuang, Zhuowen Tu, Jingdong Wang	To further reduce the training difficulty, we present a simple network architecture, deep merge-and-run neural networks.
438	Grouping Attribute Recognition for Pedestrian with Joint Recurrent Learning	Xin Zhao, Liufang Sang, Guiguang Ding, Yuchen Guo, Xiaoming Jin	Inspired by Recurrent Neural Network (RNN)’s super capability of learning context correlations, this paper proposes an end-to-end Grouping Recurrent Learning (GRL) model that takes advantage of the intra-group mutual exclusion and inter-group correlation to improve the performance of pedestrian attribute recognition.
439	Attentional Image Retweet Modeling via Multi-Faceted Ranking Network Learning	Zhou Zhao, Lingtao Meng, Jun Xiao, Min Yang, Fei Wu, Deng Cai, Xiaofei He, Yueting Zhuang	In this paper, we study the problem of image retweet prediction in social media, which predicts the image sharing behavior that the user reposts the image tweets from their followees.
440	Robust Feature Selection on Incomplete Data	Wei Zheng, Xiaofeng Zhu, Yonghua Zhu, Shichao Zhang	Robust Feature Selection on Incomplete Data
441	Self-Adaptive Double Bootstrapped DDPG	Zhuobin Zheng, Chun Yuan, Zhihui Lin, Yangyang Cheng, Hanghao Wu	In this work, we propose Self-Adaptive Double Bootstrapped DDPG (SOUP), an algorithm that extends DDPG to bootstrapped actor-critic architecture.
442	Where to Prune: Using LSTM to Guide End-to-end Pruning	Jing Zhong, Guiguang Ding, Yuchen Guo, Jungong Han, Bin Wang	In this paper, we argue that the pruning order is also very significant for model pruning.
443	Trajectory-User Linking via Variational AutoEncoder	Fan Zhou, Qiang Gao, Goce Trajcevski, Kunpeng Zhang, Ting Zhong, Fengli Zhang	We tackle the TUL problem with a semi-supervised learning framework, called TULVAE (TUL via Variational AutoEncoder), which learns the human mobility in a neural generative architecture with stochastic latent variables that span hidden states in RNN.
444	On the Convergence Properties of a K-step Averaging Stochastic Gradient Descent Algorithm for Nonconvex Optimization	Fan Zhou, Guojing Cong	We adopt and analyze a synchronous K-step averaging stochastic gradient descent algorithm which we call K-AVG for solving large scale machine learning problems.
445	Cost-aware Cascading Bandits	Ruida Zhou, Chao Gan, Jing Yang, Cong Shen	In this paper, we propose a cost-aware cascading bandits model, a new variant of multi-armed bandits with cascading feedback, by considering the random cost of pulling arms.
446	Towards Generalized and Efficient Metric Learning on Riemannian Manifold	Pengfei Zhu, Hao Cheng, Qinghua Hu, Qilong Wang, Changqing Zhang	To address above limitations, this paper makes an attempt to propose a generalized and efficient Riemannian manifold metric learning (RMML) method, which can be flexibly adopted to both SPD and Grassmann manifolds.
447	Beyond Similar and Dissimilar Relations : A Kernel Regression Formulation for Metric Learning	Pengfei Zhu, Ren Qi, Qinghua Hu, Qilong Wang, Changqing Zhang, Liu Yang	To this end, in this paper we propose a novel relation alignment metric learning (RAML) formulation to handle the metric learning problem in those scenarios.
448	Fast Model Identification via Physics Engines for Data-Efficient Policy Search	Shaojun Zhu, Andrew Kimmel, Kostas E. Bekris, Abdeslam Boularias	This paper presents a method for identifying mechanical parameters of robots or objects, such as their mass and friction coefficients.
449	Robust Graph Dimensionality Reduction	Xiaofeng Zhu, Cong Lei, Hao Yu, Yonggang Li, Jiangzhang Gan, Shichao Zhang	In this paper, we propose conducting Robust Graph Dimensionality Reduction (RGDR) by learning a transformation matrix to map original high-dimensional data into their low-dimensional intrinsic space without the influence of outliers.
450	Improving Deep Neural Network Sparsity through Decorrelation Regularization	Xiaotian Zhu, Wengang Zhou, Houqiang Li	We propose to suppress such correlation with a new kind of constraint called decorrelation regularization, which explicitly forces the network to learn a set of less correlated filters.
451	Localized Incomplete Multiple Kernel k-means	Xinzhong Zhu, Xinwang Liu, Miaomiao Li, En Zhu, Li Liu, Zhiping Cai, Jianping Yin, Wen Gao	In this paper, we propose a novel localized incomplete multiple kernel k-means (LI-MKKM) algorithm to address this issue.
452	Robust Multi-view Learning via Half-quadratic Minimization	Yonghua Zhu, Xiaofeng Zhu, Wei Zheng	In this paper, we propose a robust multi-view clustering method to address these issues.
453	Sampling for Approximate Bipartite Network Projection	Nesreen Ahmed, Nick Duffield, Liangzhen Xia	This paper presents a new sampling algorithm that provides a fixed size unbiased estimate of the similarity matrix resulting from a bipartite edge stream projection.
454	A Fast and Accurate Method for Estimating People Flow from Spatiotemporal Population Data	Yasunori Akagi, Takuya Nishimura, Takeshi Kurashima, Hiroyuki Toda	This paper proposes a probabilistic model based on collective graphical models that can estimate crowd movement from spatiotemporal population data.
455	Content-Aware Hierarchical Point-of-Interest Embedding Model for Successive POI Recommendation	Buru Chang, Yonggyu Park, Donghyeon Park, Seongsoon Kim, Jaewoo Kang	To address this problem, we propose CAPE, the first content-aware POI embedding model which utilizes text content that provides information about the characteristics of a POI. To validate the efficacy of CAPE, we constructed a large-scale POI dataset.
456	Beyond the Click-Through Rate: Web Link Selection with Multi-level Feedback	Kun Chen, Kechao Cai, Longbo Huang, John C.S. Lui	With this observation, we consider the context-free web link selection problem, where the objective is to maximize revenue while ensuring that the attractiveness is no less than a preset threshold.
457	NeuCast: Seasonal Neural Forecast of Power Grid Time Series	Pudi Chen, Shenghua Liu, Chuan Shi, Bryan Hooi, Bai Wang, Xueqi Cheng	Therefore, we propose NeuCast, a seasonal neural forecasting method, which dynamically models various loads as co-evolving time series in a hidden space, as well as extra weather conditions, in a neural network structure.
458	Predicting Complex Activities from Ongoing Multivariate Time Series	Weihao Cheng, Sarah Erfani, Rui Zhang, Ramamohanarao Kotagiri	In this paper, we propose Simultaneous Complex Activities Recognition and Action Sequence Discovering (SimRAD), an algorithm which predicts a CA over time by mining a sequence of multivariate actions from sensor data using a Deep Neural Network.
459	DELF: A Dual-Embedding based Deep Latent Factor Model for Recommendation	Weiyu Cheng, Yanyan Shen, Yanmin Zhu, Linpeng Huang	Inspired by the idea of NSVD that represents users based on their interacted items, this paper proposes a dual-embedding based deep latent factor model named DELF for recommendation with implicit feedback.
460	Learning to Recognize Transient Sound Events using Attentional Supervision	Szu-Yu Chou, Jyh-Shing Jang, Yi-Hsuan Yang	This paper presents an attempt to learn a neural network model that recognizes more than 500 different sound events from the audio part of user generated videos (UGV).
461	Improving Implicit Recommender Systems with View Data	Jingtao Ding, Guanghui Yu, Xiangnan He, Yuhan Quan, Yong Li, Tat-Seng Chua, Depeng Jin, Jiajie Yu	We propose to model the pairwise ranking relations among purchased, viewed, and non-viewed interactions, being more effective and ﬂexible than typical pointwise matrix factorization (MF) methods.
462	Recurrent Collaborative Filtering for Unifying General and Sequential Recommender	Disheng Dong, Xiaolin Zheng, Ruixun Zhang, Yan Wang	In this paper, we propose a recommendation model named Recurrent Collaborative Filtering (RCF), which unifies both paradigms within a single model.Specifically, we combine recurrent neural network (the sequential recommender part) and matrix factorization model (the general recommender part) in a multi-task learning framework, where we perform joint optimization with shared model parameters enforcing the two parts to regularize each other.
463	Automatic Opioid User Detection from Twitter: Transductive Ensemble Built on Different Meta-graph Based Similarities over Heterogeneous Information Network	Yujie Fan, Yiming Zhang, Yanfang Ye, Xin Li	To combat such deadly epidemic, in this paper, we propose a novel framework named HinOPU to automatically detect opioid users from Twitter, which will assist in sharpening our understanding toward the behavioral process of opioid addiction and treatment.
464	Deep Attributed Network Embedding	Hongchang Gao, Heng Huang	In this paper, we propose a novel deep attributed network embedding approach, which can capture the high non-linearity and preserve various proximities in both topological structure and node attributes.
465	Interpretable Drug Target Prediction Using Deep Neural Representation	Kyle Yingkai Gao, Achille Fokoue, Heng Luo, Arun Iyengar, Sanjoy Dey, Ping Zhang	In this work, we propose an end-to-end neural network model that predicts DTIs directly from low level representations.
466	Recommendation with Multi-Source Heterogeneous Information	Li Gao, Hong Yang, Jia Wu, Chuan Zhou, Weixue Lu, Yue Hu	To this end, we in this paper consider item recommendations based on heterogeneous information sources.
467	Discrete Interventions in Hawkes Processes with Applications in Invasive Species Management	Amrita Gupta, Mehrdad Farajtabar, Bistra Dilkina, Hongyuan Zha	We propose a novel approach to minimize the spread of an invasive species given a limited intervention budget.
468	Aspect-Level Deep Collaborative Filtering via Heterogeneous Information Networks	Xiaotian Han, Chuan Shi, Senzhang Wang, Philip S. Yu, Li Song	In this paper, we propose a Neural network based Aspect-level Collaborative Filtering model (NeuACF) to exploit different aspect latent factors.
469	Interpretable Recommendation via Attraction Modeling: Learning Multilevel Attractiveness over Multimodal Movie Contents	Liang Hu, Songlei Jian, Longbing Cao, Qingkui Chen	Accordingly, we propose attraction modeling to learn and interpret user attractiveness.
470	Integrative Network Embedding via Deep Joint Reconstruction	Di Jin, Meng Ge, Liang Yang, Dongxiao He, Longbiao Wang, Weixiong Zhang	In this paper, we develop a weight-free multi-component network embedding approach by network reconstruction via a deep Autoencoder.
471	Modeling Contemporaneous Basket Sequences with Twin Networks for Next-Item Recommendation	Duc-Trong Le, Hady W. Lauw, Yuan Fang	Given a sequence of a particular type (e.g., purchases)– referred to as the target sequence, we seek to predict the next item expected to appear beyond this sequence.
472	Lightweight Label Propagation for Large-Scale Network Data	De-Ming Liang, Yu-Feng Li	In this paper, we propose a novel algorithm named \algo to deal with large-scale data.
473	GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction	Yuxuan Liang, Songyu Ke, Junbo Zhang, Xiuwen Yi, Yu Zheng	In this paper, we predict the readings of a geo-sensor over several future hours by using a multi-level attention-based recurrent neural network that considers multiple sensors’ readings, meteorological data, and spatial data.
474	Predicting Activity and Location with Multi-task Context Aware Recurrent Neural Network	Dongliang Liao, Weiqing Liu, Yuan Zhong, Jing Li, Guowei Wang	Unlike existing methods, we introduce spatial-activity topics as the latent factor capturing both users’ activity and location preferences.
475	Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation	Guiliang Liu, Oliver Schulte	This paper proposes a new approach to capturing game context: we apply Deep Reinforcement Learning (DRL) to learn an action-value Q function from 3M play-by-play events in the National Hockey League (NHL).
476	Discrete Factorization Machines for Fast Feature-based Recommendation	Han Liu, Xiangnan He, Fuli Feng, Liqiang Nie, Rui Liu, Hanwang Zhang	In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation.
477	Hashtag2Vec: Learning Hashtag Representation with Relational Hierarchical Embedding Model	Jie Liu, Zhicheng He, Yalou Huang	In this paper, we investigate the problem of hashtag embedding by combining the short text content with the various heterogeneous relations in social networks.
478	Dynamic Bayesian Logistic Matrix Factorization for Recommendation with Implicit Feedback	Yong Liu, Lifan Zhao, Guimei Liu, Xinyan Lu, Peng Gao, Xiao-Li Li, Zhihui Jin	To remedy this issue, we have proposed a novel dynamic matrix factorization model, named Dynamic Bayesian Logistic Matrix Factorization (DBLMF), which aims to learn heterogeneous user and item embeddings that are drifting with inconsistent diffusion rates.
479	LC-RNN: A Deep Learning Model for Traffic Speed Prediction	Zhongjian Lv, Jiajie Xu, Kai Zheng, Hongzhi Yin, Pengpeng Zhao, Xiaofang Zhou	In this paper, we propose a novel model, called LC-RNN, to achieve more accurate traffic speed prediction than existing solutions.
480	Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders	Tengfei Ma, Cao Xiao, Jiayu Zhou, Fei Wang	In this paper, we propose to learn accurate and interpretable similarity measures from multiple types of drug features.
481	Your Tweets Reveal What You Like: Introducing Cross-media Content Information into Multi-domain Recommendation	Weizhi Ma, Min Zhang, Chenyang Wang, Cheng Luo, Yiqun Liu, Shaoping Ma	In this paper, we propose a framework to capture the features from user’s off-topic content information in social media and introduce them into Matrix Factorization (MF) based algorithms.
482	From Reality to Perception: Genre-Based Neural Image Style Transfer	Zhuoqi Ma, Nannan Wang, Xinbo Gao, Jie Li	We present a novel genre style transfer framework modeled after the mechanism of actual artwork production. We collect a set of Van Gogh’s paintings and cubist artworks, and their semantically corresponding real world photos.
483	On Whom Should I Perform this Lab Test Next? An Active Feature Elicitation Approach	Sriraam Natarajan, Srijita Das, Nandini Ramanan, Gautam Kunapuli, Predrag Radivojac	We propose an active learning approach which identifies examples that are dissimilar to the ones with the full set of data and acquire the complete set of features for these examples.
484	Hierarchical Electricity Time Series Forecasting for Integrating Consumption Patterns Analysis and Aggregation Consistency	Yue Pang, Bo Yao, Xiangdong Zhou, Yong Zhang, Yiming Xu, Zijing Tan	In this paper, we propose a novel clustering-based hierarchical electricity time series forecasting approach.
485	ANOMALOUS: A Joint Modeling Approach for Anomaly Detection on Attributed Networks	Zhen Peng, Minnan Luo, Jundong Li, Huan Liu, Qinghua Zheng	In this paper, we investigate how to fuse attribute and network structure information more synergistically to avoid the adverse effects brought by noisy and structurally irrelevant attributes.
486	Pairwise-Ranking based Collaborative Recurrent Neural Networks for Clinical Event Prediction	Zhi Qiao, Shiwan Zhao, Cao Xiao, Xiang Li, Yong Qin, Fei Wang	In this paper, we propose to formulate the clinical event prediction problem as an events recommendation problem.
487	Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification	Sungmin Rhee, Seokjun Seo, Sun Kim	To overcome the issue, we propose a hybrid model, which integrates two key components 1) graph convolution neural network (graph CNN) and 2) relation network (RN).
488	Improving Information Centrality of a Node in Complex Networks by Adding Edges	Liren Shan, Yuhao Yi, Zhongzhi Zhang	In this paper, we study the optimization problem of maximizing the information centrality Iv of a given node v in a network with n nodes and m edges, by creating k new edges incident to v.
489	A Joint Learning Approach to Intelligent Job Interview Assessment	Dazhong Shen, Hengshu Zhu, Chen Zhu, Tong Xu, Chao Ma, Hui Xiong	To this end, in this paper, we propose a novel approach to intelligent job interview assessment by learning the large-scale real-world interview data.
490	Discrete Network Embedding	Xiaobo Shen, Shirui Pan, Weiwei Liu, Yew-Soon Ong, Quan-Sen Sun	To address the issue, this paper proposes a novel discrete network embedding (DNE) for more compact representations.
491	Estimating Latent People Flow without Tracking Individuals	Yusuke Tanaka, Tomoharu Iwata, Takeshi Kurashima, Hiroyuki Toda, Naonori Ueda	This paper proposes a probabilistic model for estimating unobserved transition populations between locations from only aggregated data.
492	Power-law Distribution Aware Trust Prediction	Xiao Wang, Ziwei Zhang, Jing Wang, Peng Cui, Shiqiang Yang	In this paper, we propose a simple yet effective method to address the problem of the violated low-rank assumption.
493	Real-time Traffic Pattern Analysis and Inference with Sparse Video Surveillance Information	Yang Wang, Yiwei Xiao, Xike Xie, Ruoyu Chen, Hengchang Liu	To this end, in this paper, we go beyond existing works and tackle the challenges of traffic flow analysis from three perspectives.
494	Predicting the Spatio-Temporal Evolution of Chronic Diseases in Population with Human Mobility Data	Yingzi Wang, Xiao Zhou, Anastasios Noulas, Cecilia Mascolo, Xing Xie, Enhong Chen	In this paper, we leverage a dataset describing the human mobility patterns of citizens in a large metropolitan area.
495	Matrix completion with Preference Ranking for Top-N Recommendation	Zengmao Wang, Yuhong Guo, Bo Du	In this paper, we propose a novel matrix completion method that integrates the low rank and preference ranking characteristics of recommendation matrix under a self-recovery model for top-N recommendation.
496	Where Have You Been? Inferring Career Trajectory from Academic Social Network	Kan Wu, Jie Tang, Chenhui Zhang	We propose a Space-Time Factor Graph Model (STFGM) incorporating spatial and temporal correlations to fulfill the challenging and new task of inferring temporal locations.
497	Extracting Job Title Hierarchy from Career Trajectories: A Bayesian Perspective	Huang Xu, Zhiwen Yu, Bin Guo, Mingfei Teng, Hui Xiong	Specifically, we propose to extract job title hierarchy from employees’ career trajectories.
498	Line separation from topographic maps using regional color and spatial information	Pengfei Xu, Qiguang Miao, Tiange Liu, Xiaojiang Chen, Dingyi Fang	To solve this problem, we propose a novel line separation method using their regional color and spatial information.
499	3-in-1 Correlated Embedding via Adaptive Exploration of the Structure and Semantic Subspaces	Liang Yang, Yuanfang Guo, Di Jin, Huazhu Fu, Xiaochun Cao	A novel generative model is proposed to formulate the generation process of the network and content from the embeddings, with respect to the Bayesian framework.
500	Biharmonic Distance Related Centrality for Edges in Weighted Networks	Yuhao Yi, Liren Shan, Huan Li, Zhongzhi Zhang	In this paper, we propose to use the rate at which the Kirchhoff index changes with respect to the change of resistance of an edge as a measure of importance for this edge in weighted networks.
501	Joint Learning of Phenotypes and Diagnosis-Medication Correspondence via Hidden Interaction Tensor Factorization	Kejing Yin, William K. Cheung, Yang Liu, Benjamin C. M. Fung, Jonathan Poon	To alleviate this limitation, we propose the hidden interaction tensor factorization (HITF) where the diagnosis-medication correspondence and the underlying phenotypes are inferred simultaneously.
502	Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting	Bing Yu, Haoteng Yin, Zhanxing Zhu	In this paper, we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain.
503	Task-Guided and Semantic-Aware Ranking for Academic Author-Paper Correlation Inference	Chuxu Zhang, Lu Yu, Xiangliang Zhang, Nitesh V. Chawla	To address these drawbacks, we propose a task-guided and semantic-aware ranking model.
504	Finding Communities with Hierarchical Semantics by Distinguishing General and Specialized topics	Ge Zhang, Di Jin, Jian Gao, Pengfei Jiao, Françoise Fogelman-Soulié, Xin Huang	To address this problem, we propose a novel probabilistic generative model.
505	DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision	Hanyuan Zhang, Hao Wu, Weiwei Sun, Baihua Zheng	In this paper, we leverage on new development of deep neural networks and propose a novel auxiliary supervision model, namely DeepTravel, that can automatically and effectively extract different features, as well as make full use of the temporal labels of the trajectory data.
506	CoupledCF: Learning Explicit and Implicit User-item Couplings in Recommendation for Deep Collaborative Filtering	Quangui Zhang, Longbing Cao, Chengzhang Zhu, Zhiqiang Li, Jinguang Sun	It leverages existing work that usually treats users/items as in- dependent while ignoring the rich couplings within and between users and items, leading to limited performance improvement.
507	NeuRec: On Nonlinear Transformation for Personalized Ranking	Shuai Zhang, Lina Yao, Aixin Sun, Sen Wang, Guodong Long, Manqing Dong	In this paper, we propose a neural network based recommendation model (NeuRec) that untangles the complexity of user-item interactions and establish an integrated network to combine non-linear transformation with latent factors.
508	PLASTIC: Prioritize Long and Short-term Information in Top-n Recommendation using Adversarial Training	Wei Zhao, Benyou Wang, Jianbo Ye, Yongqiang Gao, Min Yang, Xiaojun Chen	In this paper, we propose a PLASTIC model, Prioritizing Long And Short-Term Information in top-n reCommendation using adversarial training.
509	Open-Ended Long-form Video Question Answering via Adaptive Hierarchical Reinforced Networks	Zhou Zhao, Zhu Zhang, Shuwen Xiao, Zhou Yu, Jun Yu, Deng Cai, Fei Wu, Yueting Zhuang	In this paper, we consider the problem of long-form video question answering from the viewpoint of adaptive hierarchical reinforced encoder-decoder network learning.
510	Multi-Turn Video Question Answering via Multi-Stream Hierarchical Attention Context Network	Zhou Zhao, Xinghua Jiang, Deng Cai, Jun Xiao, Xiaofei He, Shiliang Pu	In this paper, we study the problem of multi-turn video question answering from the viewpoint of multi-step hierarchical attention context network learning.
511	Fast Vehicle Identification in Surveillance via Ranked Semantic Sampling Based Embedding	Feng Zheng, Xin Miao, Heng Huang	To address the problems, in this paper, we propose a Ranked Semantic Sampling (RSS) guided binary embedding method for fast cross-view vehicle Re-IDentification (Re-ID).
512	JUMP: a Jointly Predictor for User Click and Dwell Time	Tengfei Zhou, Hui Qian, Zebang Shen, Chao Zhang, Chengwei Wang, Shichen Liu, Wenwu Ou	In this paper, we propose a joint predictor, JUMP, for both user click and dwell time in session-based settings.
513	A Deep Framework for Cross-Domain and Cross-System Recommendations	Feng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, Mehmet Orgun, Jia Wu	To this end, in this paper, we propose a Deep framework for both Cross-Domain and Cross-System Recommendations, called DCDCSR, based on Matrix Factorization (MF) models and a fully connected Deep Neural Network (DNN).
514	A Local Algorithm for Product Return Prediction in E-Commerce	Yada Zhu, Jianbo Li, Jingrui He, Brian L. Quanz, Ajay A. Deshpande	To address these problems, in this paper, we propose to use a weighted hybrid graph to represent the rich information in the product purchase and return history, in order to predict product returns.
515	Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search	Tao Zhuang, Wenwu Ou, Zhirong Wang	We propose a global optimization framework for mutual influence aware ranking in e-commerce search.
516	Periodic-CRN: A Convolutional Recurrent Model for Crowd Density Prediction with Recurring Periodic Patterns	Ali Zonoozi, Jung-jae Kim, Xiao-Li Li, Gao Cong	To address this lack, we propose novel `Periodic-CRN’ (PCRN) method, which adapts convolutional recurrent network (CRN) to accurately capture spatial and temporal correlations, learns and incorporates explicit periodic representations, and can be optimized with multi-step ahead prediction.
517	Curriculum Adversarial Training	Qi-Zhi Cai, Chang Liu, Dawn Song	In our work, we propose curriculum adversarial training (CAT) to resolve this issue.
518	A^3NCF: An Adaptive Aspect Attention Model for Rating Prediction	Zhiyong Cheng, Ying Ding, Xiangnan He, Lei Zhu, Xuemeng Song, Mohan Kankanhalli	To tackle this problem, in this paper, we develop a novel aspect-aware recommender model named A$^3$NCF, which can capture the varying aspect attentions that a user pays to different items.
519	Fact Checking via Evidence Patterns	Valeria Fionda, Giuseppe Pirrò	We present efficient algorithms to generate and verify evidence patterns, and assemble evidence.
520	Three-Head Neural Network Architecture for Monte Carlo Tree Search	Chao Gao, Martin Müller, Ryan Hayward	We propose a three-head neural net architecture with policy, state- and action-value outputs, which could lead to more efficient MCTS since neural leaf estimate can still be back-propagated in tree with delayed node expansion and evaluation.
521	Adversarial Regression for Detecting Attacks in Cyber-Physical Systems	Amin Ghafouri, Yevgeniy Vorobeychik, Xenofon Koutsoukos	We present a heuristic algorithm for finding an approximately optimal threshold for the defender in this game, and show that it increases system resilience to attacks without significantly increasing the false alarm rate.
522	Tag-based Weakly-supervised Hashing for Image Retrieval	Ziyu Guan, Fei Xie, Wanqing Zhao, Xiaopeng Wang, Long Chen, Wei Zhao, Jinye Peng	Different from previous unsupervised or supervised hashing learning, we propose a novel weakly-supervised deep hashing framework which consists of two stages: weakly-supervised pre-training and supervised fine-tuning. The benefits are two-fold: (1) we could obtain abundant training data for deep hashing models; (2) tagging data possesses richer semantic information which could help better characterize similarity relationships between images.
523	Adversarial Task Assignment	Chen Hajaj, Yevgeniy Vorobeychik	When tasks are heterogeneous, we show that the adversarial assignment problem is NP-Hard, and present an algorithm for solving it approximately.
524	A Social Interaction Activity based Time-Varying User Vectorization Method for Online Social Networks	Tianyi Hao, Longbo Huang	In this paper, we consider the problem of user modeling in online social networks, and propose a social interaction activity based user vectorization framework, called the time-varying user vectorization (Tuv), to infer and make use of important user features.
525	Social Media based Simulation Models for Understanding Disease Dynamics	Ting Hua, Chandan K Reddy, Lei Zhang, Lijing Wang, Liang Zhao, Chang-Tien Lu, Naren Ramakrishnan	To achieve efficient and accurate real-time disease prediction, the framework proposed in this paper combines the strength of social media mining and computational epidemiology.
526	Towards Better Representation Learning for Personalized News Recommendation: a Multi-Channel Deep Fusion Approach	Jianxun Lian, Fuzheng Zhang, Xing Xie, Guangzhong Sun	Following mainstream deep learning-based RSs, we propose a novel deep fusion model (DFM), which aims to improve the representation learning abilities in deep RSs and can be used for both candidate retrieval and item re-ranking.
527	A Non-Parametric Generative Model for Human Trajectories	Kun Ouyang, Reza Shokri, David S. Rosenblum, Wenzhuo Yang	In this paper, we propose and evaluate a novel non-parametric generative model for location trajectories that tries to capture the statistical features of human mobility {\em as a whole}.
528	DyNMF: Role Analytics in Dynamic Social Networks	Yulong Pei, Jianpeng Zhang, George Fletcher, Mykola Pechenizkiy	We propose a novel dynamic non-negative matrix factorization (DyNMF) approach to simultaneously discover roles and learn role transitions.
529	LSTM Networks for Online Cross-Network Recommendations	Dilruk Perera, Roger Zimmermann	We propose a novel multi-layered Long Short-Term Memory (LSTM) network based online solution to mitigate these issues.
530	Neural User Response Generator: Fake News Detection with Collective User Intelligence	Feng Qian, Chengyue Gong, Karishma Sharma, Yan Liu	We propose a novel Two-Level Convolutional Neural Network with User Response Generator (TCNN-URG) where TCNN captures semantic information from article text by representing it at the sentence and word level, and URG learns a generative model of user response to article text from historical user responses which it can use to generate responses to new articles in order to assist fake news detection.
531	Weakly Learning to Match Experts in Online Community	Yujie Qian, Jie Tang, Kan Wu	In this paper, we formally formulate the problem and develop a weakly supervised factor graph (WeakFG) model to address the problem.
532	Optimal Cruiser-Drone Traffic Enforcement Under Energy Limitation	Ariel Rosenfeld, Oleg Maksimov, Sarit Kraus	In this paper, we propose a novel approach where police cruisers act as mobile replenishment providers in addition to their traffic enforcement duties.
533	Path Evaluation and Centralities in Weighted Graphs – An Axiomatic Approach	Jadwiga Sosnowska, Oskar Skibski	Unfortunately, in the existing extensions, paths in the graph are evaluated solely based on their weights, which is a restrictive and undesirable assumption for a variety of settings.
534	MASTER: across Multiple social networks, integrate Attribute and STructure Embedding for Reconciliation	Sen Su, Li Sun, Zhongbao Zhang, Gen Li, Jielun Qu	To address these three limitations, we rethink this problem and propose the MASTER framework, i.e., across Multiple social networks, integrate Attribute and STructure Embedding for Reconciliation.
535	A Group-based Approach to Improve Multifactorial Evolutionary Algorithm	Jing Tang, Yingke Chen, Zixuan Deng, Yanping Xiang, Colin Paul Joy	We propose a group-based MFEA that groups tasks of similar types and selectivelytransfers the genetic information only within the groups.
536	Exploiting POI-Specific Geographical Influence for Point-of-Interest Recommendation	Hao Wang, Huawei Shen, Wentao Ouyang, Xueqi Cheng	In this paper, we exploit POI-specific geographical influence to improve POI recommendation.
537	High-Fidelity Simulated Players for Interactive Narrative Planning	Pengcheng Wang, Jonathan Rowe, Wookhee Min, Bradford Mott, James Lester	In this paper, we propose a novel approach to generating high-fidelity simulated players based on deep recurrent highway networks and deep convolutional networks.
538	Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification	Zheng Wang, Mang Ye, Fan Yang, Xiang Bai, Shin’ichi Satoh	Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification
539	Axiomatization of the PageRank Centrality	Tomasz Wąs, Oskar Skibski	We propose an axiomatization of PageRank.
540	Generating Adversarial Examples with Adversarial Networks	Chaowei Xiao, Bo Li, Jun-yan Zhu, Warren He, Mingyan Liu, Dawn Song	In this paper, we propose AdvGAN to generate adversarial exam- ples with generative adversarial networks (GANs), which can learn and approximate the distribution of original instances.
541	From the Periphery to the Core: Information Brokerage in an Evolving Network	Bo Yan, Yiping Liu, Jiamou Liu, Yijin Cai, Hongyi Su, Hong Zheng	We tackle the question: How would a newcomer harness information brokerage to integrate into a dynamic network going from periphery to center?
542	Representing Urban Functions through Zone Embedding with Human Mobility Patterns	Zijun Yao, Yanjie Fu, Bin Liu, Wangsu Hu, Hui Xiong	To this end, in this paper, we propose a framework to learn the vector representation (embedding) of city zones by exploiting large-scale taxi trajectories.
543	Sequential Recommender System based on Hierarchical Attention Networks	Haochao Ying, Fuzhen Zhuang, Fuzheng Zhang, Yanchi Liu, Guandong Xu, Xing Xie, Hui Xiong, Jian Wu	To this end, in this paper, we propose a novel two-layer hierarchical attention network, which takes the above properties into account, to recommend the next item user might be interested.
544	GELU-Net: A Globally Encrypted, Locally Unencrypted Deep Neural Network for Privacy-Preserved Learning	Qiao Zhang, Cong Wang, Hongyi Wu, Chunsheng Xin, Tran V. Phuong	In this paper, we propose a novel privacy-preserved architecture where clients can collaboratively train a deep model while preserving the privacy of each client’s data.
545	Impression Allocation for Combating Fraud in E-commerce Via Deep Reinforcement Learning with Action Norm Penalty	Mengchen Zhao, Zhao Li, Bo An, Haifeng Lu, Yifan Yang, Chen Chu	In this paper, we focus on improving the platform’s impression allocation mechanism to maximize its profit and reduce the sellers’ fraudulent behaviors simultaneously.
546	A Brand-level Ranking System with the Customized Attention-GRU Model	Yu Zhu, Junxiong Zhu, Jie Hou, Yongliang Li, Beidou Wang, Ziyu Guan, Deng Cai	In this paper, we design the first brand-level ranking system to address this problem.
547	Translations as Additional Contexts for Sentence Classification	Reinald Kim Amplayo, Kyungjae Lee, Jinyoung Yeo, Seung-won Hwang	In contrast, we propose the use of translated sentences as domain-free context that is always available regardless of the domain.
548	Empirical Analysis of Foundational Distinctions in Linked Open Data	Luigi Asprino, Valerio Basile, Paolo Ciancarini, Valentina Presutti	The Web and its Semantic extension (i.e. Linked Open Data) contain open global-scale knowledge and make it available to potentially intelligent machines that want to benefit from it.
549	Think Globally, Embed Locally — Locally Linear Meta-embedding of Words	Danushka Bollegala, Kohei Hayashi, Ken-ichi Kawarabayashi	For this purpose, we propose an unsupervised locally linear meta-embedding learning method that takes pre-trained word embeddings as the input, and produces more accurate meta embeddings.
550	An Encoder-Decoder Framework Translating Natural Language to Database Queries	Ruichu Cai, Boyan Xu, Zhenjie Zhang, Xiaoyan Yang, Zijian Li, Zhihao Liang	In this paper, we consider a special case in machine translation problems, targeting to convert natural language into Structured Query Language (SQL) for data retrieval over relational database.
551	Medical Concept Embedding with Time-Aware Attention	Xiangrui Cai, Jinyang Gao, Kee Yuan Ngiam, Beng Chin Ooi, Ying Zhang, Xiaojie Yuan	In this paper, we propose to incorporate the temporal information to embed medical codes.
552	Point Set Registration for Unsupervised Bilingual Lexicon Induction	Hailong Cao, Tiejun Zhao	Inspired by the observation that word embeddings exhibit isomorphic structure across languages, we propose a novel method to induce a bilingual lexicon from only two sets of word embeddings, which are trained on monolingual source and target data respectively.
553	Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment	Muhao Chen, Yingtao Tian, Kai-Wei Chang, Steven Skiena, Carlo Zaniolo	Since many multilingual KGs also provide literal descriptions of entities, in this paper, we introduce an embedding-based approach which leverages a weakly aligned multilingual KG for semi-supervised cross-lingual learning using entity descriptions.
554	TreeNet: Learning Sentence Representations with Unconstrained Tree Structure	Zhou Cheng, Chun Yuan, Jiancheng Li, Haiqin Yang	In this paper, we proposed a novel neural network, namely TreeNet, to capture sentences structurally over the raw unconstrained constituency trees, where the number of child nodes can be arbitrary.
555	Adversarial Active Learning for Sequences Labeling and Generation	Yue Deng, KaWai Chen, Yilin Shen, Hongxia Jin	We introduce an active learning framework for general sequence learning tasks including sequence labeling and generation.
556	Submodularity-Inspired Data Selection for Goal-Oriented Chatbot Training Based on Sentence Embeddings	Mladen Dimovski, Claudiu Musat, Vladimir Ilievski, Andreea Hossman, Michael Baeriswyl	To alleviate this problem, we propose a technique of data selection in the low-data regime that enables us to train with fewer labeled sentences, thus smaller labelling costs.
557	Domain Adaptation via Tree Kernel Based Maximum Mean Discrepancy for User Consumption Intention Identification	Xiao Ding, Bibo Cai, Ting Liu, Qiankun Shi	In this paper, we propose a deep transfer learning framework for consumption intention identification, to reduce the data bias and enhance the transferability in domain-specific layers.
558	Attention-Fused Deep Matching Network for Natural Language Inference	Chaoqun Duan, Lei Cui, Xinchi Chen, Furu Wei, Conghui Zhu, Tiejun Zhao	In this paper, we present an attention-fused deep matching network (AF-DMN) for natural language inference.
559	A Deep Modular RNN Approach for Ethos Mining	Rory Duthie, Katarzyna Budzynska	We study the UK parliamentary debates which furnish a rich source of ethos with linguistic material signalling the ethotic relationships between politicians.
560	A Question Type Driven Framework to Diversify Visual Question Generation	Zhihao Fan, Zhongyu Wei, Piji Li, Yanyan Lan, Xuanjing Huang	In this paper, we propose a question type driven framework to produce multiple questions for a given image with different focuses.
561	Efficient Pruning of Large Knowledge Graphs	Stefano Faralli, Irene Finocchi, Simone Paolo Ponzetto, Paola Velardi	In this paper we present an efficient and highly accurate algorithm to prune noisy or over-ambiguous knowledge graphs given as input an extensional definition of a domain of interest, namely as a set of instances or concepts.
562	Extracting Action Sequences from Texts Based on Deep Reinforcement Learning	Wenfeng Feng, Hankz Hankui Zhuo, Subbarao Kambhampati	In this paper we aim to extract action sequences from texts in \emph{free} natural language, i.e., without any restricted templates, provided the set of actions is unknown.
563	Improving Low Resource Named Entity Recognition using Cross-lingual Knowledge Transfer	Xiaocheng Feng, Xiachong Feng, Bing Qin, Zhangyin Feng, Ting Liu	Improving Low Resource Named Entity Recognition using Cross-lingual Knowledge Transfer
564	Topic-to-Essay Generation with Neural Networks	Xiaocheng Feng, Ming Liu, Jiahao Liu, Bing Qin, Yibo Sun, Ting Liu	To address this, we develop a multi-topic aware long short-term memory (MTA-LSTM) network.In this model, we maintain a novel multi-topic coverage vector, which learns the weight of each topic and is sequentially updated during the decoding process.Afterwards this vector is fed to an attention model to guide the generator.Moreover, we automatically construct two paragraph-level Chinese essay corpora, 305,000 essay paragraphs and 55,000 question-and-answer pairs.Empirical results show that our approach obtains much better BLEU score compared to various baselines.Furthermore, human judgment shows that MTA-LSTM has the ability to generate essays that are not only coherent but also closely related to the input topics.
565	EZLearn: Exploiting Organic Supervision in Automated Data Annotation	Maxim Grechkin, Hoifung Poon, Bill Howe	In this paper, we introduce an auxiliary natural language processing system for the text modality, and incorporate co-training to reduce noise and augment signal in distant supervision.
566	Approximating Word Ranking and Negative Sampling for Word Embedding	Guibing Guo, Shichang Ouyang, Fajie Yuan, Xingwei Wang	To resolve these issues, we propose OptRank to optimize word ranking and approximate negative sampling for bettering word embedding.
567	Reinforced Mnemonic Reader for Machine Reading Comprehension	Minghao Hu, Yuxing Peng, Zhen Huang, Xipeng Qiu, Furu Wei, Ming Zhou	In this paper, we introduce the Reinforced Mnemonic Reader for machine reading comprehension tasks, which enhances previous attentive readers in two aspects.
568	Improving Entity Recommendation with Search Log and Multi-Task Learning	Jizhou Huang, Wei Zhang, Yaming Sun, Haifeng Wang, Ting Liu	In this work, we believe that the in-session contexts convey valuable evidences that could facilitate the semantic modeling of queries, and take that into consideration for entity recommendation.
569	Goal-Oriented Chatbot Dialog Management Bootstrapping with Transfer Learning	Vladimir Ilievski, Claudiu Musat, Andreea Hossman, Michael Baeriswyl	In this paper we introduce a transfer learning method to mitigate the effects of the low in-domain data availability.
570	Mitigating the Effect of Out-of-Vocabulary Entity Pairs in Matrix Factorization for KB Inference	Prachi Jain, Shikhar Murty, Mausam, Soumen Chakrabarti	To alleviate this issue, we propose three extensions to MF.
571	Learning to Give Feedback: Modeling Attributes Affecting Argument Persuasiveness in Student Essays	Zixuan Ke, Winston Carlile, Nishant Gurrapadi, Vincent Ng	Using a recently released corpus of essays that are simultaneously annotated with argument components, argument persuasiveness scores, and attributes of argument components that impact an argument’s persuasiveness, we design and train the first set of neural models that predict the persuasiveness of an argument and its attributes in a student essay, enabling useful feedback to be provided to students on why their arguments are (un)persuasive in addition to how persuasive they are.
572	ACV-tree: A New Method for Sentence Similarity Modeling	Yuquan Le, Zhi-Jie Wang, Zhe Quan, Jiawei He, Bin Yao	Owing to the success of word embeddings, recently, popular neural network methods have achieved sentence embedding, obtaining attractive performance.
573	An Adaptive Hierarchical Compositional Model for Phrase Embedding	Bing Li, Xiaochun Yang, Bin Wang, Wei Wang, Wei Cui, Xianchao Zhang	In this paper, we present a novel method for jointly learning compositionality and phrase embedding by adaptively weighting different compositions using an implicit hierarchical structure.
574	Multi-modal Sentence Summarization with Modality Attention and Image Filtering	Haoran Li, Junnan Zhu, Tianshang Liu, Jiajun Zhang, Chengqing Zong	In this paper, we introduce a multi-modal sentence summarization task that produces a short summary from a pair of sentence and image. We construct a multimodal sentence summarization dataset and extensive experiments on this dataset demonstrate that our models significantly outperform conventional models which only employ text as input.
575	Code Completion with Neural Attention and Pointer Networks	Jian Li, Yue Wang, Michael R. Lyu, Irwin King	In this paper, inspired by the prevalence of locally repeated terms in program source code, and the recently proposed pointer copy mechanism, we propose a pointer mixture network for better predicting OoV words in code completion.
576	SegBot: A Generic Neural Text Segmentation Model with Pointer Network	Jing Li, Aixin Sun, Shafiq Joty	We propose a generic end-to-end segmentation model called SegBot.
577	Adaboost with Auto-Evaluation for Conversational Models	Juncen Li, Ping Luo, Ganbin Zhou, Fen Lin, Cheng Niu	We propose a boosting method for conversational models to encourage them to generate more human-like dialogs.
578	Non-translational Alignment for Multi-relational Networks	Shengnan Li, Xin Li, Rui Ye, Mingzhong Wang, Haiping Su, Yingzi Ou	Thus, we propose a non-translational approach, which aims to utilize a probabilistic model to offer more robust solutions to the alignment task, by exploring the structural properties as well as leveraging on anchors to project each network onto the same vector space during the process of learning the representation of individual networks.
579	Learning Word Vectors with Linear Constraints: A Matrix Factorization Approach	Wenye Li, Jiawei Zhang, Jianjun Zhou, Laizhong Cui	With the objective of better capturing the semantic and syntactic information inherent in words, we propose two new embedding models based on the singular value decomposition of lexical co-occurrences of words.
580	Aspect Term Extraction with History Attention and Selective Transformation	Xin Li, Lidong Bing, Piji Li, Wai Lam, Zhimou Yang	We present a new framework for tackling ATE.
581	Constructing Narrative Event Evolutionary Graph for Script Event Prediction	Zhongyang Li, Xiao Ding, Ting Liu	To remedy this, we propose constructing an event graph to better utilize the event network information for script event prediction.
582	Deep Text Classification Can be Fooled	Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, Wenchang Shi	In this paper, we present an effective method to craft text adversarial samples, revealing one important yet underestimated fact that DNN-based text classifiers are also prone to adversarial sample attack.
583	Feature Enhancement in Attention for Visual Question Answering	Yuetan Lin, Zhangyang Pang, Donghui Wang, Yueting Zhuang	In order to further improve the accuracy of correlation between region and question in attention, we focus on region representation and propose the idea of feature enhancement, which includes three aspects.
584	Curriculum Learning for Natural Answer Generation	Cao Liu, Shizhu He, Kang Liu, Jun Zhao	To address this problem, we propose a curriculum learning based framework for natural answer generation (CL-NAG), which is able to take full advantage of the valuable learning data from a noisy and uneven-quality corpus.
585	Learning to Explain Ambiguous Headlines of Online News	Tianyu Liu, Wei Wei, Xiaojun Wan	In this paper, we focus on dealing with the information gap caused by the ambiguous news headlines. We define a new task of explaining ambiguous headlines with short informative texts, and build a benchmark dataset for evaluation.
586	Jumper: Learning When to Make Classification Decision in Reading	Xianggen Liu, Lili Mou, Haotian Cui, Zhengdong Lu, Sen Song	In this paper, we propose a novel framework, Jumper, inspired by the cognitive process of text reading, that models text classification as a sequential decision process.
587	Beyond Polarity: Interpretable Financial Sentiment Analysis with Hierarchical Query-driven Attention	Ling Luo, Xiang Ao, Feiyang Pan, Jin Wang, Tong Zhao, Ningzi Yu, Qing He	In this paper, we present an interpretable neural net framework for financial sentiment analysis.
588	A Hierarchical End-to-End Model for Jointly Improving Text Summarization and Sentiment Classification	Shuming Ma, Xu Sun, Junyang Lin, Xuancheng Ren	Based on this idea, we propose a hierarchical end-to-end model for joint learning of text summarization and sentiment classification, where the sentiment classification label is treated as the further “summarization” of the text summarization output.
589	Show and Tell More: Topic-Oriented Multi-Sentence Image Captioning	Yuzhao Mao, Chang Zhou, Xiaojie Wang, Ruifan Li	In this paper, we propose a novel Topic-Oriented Multi-Sentence (\emph{TOMS}) captioning model, which can generate multiple topic-oriented sentences to describe an image.
590	Answering Mixed Type Questions about Daily Living Episodes	Taiki Miyanishi, Jun-ichiro Hirayama, Atsunori Kanemura, Motoaki Kawanabe	We propose a physical-world question-answering (QA) method, where the system answers a text question about the physical world by searching a given sequence of sentences about daily-life episodes.
591	ElimiNet: A Model for Eliminating Options for Reading Comprehension with Multiple Choice Questions	Soham Parikh, Ananya Sai, Preksha Nema, Mitesh Khapra	We propose ElimiNet, a neural network-based model which tries to mimic this process.
592	Assigning Personality/Profile to a Chatting Machine for Coherent Conversation Generation	Qiao Qian, Minlie Huang, Haizhou Zhao, Jingfang Xu, Xiaoyan Zhu	In this paper, we address the issue of generating responses that are coherent to a pre-specified personality or profile.
593	Translating Embeddings for Knowledge Graph Completion with Relation Attention Mechanism	Wei Qian, Cong Fu, Yu Zhu, Deng Cai, Xiaofei He	In this paper, we propose a novel knowledge graph embedding method named TransAt to learn the translation based embedding, relation-related categories of entities and relation-related attention simultaneously.
594	Event Factuality Identification via Generative Adversarial Networks with Auxiliary Classification	Zhong Qian, Peifeng Li, Yue Zhang, Guodong Zhou, Qiaoming Zhu	This paper proposes a two-step framework, first extracting essential factors related with event factuality from raw texts as the input, and then identifying the factuality of events via a Generative Adversarial Network with Auxiliary Classification (AC-GAN).
595	Inferring Temporal Knowledge for Near-Periodic Recurrent Events	Dinesh Raghu, Surag Nair, Mausam	We define the novel problem of extracting and predicting occurrence dates for a class of recurrent events — events that are held periodically as per a near-regular schedule (e.g., conferences, film festivals, sport championships).
596	Learning Out-of-Vocabulary Words in Intelligent Personal Agents	Avik Ray, Yilin Shen, Hongxia Jin	In this paper, we propose novel neural networks based parsers to learn OOV words; one incorporating a new hybrid paraphrase generation model, and an enhanced sequence-to-sequence model.
597	Joint Posterior Revision of NLP Annotations via Ontological Knowledge	Marco Rospocher, Francesco Corcoglioniti	We thus propose a general probabilistic model that explicitly captures the relations between multiple NLP annotations for an entity mention, the ontological entity classes implied by those annotations, and the background ontological knowledge those classes may be consistent with.
598	Interpretable Adversarial Perturbation in Input Embedding Space for Text	Motoki Sato, Jun Suzuki, Hiroyuki Shindo, Yuji Matsumoto	One promising approach directly applies adversarial training developed in the image processing field to the input word embedding space instead of the discrete input space of texts.
599	Functional Partitioning of Ontologies for Natural Language Query Completion in Question Answering Systems	Jaydeep Sen, Ashish Mittal, Diptikalyan Saha, Karthik Sankaranarayanan	In particular, we introduce a novel concept of functional partitioning of an ontology and then design algorithms to intelligently use the components obtained from functional partitioning to extend a state-of-the-art NLIDB system to produce accurate and semantically meaningful query completions in the absence of query logs.
600	Learning to Converse with Noisy Data: Generation with Calibration	Mingyue Shang, Zhenxin Fu, Nanyun Peng, Yansong Feng, Dongyan Zhao, Rui Yan	In this paper, we propose a generation with calibration framework, that allows high- quality data to have more influences on the generation model and reduces the effect of noisy data.
601	Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling	Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Sen Wang, Chengqi Zhang	In this paper, we integrate both soft and hard attention into one context fusion model, “reinforced self-attention (ReSA)”, for the mutual benefit of each other.
602	Listen, Think and Listen Again: Capturing Top-down Auditory Attention for Speaker-independent Speech Separation	Jing Shi, Jiaming Xu, Guangcan Liu, Bo Xu	In this paper, we present a novel neural network based structure motivated by the top-down attention behavior of human when facing complicated acoustical scene.
603	Toward Diverse Text Generation with Inverse Reinforcement Learning	Zhan Shi, Xinchi Chen, Xipeng Qiu, Xuanjing Huang	In order to address these two problems, in this paper, we employ inverse reinforcement learning (IRL) for text generation.
604	Complementary Learning of Word Embeddings	Yan Song, Shuming Shi	In this paper, we propose complementary learning of word embeddings based on the CB and SG model.
605	Joint Learning Embeddings for Chinese Words and their Components via Ladder Structured Networks	Yan Song, Shuming Shi, Jing Li	In this paper, we propose a novel framework, namely, ladder structured networks (LSN), which contains three layers representing word, character and radical and learns their embeddings synchronously.
606	An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems	Yiping Song, Cheng-Te Li, Jian-Yun Nie, Ming Zhang, Dongyan Zhao, Rui Yan	We propose a novel ensemble of retrieval-based and generation-based conversation system.
607	Exploring Encoder-Decoder Model for Distant Supervised Relation Extraction	Sen Su, Ningning Jia, Xiang Cheng, Shuguang Zhu, Ruiping Li	In this paper, we present an encoder-decoder model for distant supervised relation extraction.
608	Bootstrapping Entity Alignment with Knowledge Graph Embedding	Zequn Sun, Wei Hu, Qingheng Zhang, Yuzhong Qu	In this paper, we propose a bootstrapping approach to embedding-based entity alignment.
609	A Weakly Supervised Method for Topic Segmentation and Labeling in Goal-oriented Dialogues via Reinforcement Learning	Ryuichi Takanobu, Minlie Huang, Zhongzhou Zhao, Fenglin Li, Haiqing Chen, Xiaoyan Zhu, Liqiang Nie	We propose a reinforcement learning (RL) method for topic segmentation and labeling in goal-oriented dialogues, which aims to detect topic boundaries among dialogue utterances and assign topic labels to the utterances.
610	Multiway Attention Networks for Modeling Sentence Pairs	Chuanqi Tan, Furu Wei, Wenhui Wang, Weifeng Lv, Ming Zhou	In this paper, we propose the multiway attention networks which employ multiple attention functions to match sentence pairs under the matching-aggregation framework.
611	Get The Point of My Utterance! Learning Towards Effective Responses with Multi-Head Attention Mechanism	Chongyang Tao, Shen Gao, Mingyue Shang, Wei Wu, Dongyan Zhao, Rui Yan	To solve this problem, in this paper, we propose a novel Multi-Head Attention Mechanism (MHAM) for generative dialog systems, which aims at capturing multiple semantic aspects from the user utterance.
612	Hermitian Co-Attention Networks for Text Matching in Asymmetrical Domains	Yi Tay, Anh Tuan Luu, Siu Cheung Hui	In this paper, we argue that Co-Attention models in asymmetrical domains require different treatment as opposed to symmetrical domains, i.e., a concept of word-level directionality should be incorporated while learning word-level similarity scores.
613	One "Ruler" for All Languages: Multi-Lingual Dialogue Evaluation with Adversarial Multi-Task Learning	Xiaowei Tong, Zhenxin Fu, Mingyue Shang, Dongyan Zhao, Rui Yan	To address this issue, we propose an adversarial multi-task neural metric (ADVMT) for multi-lingual dialogue evaluation, with shared feature extraction across languages.
614	Aspect Sentiment Classification with both Word-level and Clause-level Attention Networks	Jingjing Wang, Jie Li, Shoushan Li, Yangyang Kang, Min Zhang, Luo Si, Guodong Zhou	In this paper, we highlight the need for incorporating the importance degrees of both words and clauses inside a sentence and propose a hierarchical network with both word-level and clause-level attentions to aspect sentiment classification.
615	SentiGAN: Generating Sentimental Texts via Mixture Adversarial Networks	Ke Wang, Xiaojun Wan	In this paper, we propose a novel framework – SentiGAN, which has multiple generators and one multi-class discriminator, to address the above problems.
616	A Reinforced Topic-Aware Convolutional Sequence-to-Sequence Model for Abstractive Text Summarization	Li Wang, Junlin Yao, Yunzhe Tao, Li Zhong, Wei Liu, Qiang Du	In this paper, we propose a deep learning approach to tackle the automatic summarization tasks by incorporating topic information into the convolutional sequence-to-sequence (ConvS2S) model and using self-critical sequence training (SCST) for optimization.
617	Joint Extraction of Entities and Relations Based on a Novel Graph Scheme	Shaolei Wang, Yue Zhang, Wanxiang Che, Ting Liu	In this paper, we convert the joint task into a directed graph by designing a novel graph scheme and propose a transition-based approach to generate the directed graph incrementally, which can achieve joint learning through joint decoding.
618	Densely Connected CNN with Multi-scale Feature Attention for Text Classification	Shiyao Wang, Minlie Huang, Zhidong Deng	In this paper, we present a densely connected CNN with multi-scale feature attention for text classification.
619	Transition-based Adversarial Network for Cross-lingual Aspect Extraction	Wenya Wang, Sinno Jialin Pan	To solve it, we develop a novel deep model to transfer knowledge from a source language with labeled training data to a target language without any annotations.
620	Quality Matters: Assessing cQA Pair Quality via Transductive Multi-View Learning	Xiaochi Wei, Heyan Huang, Liqiang Nie, Fuli Feng, Richang Hong, Tat-Seng Chua	Toward this end, we present a transductive multi-view learning model.
621	Instance Weighting with Applications to Cross-domain Text Classification via Trading off Sample Selection Bias and Variance	Rui Xia, Zhenchun Pan, Feng Xu	In this paper, we study the domain adaptation problem from the instance weighting perspective.
622	Transformable Convolutional Neural Network for Text Classification	Liqiang Xiao, Honglun Zhang, Wenqing Chen, Yongkun Wang, Yaohui Jin	In this paper, we propose two modules to provide CNNs with the flexibility for complex features and the adaptability for transformation, namely, transformable convolution and transformable pooling.
623	Scheduled Policy Optimization for Natural Language Communication with Intelligent Agents	Wenhan Xiong, Xiaoxiao Guo, Mo Yu, Shiyu Chang, Bowen Zhou, William Yang Wang	Unlike current methods which start with learning from demonstrations (LfD) and then use reinforcement learning (RL) to fine-tune the model parameters, we propose a novel policy optimization algorithm which can dynamically schedule demonstration learning and RL.
624	Lifelong Domain Word Embedding via Meta-Learning	Hu Xu, Bing Liu, Lei Shu, Philip S. Yu	In this paper, we propose a novel lifelong learning setting for domain embedding.
625	Enhancing Semantic Representations of Bilingual Word Embeddings with Syntactic Dependencies	Linli Xu, Wenjun Ouyang, Xiaoying Ren, Yang Wang, Liang Jiang	To address this issue of different syntactics across different languages, we propose a model of bilingual word embeddings integrating syntactic dependencies (DepBiWE) by producing dependency parse-trees which encode the accurate relative positions for the contexts of aligned words.
626	Smarter Response with Proactive Suggestion: A New Generative Neural Conversation Paradigm	Rui Yan, Dongyan Zhao	In this paper, we propose a new paradigm for neural generative conversations: smarter response with a suggestion is provided given the query.
627	Ensemble Neural Relation Extraction with Adaptive Boosting	Dongdong Yang, Senzhang Wang, Zhoujun Li	In this paper, we propose an ensemble neural network model – Adaptive Boosting LSTMs with Attention, to more effectively perform relation extraction.
628	Generating Thematic Chinese Poetry using Conditional Variational Autoencoders with Hybrid Decoders	Xiaopeng Yang, Xiaowen Lin, Shunda Suo, Ming Li	We present a novel conditional variational autoencoder with a hybrid decoder adding the deconvolutional neural networks to the general recurrent neural networks to fully learn topic information via latent variables.
629	Teaching Machines to Ask Questions	Kaichun Yao, Libo Zhang, Tiejian Luo, Lili Tao, Yanjun Wu	We propose a novel neural network model that aims to generate diverse and human-like natural language questions.
630	Chinese Poetry Generation with a Working Memory Model	Xiaoyuan Yi, Maosong Sun, Ruoyu Li, Zonghan Yang	In this paper, inspired by the theoretical concept in cognitive psychology, we propose a novel Working Memory model for poetry generation.
631	Biased Random Walk based Social Regularization for Word Embeddings	Ziqian Zeng, Xin Liu, Yangqiu Song	In this work, we adopt random walk methods to generate paths on the social graph to model the transitivity explicitly.
632	Reinforcing Coherence for Sequence to Sequence Model in Dialogue Generation	Hainan Zhang, Yanyan Lan, Jiafeng Guo, Jun Xu, Xueqi Cheng	Three different types of coherence models, including an unlearned similarity function, a pretrained semantic matching function, and an end-to-end dual learning architecture, are proposed in this paper.
633	Weakly Supervised Audio Source Separation via Spectrum Energy Preserved Wasserstein Learning	Ning Zhang, Junchi Yan, Yuchen Zhou	We introduce a novel weakly supervised audio source separation approach based on deep adversarial learning.
634	Learning Tag Dependencies for Sequence Tagging	Yuan Zhang, Hongshen Chen, Yihong Zhao, Qun Liu, Dawei Yin	We propose a novel multi-channel model, which handles different ranges of token-tag dependencies and their interactions simultaneously.
635	Towards Reading Comprehension for Long Documents	Yuanxing Zhang, Yangbin Zhang, Kaigui Bian, Xiaoming Li	In this paper, we propose a hierarchical match attention model to instruct the machine to extract answers from a specific short span of passages for the long document reading comprehension (LDRC) task.
636	Text Emotion Distribution Learning via Multi-Task Convolutional Neural Network	Yuxiang Zhang, Jiamei Fu, Dongyu She, Ying Zhang, Senzhang Wang, Jufeng Yang	To address this problem, we introduce emotion distribution learning and propose a multi-task convolutional neural network for text emotion analysis.
637	Neural Networks Incorporating Unlabeled and Partially-labeled Data for Cross-domain Chinese Word Segmentation	Lujun Zhao, Qi Zhang, Peng Wang, Xiaoyu Liu	In this paper, we seek to address the problem of CWS for the resource-poor domains that lack annotated data.
638	Phrase Table as Recommendation Memory for Neural Machine Translation	Yang Zhao, Yining Wang, Jiajun Zhang, Chengqing Zong	In this paper, we propose a method to alleviate this problem by using a phrase table as recommendation memory.
639	Same Representation, Different Attentions: Shareable Sentence Representation Learning from Multiple Tasks	Renjie Zheng, Junkun Chen, Xipeng Qiu	In this paper, we claim that a good sentence representation should be invariant and can benefit the various subsequent tasks.
640	Commonsense Knowledge Aware Conversation Generation with Graph Attention	Hao Zhou, Tom Young, Minlie Huang, Haizhou Zhao, Jingfang Xu, Xiaoyan Zhu	In this paper, we present a novel open-domain conversation generation model to demonstrate how large-scale commonsense knowledge can facilitate language understanding and generation.
641	Differentiated Attentive Representation Learning for Sentence Classification	Qianrong Zhou, Xiaojie Wang, Xuan Dong	In this paper, we alleviate this problem by proposing a differentiated attentive learning model.
642	Multi-modal Predicate Identification using Dynamically Learned Robot Controllers	Saeid Amiri, Suhua Wei, Shiqi Zhang, Jivko Sinapov, Jesse Thomason, Peter Stone	This work enables a robot equipped with an arm to dynamically construct query-oriented MOMDPs for multi-modal predicate identification (MPI) of objects.
643	Scheduling under Uncertainty: A Query-based Approach	Luciana Arantes, Evripidis Bampis, Alexander Kononov, Manthos Letsios, Giorgio Lucarelli, Pierre Sens	In this work, we study two problems: (i) the error-query scheduling problem, whose aim is to reveal enough error-free slots with the minimum number of queries, and (ii) the lexicographic error-query scheduling problem where we seek the earliest error-free slots with the minimum number of queries.
644	Novel Structural Parameters for Acyclic Planning Using Tree Embeddings	Christer Bäckström, Peter Jonsson, Sebastian Ordyniak	We introduce two novel structural parameters for acyclic planning (planning restricted to instances with acyclic causal graphs): up-depth and down-depth.
645	Variable-Delay Controllability	Nikhil Bhargava, Christian Muise, Brian Williams	Our work introduces the substantially more complex setting of determining variable-delay controllability, where an agent learns about events after some unknown but bounded amount of time has passed.
646	Features, Projections, and Representation Change for Generalized Planning	Blai Bonet, Hector Geffner	In this work, we extend the standard formulation of generalized planning to such domains.
647	Planning and Learning with Stochastic Action Sets	Craig Boutilier, Alon Cohen, Avinatan Hassidim, Yishay Mansour, Ofer Meshi, Martin Mladenov, Dale Schuurmans	In this work, we formalize and investigate MDPs with stochastic action sets (SAS-MDPs) to provide these foundations.
648	LTL Realizability via Safety and Reachability Games	Alberto Camacho, Christian Muise, Jorge A. Baier, Sheila A. McIlraith	In this paper, we address the problem of LTL realizability and synthesis.
649	Expectation Optimization with Probabilistic Guarantees in POMDPs with Discounted-Sum Objectives	Krishnendu Chatterjee, Adrián Elgyütt, Petr Novotný, Owen Rouillé	We present several results on the EOPG problem, including the first algorithm to solve it.
650	Computational Approaches for Stochastic Shortest Path on Succinct MDPs	Krishnendu Chatterjee, Hongfei Fu, Amir Goharshady, Nastaran Okati	We consider the stochastic shortest path (SSP) problem for succinct Markov decision processes (MDPs), where the MDP consists of a set of variables, and a set of nondeterministic rules that update the variables.
651	Local Minima, Heavy Tails, and Search Effort for GBFS	Eldan Cohen, J. Christopher Beck	In this work, we show that there is a very strong exponential correlation between the depth of the single deepest local minima encountered in a search and the overall search effort.
652	Analyzing Tie-Breaking Strategies for the A* Algorithm	Augusto B. Corrêa, André G. Pereira, Marcus Ritt	In this paper, we study tie-breaking strategies for A*.
653	Emergency Response Optimization using Online Hybrid Planning	Durga Harish Dayapule, Aswin Raghavan, Prasad Tadepalli, Alan Fern	This paper poses the planning problem faced by the dispatcher responding to urban emergencies as a Hybrid (Discrete and Continuous) State and Action Markov Decision Process (HSA-MDP).
654	Automata-Theoretic Foundations of FOND Planning for LTLf and LDLf Goals	Giuseppe De Giacomo, Sasha Rubin	We consider both strong and strong cyclic plans, and develop foundational automata-based techniques to deal with both cases.
655	Complexity of Scheduling Charging in the Smart Grid	Mathijs de Weerdt, Michael Albert, Vincent Conitzer, Koos van der Linden	We show that for about 20 variants the problem is either in P or weakly NP-hard and dynamic programs exist to compute optimal solutions.
656	Traffic Light Scheduling, Value of Time, and Incentives	Argyrios Deligkas, Erez Karpas, Ron Lavi, Rann Smorodinsky	We study the intersection signalling control problem for cars with heterogeneous valuations of time (VoT).
657	Unchaining the Power of Partial Delete Relaxation, Part II: Finding Plans with Red-Black State Space Search	Maximilian Fickert, Daniel Gnad, Joerg Hoffmann	Here, we explore the generation of plans directly through RBS.
658	Model Checking Probabilistic Epistemic Logic for Probabilistic Multiagent Systems	Chen Fu, Andrea Turrini, Xiaowei Huang, Lei Song, Yuan Feng, Lijun Zhang	In this work we study the model checking problem for probabilistic multiagent systems with respect to the probabilistic epistemic logic PETL, which can specify both temporal and epistemic properties.
659	Goal-HSVI: Heuristic Search Value Iteration for Goal POMDPs	Karel Horák, Branislav Bošanský, Krishnendu Chatterjee	(2) We present a novel algorithm inspired by HSVI, termed Goal-HSVI, and show that our algorithm has convergence guarantees.
660	Learning to Infer Final Plans in Human Team Planning	Joseph Kim, Matthew E. Woicik, Matthew C. Gombolay, Sung-Hyun Son, Julie A. Shah	We present a novel learning technique to infer teams’ final plans directly from a processed form of their planning conversation.
661	Small Undecidable Problems in Epistemic Planning	Sébastien Lê Cong, Sophie Pinchinat, François Schwarzentruber	In that case, we show the epistemic planning problem with 1 public action and 2 propositions to be undecidable, while it is known to be decidable with public actions over finite models.
662	Effect-Abstraction Based Relaxation for Linear Numeric Planning	Dongxu Li, Enrico Scala, Patrik Haslum, Sergiy Bogomolov	Effect-Abstraction Based Relaxation for Linear Numeric Planning
663	Organizing Experience: a Deeper Look at Replay Mechanisms for Sample-Based Planning in Continuous State Domains	Yangchen Pan, Muhammad Zaheer, Adam White, Andrew Patterson, Martha White	The aim of this paper is to revisit sample-based planning, in stochastic and continuous domains with learned models.
664	Scalable Initial State Interdiction for Factored MDPs	Swetasudha Panda, Yevgeniy Vorobeychik	We propose a novel Stackelberg game model of MDP interdiction in which the defender modifies the initial state of the planner, who then responds by computing an optimal policy starting with that state.
665	Counterplanning using Goal Recognition and Landmarks	Alberto Pozanco, Yolanda E-Martín, Susana Fernández, Daniel Borrajo	In this paper, we introduce a fully automated domain-independent approach for counterplanning.
666	Planning in Factored State and Action Spaces with Learned Binarized Neural Network Transition Models	Buser Say, Scott Sanner	In this paper, we leverage the efficiency of Binarized Neural Networks (BNNs) to learn complex state transition models of planning domains with discretized factored state and action spaces.
667	Dynamic Resource Routing using Real-Time Dynamic Programming	Sebastian Schmoll, Matthias Schubert	In this paper, we focus on the setting, where pre-computation is not possible and search policies have to be computed on the fly.
668	Hierarchical Expertise Level Modeling for User Specific Contrastive Explanations	Sarath Sreedharan, Siddharth Srivastava, Subbarao Kambhampati	We propose an approach for addressing this problem by representing the user’s understanding of the task as an abstraction of the domain model that the planner uses.
669	LP Heuristics over Conjunctions: Compilation, Convergence, Nogood Learning	Marcel Steinmetz, Joerg Hoffmann	We design a suitable refinement method to this end.
670	Completeness-Preserving Dominance Techniques for Satisficing Planning	Álvaro Torralba	In this paper, we introduce dominance techniques for satisficing planning.
671	Admissible Abstractions for Near-optimal Task and Motion Planning	William Vega-Brown, Nicholas Roy	We define an admissibility condition for abstractions expressed using angelic semantics and show that these conditions allow us to accelerate planning while preserving the ability to find the optimal motion plan.
672	PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making	Fangkai Yang, Daoming Lyu, Bo Liu, Steven Gustafson	In this paper we present a unified framework PEORL that integrates symbolic planning with hierarchical reinforcement learning (HRL) to cope with decision-making in dynamic environment with uncertainties.
673	Minimax-Regret Querying on Side Effects for Safe Optimality in Factored Markov Decision Processes	Shun Zhang, Edmund H. Durfee, Satinder Singh	We formalize this problem, and develop a planning algorithm that avoids potentially negative side effects given what the agent knows about (un)changeable features.
674	GraspNet: An Efficient Convolutional Neural Network for Real-time Grasp Detection for Low-powered Devices	Umar Asif, Jianbin Tang, Stefan Harrer	In this paper, we propose an efficient CNN architecture which produces high grasp detection accuracy in real-time while maintaining a compact model design.
675	Learning Transferable UAV for Forest Visual Perception	Lyujie Chen, Wufan Wang, Jihong Zhu	In this paper, we propose a new pipeline of training a monocular UAV to fly a collision-free trajectory along the dense forest trail. As gathering high-precision images in the real world is expensive and the off-the-shelf dataset has some deficiencies, we collect a new dense forest trail dataset in a variety of simulated environment in Unreal Engine.
676	Bayesian Active Edge Evaluation on Expensive Graphs	Sanjiban Choudhury, Siddhartha Srinivasa, Sebastian Scherer	We propose a novel framework that combines two DRD algorithms, DIRECT and BISECT, to overcome both issues.
677	Implicit Non-linear Similarity Scoring for Recognizing Unseen Classes	Yuchen Guo, Guiguang Ding, Jungong Han, Sicheng Zhao, Bin Wang	We propose a simple yet effective framework, called Implicit Non-linear Similarity Scoring (ICINESS).
678	Interactive Robot Transition Repair With SMT	Jarrett Holtz, Arjun Guha, Joydeep Biswas	We present interactive SMT- based Robot Transition Repair (SRTR): instead of manually adjusting parameters, we ask the roboticist to identify a few instances where the robot is in a wrong state and what the right state should be.
679	Virtual-to-Real: Learning to Control in Visual Semantic Segmentation	Zhang-Wei Hong, Yu-Ming Chen, Hsuan-Kung Yang, Shih-Yang Su, Tzu-Yun Shann, Yi-Hsiang Chang, Brian Hsi-Lin Ho, Chih-Chieh Tu, Tsu-Ching Hsiao, Hsin-Wei Hsiao, Sih-Pin Lai, Yueh-Chuan Chang, Chun-Yi Lee	This paper proposes a modular architecture for tackling the virtual-to-real problem.
680	Online, Interactive User Guidance for High-dimensional, Constrained Motion Planning	Fahad Islam, Oren Salzman, Maxim Likhachev	We consider the problem of planning a collision-free path for a high-dimensional robot.
681	An Appearance-and-Structure Fusion Network for Object Viewpoint Estimation	Yueying Kao, Weiming Li, Zairan Wang, Dongqing Zou, Ran He, Qiang Wang, Minsu Ahn, Sunghoon Hong	To tackle these problems, a novel Appearance-and-Structure Fusion network, which we call it ASFnet that estimates viewpoint by fusing both appearance and structure information, is proposed in this paper.
682	Learning Unmanned Aerial Vehicle Control for Autonomous Target Following	Siyi Li, Tianbo Liu, Chi Zhang, Dit-Yan Yeung, Shaojie Shen	In this paper, we consider the challenging problem of learning unmanned aerial vehicle (UAV) control for tracking a moving target.
683	Robot Task Interruption by Learning to Switch Among Multiple Models	Anahita Mohseni-Kabir, Manuela Veloso	We present a novel two-step solution.
684	Behavioral Cloning from Observation	Faraz Torabi, Garrett Warnell, Peter Stone	In this work, we propose a two-phase, autonomous imitation learning technique called behavioral cloning from observation (BCO), that aims to provide improved performance with respect to both of these aspects.
685	3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations	Zhihua Wang, Stefano Rosa, Bo Yang, Sen Wang, Niki Trigoni, Andrew Markham	In this work we present a framework, 3D-PhysNet, which is able to predict how a three-dimensional solid will deform under an applied force using intuitive physics modelling.
686	Active Object Reconstruction Using a Guided View Planner	Xin Yang, Yuanbo Wang, Yaru Wang, Baocai Yin, Qiang Zhang, Xiaopeng Wei, Hongbo Fu	Inspired by the recent advance of image-based object reconstruction using deep learning, we present an active reconstruction model using a guided view planner.
687	Active Recurrence of Lighting Condition for Fine-Grained Change Detection	Qian Zhang, Wei Feng, Liang Wan, Fei-Peng Tian, Ping Tan	We propose to use the simple parallel lighting as an analogy model and based on Lambertian law to compose an instant navigation ball for this purpose.
688	Parameterised Queries and Lifted Query Answering	Tanya Braun, Ralf Möller	This paper presents parameterised queries as a means to avoid groundings, applying the lifting idea to queries.
689	Efficient Localized Inference for Large Graphical Models	Jinglin Chen, Jian Peng, Qiang Liu	We propose a new localized inference algorithm for answering marginalization queries in large graphical models with the correlation decay property.
690	The Promise and Perils of Myopia in Dynamic Pricing With Censored Information	Meenal Chhabra, Sanmay Das, Ilya Ryzhov	Under a linear demand model, we consider two information settings: partially censored, where agents who buy reveal their true valuations after the purchase is completed, and completely censored, where agents never reveal their valuations.
691	On Robust Trimming of Bayesian Network Classifiers	YooJung Choi, Guy Van den Broeck	To this end, we propose a closeness metric between Bayesian classifiers, called the expected classification agreement (ECA).
692	Metadata-dependent Infinite Poisson Factorization for Efficiently Modelling Sparse and Large Matrices in Recommendation	Trong Dinh Thac Do, Longbing Cao	In this work, Metadata-dependent Poisson Factorization (MPF) is invented to address the user/item sparsity by integrating user/item metadata into PF.
693	Redundancy-resistant Generative Hashing for Image Retrieval	Changying Du, Xingyu Xie, Changde Du, Hao Wang	Motivated by the fact that code redundancy usually is severer when more complex decoder network is used, in this paper, we propose a constrained deep generative architecture to simplify the decoder for data reconstruction.
694	A Graphical Criterion for Effect Identification in Equivalence Classes of Causal Diagrams	Amin Jaber, Jiji Zhang, Elias Bareinboim	In this work, we relax the requirement of having a fully specified causal structure and study the identifiability of effects with a singleton intervention (X), supposing that the structure is known only up to an equivalence class of causal diagrams, which is the output of standard structural learning algorithms (e.g., FCI).
695	Efficient Symbolic Integration for Probabilistic Inference	Samuel Kolb, Martin Mladenov, Scott Sanner, Vaishak Belle, Kristian Kersting	To address both limitations, we propose the use of extended algebraic decision diagrams (XADDs) as a compilation language for WMI.
696	Policy Optimization with Second-Order Advantage Information	Jiajin Li, Baoxiang Wang, Shengyu Zhang	We present the action subspace dependent gradient (ASDG) estimator which incorporates the Rao-Blackwell theorem (RB) and Control Variates (CV) into a unified framework to reduce the variance.
697	Building Sparse Deep Feedforward Networks using Tree Receptive Fields	Xiaopeng Li, Zhourong Chen, Nevin L. Zhang	In this paper, we consider the problem of learning sparse connectivity for feedforward neural networks (FNNs).
698	Patent Litigation Prediction: A Convolutional Tensor Factorization Approach	Qi Liu, Han Wu, Yuyang Ye, Hongke Zhao, Chuanren Liu, Dongfang Du	In this paper, we propose a data-driven framework, Convolutional Tensor Factorization (CTF), to identify the patents that may cause litigations between two companies.
699	Unsupervised Learning based Jump-Diffusion Process for Object Tracking in Video Surveillance	Xiaobai Liu, Donovan Lo, Chau Thuan	This paper presents a principled way for dealing with occlusions in visual tracking which is a long-standing issue in computer vision but largely remains unsolved.
700	Lifted Filtering via Exchangeable Decomposition	Stefan Lüdtke, Max Schröder, Sebastian Bader, Kristian Kersting, Thomas Kirste	We present a model for exact recursive Bayesian filtering based on lifted multiset states.
701	Stochastic Anytime Search for Bounding Marginal MAP	Radu Marinescu, Rina Dechter, Alexander Ihler	In this paper, we develop new search-based bounding schemes for Marginal MAP that produce anytime upper and lower bounds without performing exact likelihood computations.
702	Estimation with Incomplete Data: The Linear Case	Karthika Mohan, Felix Thoemmes, Judea Pearl	In this work, we devise model-based methods to consistently estimate mean, variance and covariance given data that are Missing Not At Random (MNAR).
703	Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty	Vu-Linh Nguyen, Sébastien Destercke, Marie-Hélène Masson, Eyke Hüllermeier	We propose a method for reliable prediction in multi-class classification, where reliability refers to the possibility of partial abstention in cases of uncertainty.
704	Algorithms for the Nearest Assignment Problem	Sara Rouhani, Tahrima Rahman, Vibhav Gogate	We consider the following nearest assignment problem (NAP): given a Bayesian network B and probability value q, find a configuration w of variables in B such that difference between q and the probability of w is minimized.
705	A Symbolic Approach to Explaining Bayesian Network Classifiers	Andy Shih, Arthur Choi, Adnan Darwiche	We propose an approach for explaining Bayesian network classifiers, which is based on compiling such classifiers into decision functions that have a tractable and symbolic form.
706	Scalable Probabilistic Causal Structure Discovery	Dhanya Sridhar, Jay Pujara, Lise Getoor	In this paper, we identify three key requirements for inferring the structure of causal networks for scientific discovery: (1) robustness to noise in observed measurements; (2) scalability to handle hundreds of variables; and (3) flexibility to encode domain knowledge and other structural constraints.
707	A Scalable Scheme for Counting Linear Extensions	Topi Talvitie, Kustaa Kangas, Teppo Niinimäki, Mikko Koivisto	Here, we present a novel scheme, relaxation Tootsie Pop, which in our experiments exhibits polynomial characteristics and significantly outperforms previous schemes.
708	Mixed Causal Structure Discovery with Application to Prescriptive Pricing	Wei Wenjuan, Feng Lu, Liu Chunchen	Prescriptive pricing is one of the most advanced pricing techniques, which derives the optimal price strategy to maximize the future profit/revenue by carrying out a two-stage process, demand modeling and price optimization.Demand modeling tries to reveal price-demand laws by discovering causal relationships among demands, prices, and objective factors, which is the foundation of price optimization.Existing methods either use regression or causal learning for uncovering the price-demand relations, but suffer from pain points in either accuracy/efficiency or mixed data type processing, while all of these are actual requirements in practical pricing scenarios.This paper proposes a novel demand modeling technique for practical usage.Speaking concretely, we propose a new locally consistent information criterion named MIC,and derive MIC-based inference algorithms for an accurate recovery of causal structure on mixed factor space.Experiments on simulate/real datasets show the superiority of our new approach in both price-demand law recovery and demand forecasting, as well as show promising performance in supporting optimal pricing.
709	A Savage-style Utility Theory for Belief Functions	Chunlai Zhou, Biao Qin, Xiaoyong Du	In this paper, we provide an axiomatic justification for decision making with belief functions by studying the belief-function counterpart of Savage’s Theorem where the state space is finite and the consequence set is a continuum [l, M] (l