Paper Digest: IJCAI 2021 Highlights
Download IJCAI-2021-Paper-Digests.pdf– highlights of all IJCAI-2021 papers. Readers can also choose to read this highlight article on our console, which allows users to filter out papers using keywords, authors, etc. International Joint Conference on Artificial Intelligence (IJCAI) is one of the top artificial intelligence conferences in the world. In 2021, it is to be held online.
To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights / summaries to quickly get the main idea of each paper. For users searching for papers/patents/grants with highlights, related papers, patents, grants, experts and organizations, please try our search console. We also provide an exclusive professor search service to find more than 400K professors across the US using their research work.
If you do not want to miss any interesting academic paper, you are welcome to sign up our free daily paper digest service to get updates on new papers published in your area every day. You are also welcome to follow us on Twitter and Linkedin to get updated with new conference digests.
Paper Digest Team
team@paperdigest.org
TABLE 1: Paper Digest: IJCAI 2021 Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | Distance Polymatrix Coordination Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose the new class of distance polymatrix coordination games, properly generalizing polymatrix coordination games, in which the overall utility of player x further depends on the payoffs arising by mutual actions of players v,z that are the endpoints of edges at any distance h |
Alessandro Aloisio; Michele Flammini; Bojana Kodric; Cosimo Vinci; |
| 2 | Diversity in Kemeny Rank Aggregation: A Parameterized Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we investigate the impact of this combination in the field of Kemeny Rank Aggregation, a well-studied class of problems lying in the intersection of order theory and social choice theory and also in the field of order theory itself. |
Emmanuel Arrighi; Henning Fernau; Daniel Lokshtanov; Mateus de Oliveira Oliveira; Petra Wolf; |
| 3 | School Choice with Flexible Diversity Goals and Specialized Seats Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a new and rich model of school choice with flexible diversity goals and specialized seats. |
Haris Aziz; Zhaohong Sun; |
| 4 | PROPm Allocations of Indivisible Goods to Multiple Agents Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the classic problem of fairly allocating a set of indivisible goods among a group of agents, and focus on the notion of approximate proportionality known as PROPm. |
Artem Baklanov; Pranav Garimidi; Vasilis Gkatzelis; Daniel Schoepflin; |
| 5 | Learning Within An Instance for Designing High-Revenue Combinatorial Auctions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We develop a new framework for designing truthful, high-revenue (combinatorial) auctions for limited supply. |
Maria-Florina Balcan; Siddharth Prasad; Tuomas Sandholm; |
| 6 | Combining Fairness and Optimality When Selecting and Allocating Projects Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We consider the problem of the conjoint selection and allocation of projects to a population of agents, e.g. students are assigned papers and shall present them to their peers. |
Khaled Belahcène; Vincent Mousseau; Anaëlle Wilczynski; |
| 7 | Two Influence Maximization Games on Graphs Made Temporal Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the dynamic nature of real-world networks, we generalize competitive diffusion games and Voronoi games from static to temporal graphs, where edges may appear or disappear over time. |
Niclas Boehmer; Vincent Froese; Julia Henkel; Yvonne Lasars; Rolf Niedermeier; Malte Renken; |
| 8 | Winner Robustness Via Swap- and Shift-Bribery: Parameterized Counting Complexity and Experiments Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the parameterized complexity of counting variants of Swap- and Shift-Bribery, focusing on the parameterizations by the number of swaps and the number of voters. |
Niclas Boehmer; Robert Bredereck; Piotr Faliszewski; Rolf Niedermeier; |
| 9 | Putting A Compass on The Map of Elections Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We provide such an interpretation by introducing four canonical “extreme” elections, acting as a compass on the map. |
Niclas Boehmer; Robert Bredereck; Piotr Faliszewski; Rolf Niedermeier; Stanisław Szufa; |
| 10 | Loyalty in Cardinal Hedonic Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on the assumption that agents are benevolent towards other agents they like to form coalitions with, we propose loyalty in hedonic games, a binary relation dependent on agents’ utilities. |
Martin Bullinger; Stefan Kober; |
| 11 | Approximating The Shapley Value Using Stratified Empirical Bernstein Sampling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For this task, we provide two contributions to the state of the art. First, we derive a novel concentration inequality that is tailored to stratified Shapley value estimation using sample variance information. Second, by sequentially choosing samples to minimize our inequality, we develop a new and more efficient method of sampling to estimate the Shapley value. |
Mark A. Burgess; Archie C. Chapman; |
| 12 | Picking Sequences and Monotonicity in Weighted Fair Division Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the problem of fairly allocating indivisible items to agents with different entitlements, which captures, for example, the distribution of ministries among political parties in a coalition government. |
Mithun Chakraborty; Ulrike Schmidt-Kraepelin; Warut Suksompong; |
| 13 | Fractional Matchings Under Preferences: Stability and Optimality Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study generalizations of stable matching in which agents may be matched fractionally; this models time-sharing assignments. |
Jiehua Chen; Sanjukta Roy; Manuel Sorge; |
| 14 | Temporal Induced Self-Play for Stochastic Bayesian Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Temporal-Induced Self-Play (TISP), a novel reinforcement learning-based framework to find strategies with decent performances from any decision point onward. |
Weizhe Chen; Zihan Zhou; Yi Wu; Fei Fang; |
| 15 | Cooperation in Threshold Public Projects with Binary Actions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: When can cooperation arise from self-interested decisions in public goods games? And how can we help agents to act cooperatively? We examine these classical questions in a pivotal participation game, a variant of public good games, where heterogeneous agents make binary participation decisions on contributing their endowments, and the public project succeeds when it has enough contributions. |
Yiling Chen; Biaoshuai Tao; Fang-Yi Yu; |
| 16 | Learning in Markets: Greed Leads to Chaos But Following The Price Is Right Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study learning dynamics in distributed production economies such as blockchain mining, peer-to-peer file sharing and crowdsourcing. |
Yun Kuen Cheung; Stefanos Leonardos; Georgios Piliouras; |
| 17 | Identifying Norms from Observation Using MCMC Sampling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper investigates the problem of identifying norm candidates from a normative language expressed as a probabilistic context-free grammar, using Markov Chain Monte Carlo (MCMC) search. |
Stephen Cranefield; Ashish Dhiman; |
| 18 | Improving Multi-agent Coordination By Learning to Estimate Contention Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a multi-agent learning algorithm, ALMA-Learning, for efficient and fair allocations in large-scale systems. |
Panayiotis Danassis; Florian Wiedemair; Boi Faltings; |
| 19 | Multi-Agent Intention Progression with Black-Box Agents Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new approach to intention progression in multi-agent settings where other agents are effectively black boxes. |
Michael Dann; Yuan Yao; Brian Logan; John Thangarajah; |
| 20 | The Parameterized Complexity of Connected Fair Division Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the Connected Fair Division problem (CFD), which generalizes the fundamental problem of fairly allocating resources to agents by requiring that the items allocated to each agent form a connected subgraph in a provided item graph G. |
Argyrios Deligkas; Eduard Eiben; Robert Ganian; Thekla Hamm; Sebastian Ordyniak; |
| 21 | Neural Regret-Matching for Distributed Constraint Optimization Problems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper tackles the limitation by incorporating deep neural networks in solving DCOPs for the first time and presents a neural-based sampling scheme built upon regret-matching. |
Yanchen Deng; Runsheng Yu; Xinrun Wang; Bo An; |
| 22 | Online Selection of Diverse Committees Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study three methods, theoretically and experimentally: a greedy algorithm that includes volunteers as long as proportionality is not violated; a non-adaptive method that includes a volunteer with a probability depending only on their features, assuming that the joint feature distribution in the volunteer pool is known; and a reinforcement learning based approach when this distribution is not known a priori but learnt online. |
Virginie Do; Jamal Atif; Jérôme Lang; Nicolas Usunier; |
| 23 | Graphical Cake Cutting Via Maximin Share Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the recently introduced cake-cutting setting in which the cake is represented by an undirected graph. |
Edith Elkind; Erel Segal-Halevi; Warut Suksompong; |
| 24 | Keep Your Distance: Land Division With Separation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper is part of an ongoing endeavor to bring the theory of fair division closer to practice by handling requirements from real-life applications. We focus on two requirements originating from the division of land estates: (1) each agent should receive a plot of a usable geometric shape, and (2) plots of different agents must be physically separated. |
Edith Elkind; Erel Segal-Halevi; Warut Suksompong; |
| 25 | On A Competitive Secretary Problem with Deferred Selections Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a novel multi-agent secretary model, in which the competition is explicit. |
Tomer Ezra; Michal Feldman; Ron Kupfer; |
| 26 | Relaxed Core Stability in Fractional Hedonic Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For this reason, we consider relaxed core stable outcomes where the notion of permissible deviations is modified along two orthogonal directions: the former takes into account the size of the deviating coalition, and the latter the amount of utility gain for each member of the deviating coalition. |
Angelo Fanelli; Gianpiero Monaco; Luca Moscardelli; |
| 27 | Reasoning Over Argument-Incomplete AAFs in The Presence of Correlations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce "argument-incomplete Abstract Argumentation Frameworks with dependencies", that extend the traditional abstract argumentation reasoning to the case where some arguments are uncertain and correlated through logical dependencies (such as mutual exclusion, implication, etc.). |
Bettina Fazzinga; Sergio Flesca; Filippo Furfaro; |
| 28 | Kemeny Consensus Complexity Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the complexity of consensus-related questions, with a particular focus on Kemeny and its qualitative version Slater. |
Zack Fitzsimmons; Edith Hemaspaandra; |
| 29 | Two-Sided Matching Meets Fair Division Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a new model for two-sided matching which allows us to borrow popular fairness notions from the fair division literature such as envy-freeness up to one good and maximin share guarantee. |
Rupert Freeman; Evi Micha; Nisarg Shah; |
| 30 | Worst-case Bounds on Power Vs. Proportion in Weighted Voting Games with Application to False-name Manipulation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We take a novel approach to the study of the power of big vs.~small players in these games. |
Yotam Gafni; Ron Lavi; Moshe Tennenholtz; |
| 31 | Even More Effort Towards Improved Bounds and Fixed-Parameter Tractability for Multiwinner Rules Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we consider several utilitarian and egailitarian OWA (ordered weighted average) scoring rules, which are an extensively researched family of rules (and a subfamily of the family of committee scoring rules). |
Sushmita Gupta; Pallavi Jain; Saket Saurabh; Nimrod Talmon; |
| 32 | Fair and Efficient Resource Allocation with Partial Information Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the fundamental problem of allocating indivisible goods to agents with additive preferences. |
Daniel Halpern; Nisarg Shah; |
| 33 | Accomplice Manipulation of The Deferred Acceptance Algorithm Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study a novel model of strategic behavior under the deferred acceptance algorithm: manipulation through an accomplice. |
Hadi Hosseini; Fatima Umar; Rohit Vaish; |
| 34 | Guaranteeing Maximin Shares: Some Agents Left Behind Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: While MMS allocations do not always exist, several approximation techniques have been developed to ensure that all agents receive a fraction of their maximin share. We focus on an alternative approximation notion, based on the population of agents, that seeks to guarantee MMS for a fraction of agents. |
Hadi Hosseini; Andrew Searns; |
| 35 | Surprisingly Popular Voting Recovers Rankings, Surprisingly! Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We explore practical techniques for extending the surprisingly popular algorithm to ranked voting by partial votes and predictions and designing robust aggregation rules. |
Hadi Hosseini; Debmalya Mandal; Nisarg Shah; Kevin Shi; |
| 36 | SURPRISE! and When to Schedule It Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To quantify the relationship between information flow and audiences’ perceived quality, we conduct a case study where subjects watch one of the world’s biggest esports events, LOL S10. |
Zhihuan Huang; Shengwei Xu; You Shan; Yuxuan Lu; Yuqing Kong; Tracy Xiao Liu; Grant Schoenebeck; |
| 37 | Dynamic Proportional Rankings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose generalizations of well-known aggregation rules to this setting and study their monotonicity and proportionality properties. |
Jonas Israel; Markus Brill; |
| 38 | A Polynomial-time, Truthful, Individually Rational and Budget Balanced Ridesharing Mechanism Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this, we formulate a general ridesharing problem and apply mechanism design to develop a novel mechanism which satisfies all four properties and whose social cost is within 8.6% of the optimal on average. |
Tatsuya Iwase; Sebastian Stein; Enrico H. Gerding; |
| 39 | Participatory Budgeting with Project Groups Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We show that the problem is generally intractable and describe efficient exact algorithms for several special cases, including instances with only few groups and instances where the group structure is close to being hierarchical, as well as efficient approximation algorithms. |
Pallavi Jain; Krzysztof Sornat; Nimrod Talmon; Meirav Zehavi; |
| 40 | Interaction Considerations in Learning from Humans Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our findings show that Evaluating interactions are more cognitively loading and less usable than the others, and Categorizing and Showing interactions are the least cognitively loading and most usable. |
Pallavi Koppol; Henny Admoni; Reid Simmons; |
| 41 | Two-Stage Facility Location Games with Strategic Clients and Facilities Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We consider non-cooperative facility location games where both facilities and clients act strategically and heavily influence each other. |
Simon Krogmann; Pascal Lenzner; Louise Molitor; Alexander Skopalik; |
| 42 | Fairness in Long-Term Participatory Budgeting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a theory of fairness for this setting, focusing on three main concepts that apply to types (groups) of voters: (i) achieving equal welfare for all types, (ii) minimizing inequality of welfare (as measured by the Gini coefficient), and (iii) achieving equal welfare in the long run. |
Martin Lackner; Jan Maly; Simon Rey; |
| 43 | Strategyproof Randomized Social Choice for Restricted Sets of Utility Functions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For finding more insights into the trade-off between strategyproofness and decisiveness, we propose the notion of U-strategyproofness which requires that only voters with a utility function in the set U cannot manipulate. |
Patrick Lederer; |
| 44 | Budget-feasible Mechanisms for Representing Groups of Agents Proportionally Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we consider the problem of designing budget-feasible mechanisms for selecting agents with private costs from various groups to ensure proportional representation, where the minimum proportion of the selected agents from each group is maximized. |
Xiang Liu; Hau Chan; Minming Li; Weiwei Wu; |
| 45 | Improving Welfare in One-Sided Matchings Using Simple Threshold Queries Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study one-sided matching problems where each agent must be assigned at most one object. |
Thomas Ma; Vijay Menon; Kate Larson; |
| 46 | Generalized Kings and Single-Elimination Winners in Random Tournaments Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we provide an almost complete characterization of the probability threshold such that all, a large number, or a small number of alternatives are k-kings with high probability in two random models. |
Pasin Manurangsi; Warut Suksompong; |
| 47 | Almost Envy-Freeness for Groups: Improved Bounds Via Discrepancy Theory Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the allocation of indivisible goods among groups of agents using well-known fairness notions such as envy-freeness and proportionality. |
Pasin Manurangsi; Warut Suksompong; |
| 48 | Winner Determination and Strategic Control in Conditional Approval Voting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our work focuses on a generalization of the classic Minisum approval voting rule, introduced by Barrot and Lang (2016), and referred to as Conditional Minisum (CMS), for multi-issue elections. |
Evangelos Markakis; Georgios Papasotiropoulos; |
| 49 | Majority Vote in Social Networks: Make Random Friends or Be Stubborn to Overpower Elites Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose two countermeasures that can be adopted by individual nodes relatively easily and guarantee that the elites will not have this disproportionate power to engineer the dominant output color. |
Charlotte Out; Ahad N. Zehmakan; |
| 50 | Mean Field Games Flock! The Reinforcement Learning Way Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a method enabling a large number of agents to learn how to flock. |
Sarah Perrin; Mathieu Laurière; Julien Pérolat; Matthieu Geist; Romuald Élie; Olivier Pietquin; |
| 51 | Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We investigate two methods to reduce forms of inequality in ride-pooling platforms: by incorporating fairness constraints into the objective function and redistributing income to drivers who deserve more. |
Naveen Raman; Sanket Shah; John Dickerson; |
| 52 | Shortlisting Rules and Incentives in An End-to-End Model for Participatory Budgeting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce an end-to-end model for participatory budgeting grounded in social choice theory. |
Simon Rey; Ulle Endriss; Ronald de Haan; |
| 53 | Matchings with Group Fairness Constraints: Online and Offline Algorithms Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We consider the problem of assigning items to platforms in the presence of group fairness constraints. |
Govind S. Sankar; Anand Louis; Meghana Nasre; Prajakta Nimbhorkar; |
| 54 | Stochastic Market Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated from real world societies, in this work we propose to utilize market forces to provide incentives for agents to become cooperative. |
Kyrill Schmid; Lenz Belzner; Robert Müller; Johannes Tochtermann; Claudia Linnhoff-Popien; |
| 55 | Tango: Declarative Semantics for Multiagent Communication Protocols Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper formulates a protocol semantics declaratively via inference rules that determine when a message emission or reception becomes enabled during an enactment, and its effect on the local state of an agent. |
Munindar P. Singh; Samuel H. Christie V.; |
| 56 | Vitality Indices Are Equivalent to Induced Game-Theoretic Centralities Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we show that vitality indices can be characterized using the axiom of Balanced Contributions proposed by Myerson in the coalitional game theory literature. |
Oskar Skibski; |
| 57 | Game-theoretic Analysis of Effort Allocation of Contributors to Public Projects Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a game-theoretic effort allocation model of contributors to public projects for modeling effort allocation of strategic contributors. |
Jared Soundy; Chenhao Wang; Clay Stevens; Hau Chan; |
| 58 | New Algorithms for Japanese Residency Matching Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose two suitable algorithms to reduce waste with minimal modification to the current system and show that they are superior to the algorithm currently deployed in JRMP by comparing them theoretically and empirically. |
Zhaohong Sun; Taiki Todo; Makoto Yokoo; |
| 59 | Fair Pairwise Exchange Among Groups Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the pairwise organ exchange problem among groups motivated by real-world applications and consider two types of group formulations. |
Zhaohong Sun; Taiki Todo; Toby Walsh; |
| 60 | Reducing Bus Bunching with Asynchronous Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we formulate route-level bus fleet control as an asynchronous multi-agent reinforcement learning (ASMR) problem and extend the classical actor-critic architecture to handle the asynchronous issue. |
Jiawei Wang; Lijun Sun; |
| 61 | Emergent Prosociality in Multi-Agent Games Through Gifting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose using a less restrictive peer-rewarding mechanism, gifting, that guides the agents toward more socially desirable equilibria while allowing agents to remain selfish and decentralized. |
Woodrow Z. Wang; Mark Beliaev; Erdem Bıyık; Daniel A. Lazar; Ramtin Pedarsani; Dorsa Sadigh; |
| 62 | An Axiom System for Feedback Centralities Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an axiom system for four classic feedback centralities: Eigenvector centrality, Katz centrality, Katz prestige and PageRank. |
Tomasz Wąs; Oskar Skibski; |
| 63 | Manipulation of K-Coalitional Games on Social Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we study the susceptibility for manipulation of these objectives, given the abilities and information that the manipulator has. |
Naftali Waxman; Sarit Kraus; Noam Hazon; |
| 64 | State-Aware Value Function Approximation with Attention Mechanism for Restless Multi-armed Bandits Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose considering both factors using the attention mechanism, which has achieved great success in deep learning. |
Shuang Wu; Jingyu Zhao; Guangjian Tian; Jun Wang; |
| 65 | Budget-feasible Maximum Nash Social Welfare Is Almost Envy-free Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we are interested in the fairness of the NSW in a budget-feasible allocation problem, in which each item has a cost that will be incurred to the agent it is allocated to, and each agent has a budget constraint on the total cost of items she receives. |
Xiaowei Wu; Bo Li; Jiarui Gan; |
| 66 | Learning with Generated Teammates to Achieve Type-Free Ad-Hoc Teamwork Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we propose the model of Entropy-regularized Deep Recurrent Q-Network (EDRQN) to generate teammates automatically, meanwhile utilize them to pre-train our agent. |
Dong Xing; Qianhui Liu; Qian Zheng; Gang Pan; |
| 67 | H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture for Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel framework called hierarchical federated learning (H-FL) to tackle this challenge. |
He Yang; |
| 68 | Dominant Resource Fairness with Meta-Types Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new mechanism called Dominant Resource Fairness with Meta Types which determines the allocations by solving a small number of linear programs. |
Steven Yin; Shatian Wang; Lingyi Zhang; Christian Kroer; |
| 69 | Altruism Design in Networked Public Goods Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a novel extension of public goods games to account for altruistic motivations by adding a term in the utility function that incorporates the perceived benefits an agent obtains from the welfare of others, mediated by an altruism graph. |
Sixie Yu; David Kempe; Yevgeniy Vorobeychik; |
| 70 | MFVFD: A Multi-Agent Q-Learning Approach to Cooperative and Non-Cooperative Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: With the individual value function decomposition, we propose MFVFD, a novel multi-agent Q-learning approach for solving cooperative and non-cooperative tasks based on mean-field theory. |
Tianhao Zhang; Qiwei Ye; Jiang Bian; Guangming Xie; Tie-Yan Liu; |
| 71 | Data-Efficient Reinforcement Learning for Malaria Control Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: With the GP world model, we propose a variance-bonus reward to measure the uncertainty about the world. |
Lixin Zou; |
| 72 | Interacting with Explanations Through Critiquing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel technique using aspect markers that learns to generate personalized explanations of recommendations from review texts, and we show that human users significantly prefer these explanations over those produced by state-of-the-art techniques. |
Diego Antognini; Claudiu Musat; Boi Faltings; |
| 73 | On Smoother Attributions Using Neural Stochastic Differential Equations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper uses the recently identified connection between dynamical systems and residual neural networks to show that the attributions computed over neural stochastic differential equations (SDEs) are less noisy, visually sharper, and quantitatively more robust. |
Sumit Jha; Rickard Ewetz; Alvaro Velasquez; Susmit Jha; |
| 74 | Location Predicts You: Location Prediction Via Bi-direction Speculation and Dual-level Association Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the issues above, we propose a novel Bi-direction Speculation and Dual-level Association method (BSDA), which considers both users’ interests in POIs and POIs’ appeal to users. |
Xixi Li; Ruimin Hu; Zheng Wang; Toshihiko Yamasaki; |
| 75 | Addressing The Long-term Impact of ML Decisions Via Policy Regret Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We capture these considerations through the notion of policy regret, a much stronger notion than the often-studied external regret, and present an algorithm with provably sub-linear policy regret for sufficiently long time horizons. |
David Lindner; Hoda Heidari; Andreas Krause; |
| 76 | Multi-Objective Reinforcement Learning for Designing Ethical Environments Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we make headway along this direction by proposing a novel way of designing environments wherein it is formally guaranteed that an agent learns to behave ethically while pursuing its individual objectives. |
Manel Rodriguez-Soto; Maite Lopez-Sanchez; Juan A. Rodriguez Aguilar; |
| 77 | Bias Silhouette Analysis: Towards Assessing The Quality of Bias Metrics for Word Embedding Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study how to assess the quality of bias metrics for word embedding models. |
Maximilian Spliethöver; Henning Wachsmuth; |
| 78 | Decision Making with Differential Privacy Under A Fairness Lens Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The paper analyzes the reasons for these disproportionate impacts and proposes guidelines to mitigate these effects. |
Cuong Tran; Ferdinando Fioretto; Pascal Van Hentenryck; Zhiyan Yao; |
| 79 | An Examination of Fairness of AI Models for Deepfake Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we evaluate bias present in deepfake datasets and detection models across protected subgroups. |
Loc Trinh; Yan Liu; |
| 80 | Characteristic Examples: High-Robustness, Low-Transferability Fingerprinting of Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes Characteristic Examples for effectively fingerprinting deep neural networks, featuring high-robustness to the base model against model pruning as well as low-transferability to unassociated models. |
Siyue Wang; Xiao Wang; Pin-Yu Chen; Pu Zhao; Xue Lin; |
| 81 | GASP: Gated Attention for Saliency Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Social cues greatly influence our attention, consequently altering our eye movements and behavior. To emphasize the efficacy of such features, we present a neural model for integrating social cues and weighting their influences. |
Fares Abawi; Tom Weber; Stefan Wermter; |
| 82 | Explaining Self-Supervised Image Representations with Visual Probing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Hence, we propose a systematic approach to obtain analogs of natural language in vision, such as visual words, context, and taxonomy. |
Dominika Basaj; Witold Oleszkiewicz; Igor Sieradzki; Michał Górszczak; Barbara Rychalska; Tomasz Trzcinski; Bartosz Zieliński; |
| 83 | Themis: A Fair Evaluation Platform for Computer Vision Competitions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, we propose Themis, a framework that trains a noise generator jointly with organizers and participants to prevent intentional fine-tuning by protecting test datasets from surreptitious manual labeling. |
Zinuo Cai; Jianyong Yuan; Yang Hua; Tao Song; Hao Wang; Zhengui Xue; Ningxin Hu; Jonathan Ding; Ruhui Ma; Mohammad Reza Haghighat; Haibing Guan; |
| 84 | Novelty Detection Via Contrastive Learning with Negative Data Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Previous generative adversarial networks based methods and self-supervised approaches suffer from instability training, mode dropping, and low discriminative ability. We overcome such problems by introducing a novel decoder-encoder framework. |
Chengwei Chen; Yuan Xie; Shaohui Lin; Ruizhi Qiao; Jian Zhou; Xin Tan; Yi Zhang; Lizhuang Ma; |
| 85 | Zero-Shot Chinese Character Recognition with Stroke-Level Decomposition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by the fact that humans can generalize to know how to write characters unseen before if they have learned stroke orders of some characters, we propose a stroke-based method by decomposing each character into a sequence of strokes, which are the most basic units of Chinese characters. |
Jingye Chen; Bin Li; Xiangyang Xue; |
| 86 | Leveraging Human Attention in Novel Object Captioning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: While previous novel object captioning methods rely on external image taggers or object detectors to describe novel objects, we present the Attention-based Novel Object Captioner (ANOC) that complements novel object captioners with human attention features that characterize generally important information independent of tasks. |
Xianyu Chen; Ming Jiang; Qi Zhao; |
| 87 | Boundary Knowledge Translation Based Reference Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a novel Reference semantic segmentation Network (Ref-Net) to conduct visual boundary knowledge translation. |
Lechao Cheng; Zunlei Feng; Xinchao Wang; Ya Jie Liu; Jie Lei; Mingli Song; |
| 88 | Hierarchical Object-oriented Spatio-Temporal Reasoning for Video Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Toward reaching this goal we propose an object-oriented reasoning approach in that video is abstracted as a dynamic stream of interacting objects. |
Long Hoang Dang; Thao Minh Le; Vuong Le; Truyen Tran; |
| 89 | Phonovisual Biases in Language: Is The Lexicon Tied to The Visual World? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The present paper addresses the study of cross-linguistic and cross-modal iconicity within a deep learning framework. |
Andrea Gregor de Varda; Carlo Strapparava; |
| 90 | Direction-aware Feature-level Frequency Decomposition for Single Image Deraining Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel direction-aware feature-level frequency decomposition network for single image deraining. |
Sen Deng; Yidan Feng; Mingqiang Wei; Haoran Xie; Yiping Chen; Jonathan Li; Xiao-Ping Zhang; Jing Qin; |
| 91 | TCIC: Theme Concepts Learning Cross Language and Vision for Image Captioning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Theme Concepts extended Image Captioning (TCIC) framework that incorporates theme concepts to represent high-level cross-modality semantics. |
Zhihao Fan; Zhongyu Wei; Siyuan Wang; Ruize Wang; Zejun Li; Haijun Shan; Xuanjing Huang; |
| 92 | Chop Chop BERT: Visual Question Answering By Chopping VisualBERT’s Heads Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To investigate why and how these models work on VQA so well, in this paper we explore the roles of individual heads and layers in Transformer models when handling 12 different types of questions. |
Chenyu Gao; Qi Zhu; Peng Wang; Qi Wu; |
| 93 | Feature Space Targeted Attacks By Statistic Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose to measure this discrepancy using statistic alignment. |
Lianli Gao; Yaya Cheng; Qilong Zhang; Xing Xu; Jingkuan Song; |
| 94 | Multi-view Feature Augmentation with Adaptive Class Activation Mapping Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an end-to-end-trainable feature augmentation module built for image classification that extracts and exploits multi-view local features to boost model performance. |
Xiang Gao; Yingjie Tian; Zhiquan Qi; |
| 95 | Learning Spectral Dictionary for Local Representation of Mesh Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we learn spectral dictionary (i.e., bases) for the weighting matrices such that the parameter size is independent of the resolution of 3D shapes. |
Zhongpai Gao; Junchi Yan; Guangtao Zhai; Xiaokang Yang; |
| 96 | Self-Supervised Video Action Localization with Adversarial Temporal Transforms Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our motivating insight is that the temporal boundary of action should be stably predicted under various temporal transforms. |
Guoqiang Gong; Liangfeng Zheng; Wenhao Jiang; Yadong Mu; |
| 97 | EventDrop: Data Augmentation for Event-based Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce EventDrop, a new method for augmenting asynchronous event data to improve the generalization of deep models. |
Fuqiang Gu; Weicong Sng; Xuke Hu; Fangwen Yu; |
| 98 | AdaVQA: Overcoming Language Priors with Adapted Margin Cosine Loss Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by this, in this work, we attempt to tackle the language prior problem from the viewpoint of the feature space learning. |
Yangyang Guo; Liqiang Nie; Zhiyong Cheng; Feng Ji; Ji Zhang; Alberto Del Bimbo; |
| 99 | Disentangled Face Attribute Editing Via Instance-Aware Latent Space Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel framework (IALS) that performs Instance-Aware Latent-Space Search to find semantic directions for disentangled attribute editing. |
Yuxuan Han; Jiaolong Yang; Ying Fu; |
| 100 | DeepME: Deep Mixture Experts for Large-scale Image Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: According to our best knowledge, how to adaptively and effectively fuse multiple CNNs for large-scale image classification is still under-explored. On this basis, a deep mixture algorithm is developed to support large-scale image classification in this paper. |
Ming He; Guangyi Lv; Weidong He; Jianping Fan; Guihua Zeng; |
| 101 | Multi-Scale Selective Feedback Network with Dual Loss for Real Image Denoising Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: However, unpaired images without noise-free labels are ubiquitous in the real world. Therefore, we proposed a multi-scale selective feedback network (MSFN) with the dual loss. |
Xiaowan Hu; Yuanhao Cai; Zhihong Liu; Haoqian Wang; Yulun Zhang; |
| 102 | Dynamic Inconsistency-aware DeepFake Video Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Dynamic Inconsistency-aware Network to handle the inconsistent problem, which uses a Cross-Reference module (CRM) to capture both the global and local inter-frame inconsistencies. |
Ziheng Hu; Hongtao Xie; YuXin Wang; Jiahong Li; Zhongyuan Wang; Yongdong Zhang; |
| 103 | AgeFlow: Conditional Age Progression and Regression with Normalizing Flows Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome these issues, this paper proposes a novel framework, termed AgeFlow, to integrate the advantages of both flow-based models and GANs. |
Zhizhong Huang; Shouzhen Chen; Junping Zhang; Hongming Shan; |
| 104 | Self-Supervised Video Representation Learning with Constrained Spatiotemporal Jigsaw Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a novel pretext task for self-supervised video representation learning by exploiting spatiotemporal continuity in videos. |
Yuqi Huo; Mingyu Ding; Haoyu Lu; Ziyuan Huang; Mingqian Tang; Zhiwu Lu; Tao Xiang; |
| 105 | Perturb, Predict & Paraphrase: Semi-Supervised Learning Using Noisy Student for Image Captioning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we provide an in-depth analysis of the noisy student SSL framework for the task of image captioning and derive state-of-the-art results. |
Arjit Jain; Pranay Reddy Samala; Preethi Jyothi; Deepak Mittal; Maneesh Singh; |
| 106 | Step-Wise Hierarchical Alignment Network for Image-Text Matching Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Different from them, in this work, we propose a step-wise hierarchical alignment network (SHAN) that decomposes image-text matching into multi-step cross-modal reasoning process. |
Zhong Ji; Kexin Chen; Haoran Wang; |
| 107 | Planning with Learned Dynamic Model for Unsupervised Point Cloud Registration Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we cast point cloud registration into a planning problem in reinforcement learning, which can seek the transformation between the source and target point clouds through trial and error. |
Haobo Jiang; Jianjun Qian; Jin Xie; Jian Yang; |
| 108 | Information Bottleneck Approach to Spatial Attention Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an IB-inspired spatial attention module for DNN structures built for visual recognition. |
Qiuxia Lai; Yu Li; Ailing Zeng; Minhao Liu; Hanqiu Sun; Qiang Xu; |
| 109 | Noise Doesn’t Lie: Towards Universal Detection of Deep Inpainting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we make the first attempt towards universal detection of deep inpainting, where the detection network can generalize well when detecting different deep inpainting methods. |
Ang Li; Qiuhong Ke; Xingjun Ma; Haiqin Weng; Zhiyuan Zong; Feng Xue; Rui Zhang; |
| 110 | IMENet: Joint 3D Semantic Scene Completion and 2D Semantic Segmentation Through Iterative Mutual Enhancement Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We argue that this sequential scheme does not ensure these two tasks fully benefit each other, and present an Iterative Mutual Enhancement Network (IMENet) to solve them jointly, which interactively refines the two tasks at the late prediction stage. |
Jie Li; Laiyan Ding; Rui Huang; |
| 111 | Deep Automatic Natural Image Matting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the difficulties when extending them to natural images with salient transparent/meticulous foregrounds or non-salient foregrounds. |
Jizhizi Li; Jing Zhang; Dacheng Tao; |
| 112 | Medical Image Segmentation Using Squeeze-and-Expansion Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose Segtran, an alternative segmentation framework based on transformers, which have unlimited "effective receptive fields" even at high feature resolutions. |
Shaohua Li; Xiuchao Sui; Xiangde Luo; Xinxing Xu; Yong Liu; Rick Goh; |
| 113 | PIANO: A Parametric Hand Bone Model from Magnetic Resonance Imaging Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present PIANO, the first parametric bone model of human hands from MRI data. |
Yuwei Li; Minye Wu; Yuyao Zhang; Lan Xu; Jingyi Yu; |
| 114 | Instance-Aware Coherent Video Style Transfer for Chinese Ink Wash Painting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel video style transfer framework for Chinese ink wash paintings. |
Hao Liang; Shuai Yang; Wenjing Wang; Jiaying Liu; |
| 115 | Noise2Grad: Extract Image Noise to Denoise Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We consider such a case in which paired data and noise statistics are not accessible, but unpaired noisy and clean images are easy to collect. To form the necessary supervision, our strategy is to extract the noise from the noisy image to synthesize new data. |
Huangxing Lin; Yihong Zhuang; Yue Huang; Xinghao Ding; Xiaoqing Liu; Yizhou Yu; |
| 116 | Direct Measure Matching for Crowd Counting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new measure-based counting approach to regress the predicted density maps to the scattered point-annotated ground truth directly. |
Hui Lin; Xiaopeng Hong; Zhiheng Ma; Xing Wei; Yunfeng Qiu; Yaowei Wang; Yihong Gong; |
| 117 | A Multi-Constraint Similarity Learning with Adaptive Weighting for Visible-Thermal Person Re-Identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these issues, we propose a Multi-Constraint (MC) similarity learning method that jointly considers the cross-modality relationships from three different aspects, i.e., Instance-to-Instance (I2I), Center-to-Instance (C2I), and Center-to-Center (C2C). |
Yongguo Ling; Zhiming Luo; Yaojin Lin; Shaozi Li; |
| 118 | Learning 3-D Human Pose Estimation from Catadioptric Videos Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we explore a novel way of obtaining gigantic 3-D human pose data without manual annotations. |
Chenchen Liu; Yongzhi Li; Kangqi Ma; Duo Zhang; Peijun Bao; Yadong Mu; |
| 119 | Bipartite Matching for Crowd Counting with Point Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the problem, we propose a bipartite matching based method for crowd counting with only point supervision (BM-Count). |
Hao Liu; Qiang Zhao; Yike Ma; Feng Dai; |
| 120 | Dual Reweighting Domain Generalization for Face Presentation Attack Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To settle the issue, we propose a novel Dual Reweighting Domain Generalization (DRDG) framework which iteratively reweights the relative importance between samples to further improve the generalization. |
Shubao Liu; Ke-Yue Zhang; Taiping Yao; Kekai Sheng; Shouhong Ding; Ying Tai; Jilin Li; Yuan Xie; Lizhuang Ma; |
| 121 | Graph Consistency Based Mean-Teaching for Unsupervised Domain Adaptive Person Re-Identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To handle these issues, this paper proposes a Graph Consistency based Mean-Teaching (GCMT) method with constructing the Graph Consistency Constraint (GCC) between teacher and student networks. |
Xiaobin Liu; Shiliang Zhang; |
| 122 | Domain Generalization Under Conditional and Label Shifts Via Variational Bayesian Inference Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a domain generalization (DG) approach to learn on several labeled source domains and transfer knowledge to a target domain that is inaccessible in training. |
Xiaofeng Liu; Bo Hu; Linghao Jin; Xu Han; Fangxu Xing; Jinsong Ouyang; Jun Lu; Georges El Fakhri; Jonghye Woo; |
| 123 | Learn from Concepts: Towards The Purified Memory for Few-shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel purified memory mechanism that simulates the recognition process of human beings. |
Xuncheng Liu; Xudong Tian; Shaohui Lin; Yanyun Qu; Lizhuang Ma; Wang Yuan; Zhizhong Zhang; Yuan Xie; |
| 124 | One-Shot Affordance Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To empower robots with this ability in unseen scenarios, we consider the challenging one-shot affordance detection problem in this paper, i.e., given a support image that depicts the action purpose, all objects in a scene with the common affordance should be detected. |
Hongchen Luo; Wei Zhai; Jing Zhang; Yang Cao; Dacheng Tao; |
| 125 | CIMON: Towards High-quality Hash Codes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new method named Comprehensive sImilarity Mining and cOnsistency learNing (CIMON). |
Xiao Luo; Daqing Wu; Zeyu Ma; Chong Chen; Minghua Deng; Jinwen Ma; Zhongming Jin; Jianqiang Huang; Xian-Sheng Hua; |
| 126 | Point-based Acoustic Scattering for Interactive Sound Propagation Via Surface Encoding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel geometric deep learning method to compute the acoustic scattering properties of geometric objects. |
Hsien-Yu Meng; Zhenyu Tang; Dinesh Manocha; |
| 127 | Modality-aware Style Adaptation for RGB-Infrared Person Re-Identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a highly compact modality-aware style adaptation (MSA) framework, which aims to explore more potential relations between RGB and IR modalities by introducing new related modalities. |
Ziling Miao; Hong Liu; Wei Shi; Wanlu Xu; Hanrong Ye; |
| 128 | Look Wide and Interpret Twice: Improving Performance on Interactive Instruction-following Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a new method, which outperforms the previous methods by a large margin. |
Van-Quang Nguyen; Masanori Suganuma; Takayuki Okatani; |
| 129 | Attention-based Pyramid Dilated Lattice Network for Blind Image Denoising Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, to overcome this shortcoming we present a novel attention-based pyramid dilated lattice (APDL) architecture and investigate its capability for blind image denoising. |
Mohammad Nikzad; Yongsheng Gao; Jun Zhou; |
| 130 | Few-shot Neural Human Performance Rendering from Sparse RGBD Videos Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a two-branch neural blending to combine the neural point render and classical graphics texturing pipeline, which integrates reliable observations over sparse key-frames. |
Anqi Pang; Xin Chen; Haimin Luo; Minye Wu; Jingyi Yu; Lan Xu; |
| 131 | Self-boosting for Feature Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we propose a novel distillation method named Self-boosting Feature Distillation (SFD), which eases the Teacher-Student gap by feature integration and self-distillation of Student. |
Yulong Pei; Yanyun Qu; Junping Zhang; |
| 132 | SiamRCR: Reciprocal Classification and Regression for Visual Object Tracking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel siamese tracking algorithm called SiamRCR, addressing this problem with a simple, light and effective solution. |
Jinlong Peng; Zhengkai Jiang; Yueyang Gu; Yang Wu; Yabiao Wang; Ying Tai; Chengjie Wang; Weiyao Lin; |
| 133 | Unsupervised Hashing with Contrastive Information Bottleneck Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this problem, inspired by the recent success of contrastive learning in learning continuous representations, we propose to adapt this framework to learn binary hashing codes. |
Zexuan Qiu; Qinliang Su; Zijing Ou; Jianxing Yu; Changyou Chen; |
| 134 | Adaptive Edge Attention for Graph Matching with Outliers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these issues, we present an Edge Attention-adaptive Graph Matching (EAGM) network and a novel description of edge features. |
Jingwei Qu; Haibin Ling; Chenrui Zhang; Xiaoqing Lyu; Zhi Tang; |
| 135 | Multi-Level Graph Encoding with Structural-Collaborative Relation Learning for Skeleton-Based Person Re-Identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To fully explore body relations, we construct graphs to model human skeletons from different levels, and for the first time propose a Multi-level Graph encoding approach with Structural-Collaborative Relation learning (MG-SCR) to encode discriminative graph features for person Re-ID. |
Haocong Rao; Shihao Xu; Xiping Hu; Jun Cheng; Bin Hu; |
| 136 | Learning Visual Words for Weakly-Supervised Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, to tackle this problem, we proposed to simultaneously learn the image-level labels and local visual word labels. |
Lixiang Ru; Bo Du; Chen Wu; |
| 137 | Learning with Selective Forgetting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new framework for lifelong learning, called Learning with Selective Forgetting, which is to update a model for the new task with forgetting only the selected classes of the previous tasks while maintaining the rest. |
Takashi Shibata; Go Irie; Daiki Ikami; Yu Mitsuzumi; |
| 138 | Structure Guided Lane Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel structure guided framework to solve these problems simultaneously. |
Jinming Su; Chao Chen; Ke Zhang; Junfeng Luo; Xiaoming Wei; Xiaolin Wei; |
| 139 | Towards Unsupervised Deformable-Instances Image-to-Image Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an effective pipeline named Mask-Guided Deformable-instances GAN (MGD-GAN) which first generates target masks in batch and then utilizes them to synthesize corresponding instances on the background image, with all instances efficiently translated and background well preserved. |
Sitong Su; Jingkuan Song; Lianli Gao; Junchen Zhu; |
| 140 | Enhance Image As You Like with Unpaired Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In contrast, we propose a lightweight one-path conditional generative adversarial network (cGAN) to learn a one-to-many relation from low-light to normal-light image space, given only sets of low- and normal-light training images without any correspondence. |
Xiaopeng Sun; Muxingzi Li; Tianyu He; Lubin Fan; |
| 141 | Speech2Talking-Face: Inferring and Driving A Face with Synchronized Audio-Visual Representation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our key insight is to synchronize audio and visual representations from two perspectives in a style-based generative framework. |
Yasheng Sun; Hang Zhou; Ziwei Liu; Hideki Koike; |
| 142 | Context-aware Cross-level Fusion Network for Camouflaged Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Context-aware Cross level Fusion Network (C2F-Net) to address the challenging COD task. |
Yujia Sun; Geng Chen; Tao Zhou; Yi Zhang; Nian Liu; |
| 143 | Proposal-free One-stage Referring Expression Via Grid-Word Cross-Attention Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a proposal-free one-stage (PFOS) model that is able to regress the region-of-interest from the image, based on a textual query, in an end-to-end manner. |
Wei Suo; MengYang Sun; Peng Wang; Qi Wu; |
| 144 | MatchVIE: Exploiting Match Relevancy Between Entities for Visual Information Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, in this paper we propose a novel key-value matching model based on a graph neural network for VIE (MatchVIE). |
Guozhi Tang; Lele Xie; Lianwen Jin; Jiapeng Wang; Jingdong Chen; Zhen Xu; Qianying Wang; Yaqiang Wu; Hui Li; |
| 145 | AVA: Adversarial Vignetting Attack Against Visual Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Due to this natural advantage, in this work, we study the vignetting from a new viewpoint, i.e., adversarial vignetting attack (AVA), which aims to embed intentionally misleading information into the vignetting and produce a natural adversarial example without noise patterns. |
Binyu Tian; Felix Juefei-Xu; Qing Guo; Xiaofei Xie; Xiaohong Li; Yang Liu; |
| 146 | Towards Cross-View Consistency in Semantic Segmentation While Varying View Direction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on this fact, we propose to generate images which are the same as real images of the scene taken in certain novel view directions for training and evaluation. |
Xin Tong; Xianghua Ying; Yongjie Shi; He Zhao; Ruibin Wang; |
| 147 | Learning Interpretable Concept Groups in CNNs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel training methodology—Concept Group Learning (CGL)—that encourages training of interpretable CNN filters by partitioning filters in each layer into \emph{concept groups}, each of which is trained to learn a single visual concept. |
Saurabh Varshneya; Antoine Ledent; Robert A. Vandermeulen; Yunwen Lei; Matthias Enders; Damian Borth; Marius Kloft; |
| 148 | Text-based Person Search Via Multi-Granularity Embedding Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we propose a novel multi-granularity embedding learning model for text-based person search. |
Chengji Wang; Zhiming Luo; Yaojin Lin; Shaozi Li; |
| 149 | Cross-Domain Few-Shot Classification Via Adversarial Task Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we aim to improve the robustness of the inductive bias through task augmentation. |
Haoqing Wang; Zhi-Hong Deng; |
| 150 | Tag, Copy or Predict: A Unified Weakly-Supervised Learning Framework for Visual Information Extraction Using Sequences Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a unified weakly-supervised learning framework called TCPNet (Tag, Copy or Predict Network), which introduces 1) an efficient encoder to simultaneously model the semantic and layout information in 2D OCR results, 2) a weakly-supervised training method that utilizes only sequence-level supervision; and 3) a flexible and switchable decoder which contains two inference modes: one (Copy or Predict Mode) is to output key information sequences of different categories by copying a token from the input or predicting one in each time step, and the other (Tag Mode) is to directly tag the input sequence in a single forward pass. |
Jiapeng Wang; Tianwei Wang; Guozhi Tang; Lianwen Jin; Weihong Ma; Kai Ding; Yichao Huang; |
| 151 | Spline Positional Encoding for Learning 3D Implicit Signed Distance Fields Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel positional encoding scheme, called Spline Positional Encoding, to map the input coordinates to a high dimensional space before passing them to MLPs, which help recover 3D signed distance fields with fine-scale geometric details from unorganized 3D point clouds. |
Peng-Shuai Wang; Yang Liu; Yu-Qi Yang; Xin Tong; |
| 152 | Audio2Head: Audio-driven One-shot Talking-head Generation with Natural Head Motion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an audio-driven talking-head method to generate photo-realistic talking-head videos from a single reference image. |
Suzhen Wang; Lincheng Li; Yu Ding; Changjie Fan; Xin Yu; |
| 153 | Norm-guided Adaptive Visual Embedding for Zero-Shot Sketch-Based Image Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a novel Norm-guided Adaptive Visual Embedding (NAVE) model, for adaptively building the common space based on visual similarity instead of language-based pre-defined prototypes. |
Wenjie Wang; Yufeng Shi; Shiming Chen; Qinmu Peng; Feng Zheng; Xinge You; |
| 154 | Dig Into Multi-modal Cues for Video Retrieval with Hierarchical Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, in this paper, we leverage the hierarchical video-text alignment to fully explore the detailed diverse characteristics in multi-modal cues for fine-grained alignment with local semantics from phrases, as well as to capture a high-level semantic correspondence. |
Wenzhe Wang; Mengdan Zhang; Runnan Chen; Guanyu Cai; Penghao Zhou; Pai Peng; Xiaowei Guo; Jian Wu; Xing Sun; |
| 155 | Towards Compact Single Image Super-Resolution Via Contrastive Self-distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we proposed a novel contrastive self-distillation (CSD) framework to simultaneously compress and accelerate various off-the-shelf SR models. |
Yanbo Wang; Shaohui Lin; Yanyun Qu; Haiyan Wu; Zhizhong Zhang; Yuan Xie; Angela Yao; |
| 156 | Deep Unified Cross-Modality Hashing By Pairwise Data Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, to address the issues above, we propose a novel end-to-end Deep Unified Cross-Modality Hashing method named DUCMH, which is able to jointly learn unified hash codes and unified hash functions by alternate learning and data alignment. |
Yimu Wang; Bo Xue; Quan Cheng; Yuhui Chen; Lijun Zhang; |
| 157 | HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a high fidelity face swapping method, called HifiFace, which can well preserve the face shape of the source face and generate photo-realistic results. |
Yuhan Wang; Xu Chen; Junwei Zhu; Wenqing Chu; Ying Tai; Chengjie Wang; Jilin Li; Yongjian Wu; Feiyue Huang; Rongrong Ji; |
| 158 | Domain-Smoothing Network for Zero-Shot Sketch-Based Image Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, a cross-modal contrastive method is proposed to learn generalized representations to smooth the domain gap by mining relations with additional augmented samples. |
Zhipeng Wang; Hao Wang; Jiexi Yan; Aming Wu; Cheng Deng; |
| 159 | Local Representation Is Not Enough: Soft Point-Wise Transformer for Descriptor and Detector of Local Features Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these issues, we propose a novel Soft Point-Wise Transformer for Descriptor and Detector, simultaneously mining long-range intrinsic and cross-scale dependencies of local features. |
Zihao Wang; Xueyi Li; Zhen Li; |
| 160 | Weakly Supervised Dense Video Captioning Via Jointly Usage of Knowledge Distillation and Cross-modal Matching Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes an approach to Dense Video Captioning (DVC) without pairwise event-sentence annotation. |
Bofeng Wu; Guocheng Niu; Jun Yu; Xinyan Xiao; Jian Zhang; Hua Wu; |
| 161 | Tracklet Proposal Network for Multi-Object Tracking on Point Clouds Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes the first tracklet proposal network, named PC-TCNN, for Multi-Object Tracking (MOT) on point clouds. |
Hai Wu; Qing Li; Chenglu Wen; Xin Li; Xiaoliang Fan; Cheng Wang; |
| 162 | Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Video Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a novel task, referred to as Weakly-Supervised Spatio-Temporal Anomaly Detection (WSSTAD) in surveillance video. |
Jie Wu; Wei Zhang; Guanbin Li; Wenhao Wu; Xiao Tan; Yingying Li; Errui Ding; Liang Lin; |
| 163 | GM-MLIC: Graph Matching Based Multi-Label Image Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we treat each image as a bag of instances, and reformulate the task of MLIC as a instance-label matching selection problem. |
Yanan Wu; He Liu; Songhe Feng; Yi Jin; Gengyu Lyu; Zizhang Wu; |
| 164 | Micro-Expression Recognition Enhanced By Macro-Expression from Spatial-Temporal Domain Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Since micro-expression and macro-expression share some similarities in both spatial and temporal facial behavior patterns, we propose a macro-to-micro transformation framework for micro-expression recognition. |
Bin Xia; Shangfei Wang; |
| 165 | Segmenting Transparent Objects in The Wild with Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation dataset. |
Enze Xie; Wenjia Wang; Wenhai Wang; Peize Sun; Hang Xu; Ding Liang; Ping Luo; |
| 166 | Adversarial Feature Disentanglement for Long-Term Person Re-identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose an adversarial feature disentanglement network (AFD-Net) which contains intra-class reconstruction and inter-class adversary to disentangle the identity-related and identity-unrelated (clothing) features. Moreover, we collect a challenging Market-Clothes dataset and a real-world PKU-Market-Reid dataset for evaluation. |
Wanlu Xu; Hong Liu; Wei Shi; Ziling Miao; Zhisheng Lu; Feihu Chen; |
| 167 | Tool- and Domain-Agnostic Parameterization of Style Transfer Effects Leveraging Pretrained Perceptual Metrics Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To enable this, we introduce a framework that utilizes an existing pretrained model for style transfer to calculate a perceptual style distance to the reference sample and uses black-box optimization to find the parameters that minimize this distance. |
Hiromu Yakura; Yuki Koyama; Masataka Goto; |
| 168 | Hierarchical Self-supervised Augmented Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose to append several auxiliary classifiers to hierarchical intermediate feature maps to generate diverse self-supervised knowledge and perform the one-to-one transfer to teach the student network thoroughly. |
Chuanguang Yang; Zhulin An; Linhang Cai; Yongjun Xu; |
| 169 | RR-Net: Injecting Interactive Semantics in Human-Object Interaction Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we therefore propose novel relation reasoning for HOI detection. |
Dongming Yang; Yuexian Zou; Can Zhang; Meng Cao; Jie Chen; |
| 170 | Non-contact Pain Recognition from Video Sequences with Remote Physiological Measurements Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel multi-task learning framework which encodes both appearance changes and physiological cues in a non-contact manner for pain recognition. |
Ruijing Yang; Ziyu Guan; Zitong Yu; Xiaoyi Feng; Jinye Peng; Guoying Zhao; |
| 171 | Coupling Intent and Action for Pedestrian Crossing Behavior Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we follow the neuroscience and psychological literature to define pedestrian crossing behavior as a combination of an unobserved inner will (a probabilistic representation of binary intent of crossing vs. not crossing) and a set of multi-class actions (e.g., walking, standing, etc.). |
Yu Yao; Ella Atkins; Matthew Johnson-Roberson; Ram Vasudevan; Xiaoxiao Du; |
| 172 | Object Detection in Densely Packed Scenes Via Semi-Supervised Learning with Dual Consistency Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel semi-supervised approach to addressing this problem, which is designed based on a common teacher-student model, integrated with a novel intersection-over-union (IoU) aware consistency loss and a new proposal consistency loss. |
Chao Ye; Huaidong Zhang; Xuemiao Xu; Weiwei Cai; Jing Qin; Kup-Sze Choi; |
| 173 | Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a unified adversarial face generation method – Adv-Makeup, which can realize imperceptible and transferable attack under the black-box setting. |
Bangjie Yin; Wenxuan Wang; Taiping Yao; Junfeng Guo; Zelun Kong; Shouhong Ding; Jilin Li; Cong Liu; |
| 174 | Multimodal Transformer Networks for Pedestrian Trajectory Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these issues, we propose an efficient multimodal transformer network that aggregates the trajectory and ego-vehicle speed variations at a coarse granularity and interacts with the optical flow in a fine-grained level to fill the vacancy of highly dynamic motion. |
Ziyi Yin; Ruijin Liu; Zhiliang Xiong; Zejian Yuan; |
| 175 | EmbedMask: Embedding Coupling for Instance Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a single-stage method, named EmbedMask, that unifies both methods by taking their advantages, so it can achieve good performance in instance segmentation and produce high-resolution masks in a high speed. |
Hui Ying; Zhaojin Huang; Shu Liu; Tianjia Shao; Kun Zhou; |
| 176 | CogTree: Cognition Tree Loss for Unbiased Scene Graph Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we analyze this problem from a novel cognition perspective: automatically building a hierarchical cognitive structure from the biased predictions and navigating that hierarchy to locate the relationships, making the tail relationships receive more attention in a coarse-to-fine mode. |
Jing Yu; Yuan Chai; Yujing Wang; Yue Hu; Qi Wu; |
| 177 | Dual-Cross Central Difference Network for Face Anti-Spoofing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose two Cross Central Difference Convolutions (C-CDC), which exploit the difference of the center and surround sparse local features from the horizontal/vertical and diagonal directions, respectively. |
Zitong Yu; Yunxiao Qin; Hengshuang Zhao; Xiaobai Li; Guoying Zhao; |
| 178 | Detecting Deepfake Videos with Temporal Dropout 3DCNN Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address that, this paper aims to leverage the possible inconsistent cues among video frames and proposes a Temporal Dropout 3-Dimensional Convolutional Neural Network (TD-3DCNN) to detect deepfake videos. |
Daichi Zhang; Chenyu Li; Fanzhao Lin; Dan Zeng; Shiming Ge; |
| 179 | Low Resolution Information Also Matters: Learning Multi-Resolution Representations for Person Re-Identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we explore the influence of resolutions on feature extraction and develop a novel method for cross-resolution person re-ID called Multi-Resolution Representations Joint Learning (MRJL). |
Guoqing Zhang; Yuhao Chen; Weisi Lin; Arun Chandran; Xuan Jing; |
| 180 | Removing Foreground Occlusions in Light Field Using Micro-lens Dynamic Filter Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a learning-based method combining ‘seeking’ and ‘generating’ to recover occluded background. |
Shuo Zhang; Zeqi Shen; Youfang Lin; |
| 181 | Learning Implicit Temporal Alignment for Few-shot Video Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our main idea is to introduce an implicit temporal alignment for a video pair, capable of estimating the similarity between them in an accurate and robust manner. |
Songyang Zhang; Jiale Zhou; Xuming He; |
| 182 | What If We Could Not See? Counterfactual Analysis for Egocentric Action Anticipation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: While future actions may be wrongly predicted due to the dataset bias, we present a counterfactual analysis framework for egocentric action anticipation (CA-EAA) to enhance the capacity. |
Tianyu Zhang; Weiqing Min; Jiahao Yang; Tao Liu; Shuqiang Jiang; Yong Rui; |
| 183 | Context-Aware Image Inpainting with Learned Semantic Priors Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this problem, we introduce pretext tasks that are semantically meaningful to estimating the missing contents. |
Wendong Zhang; Junwei Zhu; Ying Tai; Yunbo Wang; Wenqing Chu; Bingbing Ni; Chengjie Wang; Xiaokang Yang; |
| 184 | Sequential 3D Human Pose Estimation Using Adaptive Point Cloud Sampling Strategy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a new perspective on the 3D human pose estimation method from point cloud sequences. |
Zihao Zhang; Lei Hu; Xiaoming Deng; Shihong Xia; |
| 185 | Rescuing Deep Hashing from Dead Bits Problem Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a simple but effective gradient amplifier which acts before activation functions to alleviate DBP. |
Shu Zhao; Dayan Wu; Yucan Zhou; Bo Li; Weiping Wang; |
| 186 | PointLIE: Locally Invertible Embedding for Point Cloud Sampling and Recovery Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel Locally Invertible Embedding (PointLIE) framework to unify the point cloud sampling and upsampling into one single framework through bi-directional learning. |
Weibing Zhao; Xu Yan; Jiantao Gao; Ruimao Zhang; Jiayan Zhang; Zhen Li; Song Wu; Shuguang Cui; |
| 187 | A Sketch-Transformer Network for Face Photo-Sketch Synthesis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a face photo-sketch synthesis model, which converts a face photo into an artistic face sketch or recover a photo-realistic facial image from a sketch portrait. |
Mingrui Zhu; Changcheng Liang; Nannan Wang; Xiaoyu Wang; Zhifeng Li; Xinbo Gao; |
| 188 | PoseGTAC: Graph Transformer Encoder-Decoder with Atrous Convolution for 3D Human Pose Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this issue, we propose a novel Graph Transformer Encoder-Decoder with Atrous Convolution, named PoseGTAC, to effectively extract multi-scale context and long-range information. |
Yiran Zhu; Xing Xu; Fumin Shen; Yanli Ji; Lianli Gao; Heng Tao Shen; |
| 189 | Reducing SAT to Max2SAT Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we provide an efficient and constructive method for Reducing SAT to Max2SAT and show empirical results of how MaxSAT solvers are more efficient than SAT solvers solving the translation of hard formulas for Resolution. |
Carlos Ansótegui; Jordi Levy; |
| 190 | Improved CP-Based Lagrangian Relaxation Approach with An Application to The TSP Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an improved CP-LR approach that locally modifies the Lagrangian multipliers in order to increase the number of filtered values. |
Raphaël Boudreault; Claude-Guy Quimper; |
| 191 | Efficiently Explaining CSPs with Unsatisfiable Subset Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We build on these formal foundations and tackle two emerging questions, namely how to generate explanations that are provably optimal (with respect to the given cost metric) and how to generate them efficiently. |
Emilio Gamba; Bart Bogaerts; Tias Guns; |
| 192 | Decomposition Strategies to Count Integer Solutions Over Linear Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose new decomposition techniques which target both the elimination of variables as well as inequalities using structural properties of counting problems. |
Cunjing Ge; Armin Biere; |
| 193 | Solving Graph Homomorphism and Subgraph Isomorphism Problems Faster Through Clique Neighbourhood Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We show how techniques from the state-of-the-art in subgraph isomorphism solving can be applied to broader graph homomorphism problems, and introduce a new form of filtering based upon clique-finding. |
Sonja Kraiczy; Ciaran McCreesh; |
| 194 | Backdoor DNFs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce backdoor DNFs, as a tool to measure the theoretical hardness of CNF formulas. |
Sebastian Ordyniak; Andre Schidler; Stefan Szeider; |
| 195 | Learning Implicitly with Noisy Data in Linear Arithmetic Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we extend implicit learning in PAC-Semantics to handle noisy data in the form of intervals and threshold uncertainty in the language of linear arithmetic. |
Alexander Rader; Ionela G Mocanu; Vaishak Belle; Brendan Juba; |
| 196 | Computing Optimal Hypertree Decompositions with SAT Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel characterization for hypertree width in terms of linear elimination orderings. |
Andre Schidler; Stefan Szeider; |
| 197 | Exploring Periodicity and Interactivity in Multi-Interest Framework for Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a novel method called PIMI to mitigate this issue. |
Gaode Chen; Xinghua Zhang; Yanyan Zhao; Cong Xue; Ji Xiang; |
| 198 | Masked Contrastive Learning for Anomaly Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a task-specific variant of contrastive learning named masked contrastive learning, which is more befitted for anomaly detection. |
Hyunsoo Cho; Jinseok Seol; Sang-goo Lee; |
| 199 | Multi-Channel Pooling Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a Multi-channel Graph Pooling method named MuchPool, which captures the local structure, the global structure, and node feature simultaneously in graph pooling. |
Jinlong Du; Senzhang Wang; Hao Miao; Jiaqiang Zhang; |
| 200 | Guided Attention Network for Concept Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel model, named Guided Attention Concept Extraction Network (GACEN), which uses title, topic, and clue words as additional supervision to provide guidance directly. |
Songtao Fang; Zhenya Huang; Ming He; Shiwei Tong; Xiaoqing Huang; Ye Liu; Jie Huang; Qi Liu; |
| 201 | Learning Stochastic Equivalence Based on Discrete Ricci Curvature Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Because automorphic equivalence and regular equivalence strictly tie the role of a node to the identities of all its neighbors. To mitigate this problem, we construct a framework called Curvature-based Network Embedding with Stochastic Equivalence (CNESE) to embed stochastic equivalence. |
Xuan Guo; Qiang Tian; Wang Zhang; Wenjun Wang; Pengfei Jiao; |
| 202 | Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new FL framework with sparsification-amplified privacy. |
Rui Hu; Yanmin Gong; Yuanxiong Guo; |
| 203 | Temporal Heterogeneous Information Network Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to fill the gap of obtaining multi-type node embeddings by considering all temporal dynamics during the evolution, we propose a novel temporal HIN embedding method (THINE). |
Hong Huang; Ruize Shi; Wei Zhou; Xiao Wang; Hai Jin; Xiaoming Fu; |
| 204 | Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome this problem, inspired by the recent success of graph contrastive learning and Siamese networks in visual representation learning, we propose a novel self-supervised approach in this paper to learn node representations by enhancing Siamese self-distillation with multi-scale contrastive learning. |
Ming Jin; Yizhen Zheng; Yuan-Fang Li; Chen Gong; Chuan Zhou; Shirui Pan; |
| 205 | Practical One-Shot Federated Learning for Cross-Silo Setting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a practical one-shot federated learning algorithm named FedKT. |
Qinbin Li; Bingsheng He; Dawn Song; |
| 206 | Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Sequence-to-Graph (Seq2Graph) augmentation for each POI sequence, allowing collaborative signals to be propagated from correlated POIs belonging to other sequences. |
Yang Li; Tong Chen; Yadan Luo; Hongzhi Yin; Zi Huang; |
| 207 | Modeling Trajectories with Neural Ordinary Differential Equations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle these challenges, we devise a novel method entitled TrajODE for more natural modeling of trajectories. |
Yuxuan Liang; Kun Ouyang; Hanshu Yan; Yiwei Wang; Zekun Tong; Roger Zimmermann; |
| 208 | RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate this problem, we propose a robust framework using collaborative autoencoders to jointly identify normal observations from the data while learning its feature representation. |
Boyang Liu; Ding Wang; Kaixiang Lin; Pang-Ning Tan; Jiayu Zhou; |
| 209 | MG-DVD: A Real-time Framework for Malware Variant Detection Based on Dynamic Heterogeneous Graph Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose MG-DVD, a novel detection framework based on dynamic heterogeneous graph learning, to detect malware variants in real time. |
Chen Liu; Bo Li; Jun Zhao; Ming Su; Xu-Dong Liu; |
| 210 | Node-wise Localization of Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To utilize node locality without overfitting, we propose a node-wise localization of GNNs by accounting for both global and local aspects of the graph. |
Zemin Liu; Yuan Fang; Chenghao Liu; Steven C.H. Hoi; |
| 211 | GraphReach: Position-Aware Graph Neural Network Using Reachability Estimations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop GRAPHREACH , a position-aware inductive GNN that captures the global positions of nodes through reachability estimations with respect to a set of anchor nodes. |
Sunil Nishad; Shubhangi Agarwal; Arnab Bhattacharya; Sayan Ranu; |
| 212 | Graph Edit Distance Learning Via Modeling Optimum Matchings with Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel GED-specific loss function that simultaneously encodes the two characteristics. |
Yun Peng; Byron Choi; Jianliang Xu; |
| 213 | GAEN: Graph Attention Evolving Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Graph Attention Evolving Networks (GAEN) for dynamic network embedding with preserved similarities between nodes derived from their temporal variation patterns. |
Min Shi; Yu Huang; Xingquan Zhu; Yufei Tang; Yuan Zhuang; Jianxun Liu; |
| 214 | Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a novel Unified Message Passaging Model (UniMP) that can incorporate feature and label propagation at both training and inference time. |
Yunsheng Shi; Zhengjie Huang; Shikun Feng; Hui Zhong; Wenjing Wang; Yu Sun; |
| 215 | Keyword-Based Knowledge Graph Exploration Based on Quadratic Group Steiner Trees Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, to model cohesiveness in a generalized way, the quadratic group Steiner tree problem (QGSTP) is formulated where the cost function extends GST with quadratic terms representing semantic distances. |
Yuxuan Shi; Gong Cheng; Trung-Kien Tran; Jie Tang; Evgeny Kharlamov; |
| 216 | Federated Model Distillation with Noise-Free Differential Privacy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel framework called FEDMD-NFDP, which applies a Noise-FreeDifferential Privacy (NFDP) mechanism into a federated model distillation framework. |
Lichao Sun; Lingjuan Lyu; |
| 217 | LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we proposed a novel design of local differential privacy mechanism for federated learning to address the abovementioned issues. |
Lichao Sun; Jianwei Qian; Xun Chen; |
| 218 | Does Every Data Instance Matter? Enhancing Sequential Recommendation By Eliminating Unreliable Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This inspires us to design a novel SRS By Eliminating unReliable Data (BERD) guided with two observations: (1) unreliable instances generally have high training loss; and (2) high-loss instances are not necessarily unreliable but uncertain ones caused by blurry sequential pattern. |
Yatong Sun; Bin Wang; Zhu Sun; Xiaochun Yang; |
| 219 | Cooperative Joint Attentive Network for Patient Outcome Prediction on Irregular Multi-Rate Multivariate Health Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Hence, we propose a novel Cooperative Joint Attentive Network (CJANet) to analyze IMR-MTS in frequency domain, which adaptively handling discrepant dominant frequencies while tackling diverse data qualities caused by irregular sampling. |
Qingxiong Tan; Mang Ye; Grace Lai-Hung Wong; PongChi Yuen; |
| 220 | Pattern-enhanced Contrastive Policy Learning Network for Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a PatteRn-enhanced ContrAstive Policy Learning Network (RAP for short) for sequential recommendation, RAP formalizes the denoising problem in the form of Markov Decision Process (MDP), and sample actions for each item to determine whether it is relevant with the target item. |
Xiaohai Tong; Pengfei Wang; Chenliang Li; Long Xia; Shaozhang Niu; |
| 221 | Heuristic Search for Approximating One Matrix in Terms of Another Matrix Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose the first nontrivial optimal algorithm for the general case, using a heuristic search setting similar to the classical A* algorithm. |
Guihong Wan; Haim Schweitzer; |
| 222 | Preference-Adaptive Meta-Learning for Cold-Start Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, in this paper, we propose a Preference-Adaptive Meta-Learning approach (PAML) to improve existing meta-learning frameworks with better generalization capacity. |
Li Wang; Binbin Jin; Zhenya Huang; Hongke Zhao; Defu Lian; Qi Liu; Enhong Chen; |
| 223 | Federated Learning with Fair Averaging Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we identify a cause of unfairness in FL — conflicting gradients with large differences in the magnitudes. |
Zheng Wang; Xiaoliang Fan; Jianzhong Qi; Chenglu Wen; Cheng Wang; Rongshan Yu; |
| 224 | User-as-Graph: User Modeling with Heterogeneous Graph Pooling for News Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel user modeling approach for news recommendation, which models each user as a personalized heterogeneous graph built from user behaviors to better capture the fine-grained behavior relatedness. |
Chuhan Wu; Fangzhao Wu; Yongfeng Huang; Xing Xie; |
| 225 | Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle these challenges, we propose Spatial-Temporal Sequential Hypergraph Network (ST-SHN) to collectively encode complex crime spatial-temporal patterns as well as the underlying category-wise crime semantic relationships. |
Lianghao Xia; Chao Huang; Yong Xu; Peng Dai; Liefeng Bo; Xiyue Zhang; Tianyi Chen; |
| 226 | Heterogeneous Graph Information Bottleneck Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, a novel Heterogeneous Graph Information Bottleneck (HGIB) is proposed to implement the consensus hypothesis in an unsupervised manner. |
Liang Yang; Fan Wu; Zichen Zheng; Bingxin Niu; Junhua Gu; Chuan Wang; Xiaochun Cao; Yuanfang Guo; |
| 227 | Graph Deformer Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a simple yet effective Graph Deformer Network (GDN) to fulfill anisotropic convolution filtering on graphs, analogous to the standard convolution operation on images. |
Wenting Zhao; Yuan Fang; Zhen Cui; Tong Zhang; Jian Yang; |
| 228 | Knowledge-based Residual Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Hence, in this paper, we propose a hybrid model KRL that treats domain knowledge model as a weak learner and uses another neural net model to boost it. |
Guanjie Zheng; Chang Liu; Hua Wei; Porter Jenkins; Chacha Chen; Tao Wen; Zhenhui Li; |
| 229 | Faster Guarantees of Evolutionary Algorithms for Maximization of Monotone Submodular Functions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, the monotone submodular maximization problem (SM) is studied. |
Victoria G. Crawford; |
| 230 | DACBench: A Benchmark Library for Dynamic Algorithm Configuration Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To facilitate benchmarking and thus research on DAC, we propose DACBench, a benchmark library that seeks to collect and standardize existing DAC benchmarks from different AI domains, as well as provide a template for new ones. |
Theresa Eimer; André Biedenkapp; Maximilian Reimer; Steven Adriansen; Frank Hutter; Marius Lindauer; |
| 231 | Bounded-cost Search Using Estimates of Uncertainty Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces a relatively simple algorithm, Expected Effort Search (XES), which uses not just point estimates but belief distributions in order to estimate the probability that a node will lead to a plan within the bound. |
Maximilian Fickert; Tianyi Gu; Wheeler Ruml; |
| 232 | A Runtime Analysis of Typical Decomposition Approaches in MOEA/D Framework for Many-objective Optimization Problems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a theoretical analysis on the convergence ability of using the typical weighted sum (WS), Tchebycheff (TCH) or penalty-based boundary intersection (PBI) approach in a basic MOEA/D for solving two benchmark MaOPs. |
Zhengxin Huang; Yuren Zhou; Chuan Luo; Qingwei Lin; |
| 233 | A New Upper Bound Based on Vertex Partitioning for The Maximum K-plex Problem Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new upper bound for MKP, which is a partitioning of the candidate vertex set with respect to the constructing solution. |
Hua Jiang; Dongming Zhu; Zhichao Xie; Shaowen Yao; Zhang-Hua Fu; |
| 234 | Choosing The Right Algorithm With Hints From Complexity Theory Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we argue that the purely academic question of what could be the best possible algorithm in a certain broad class of black-box optimizers can give fruitful indications in which direction to search for good established optimization heuristics. |
Shouda Wang; Weijie Zheng; Benjamin Doerr; |
| 235 | UIBert: Learning Generic Multimodal Representations for UI Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address such challenges we introduce UIBert, a transformer-based joint image-text model trained through novel pre-training tasks on large-scale unlabeled UI data to learn generic feature representations for a UI and its components. |
Chongyang Bai; Xiaoxue Zang; Ying Xu; Srinivas Sunkara; Abhinav Rastogi; Jindong Chen; Blaise Agüera y Arcas; |
| 236 | Pruning of Deep Spiking Neural Networks Through Gradient Rewiring Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, inspired by synaptogenesis and synapse elimination in the neural system, we propose gradient rewiring (Grad R), a joint learning algorithm of connectivity and weight for SNNs, that enables us to seamlessly optimize network structure without retraining. |
Yanqi Chen; Zhaofei Yu; Wei Fang; Tiejun Huang; Yonghong Tian; |
| 237 | Human-AI Collaboration with Bandit Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our solution aims to exploit the human-machine complementarity to maximize decision rewards. |
Ruijiang Gao; Maytal Saar-Tsechansky; Maria De-Arteaga; Ligong Han; Min Kyung Lee; Matthew Lease; |
| 238 | Accounting for Confirmation Bias in Crowdsourced Label Aggregation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present an algorithmic approach to infer the correct answers of tasks by aggregating the annotations from multiple crowd workers, while taking workers’ various levels of confirmation bias into consideration. |
Meric Altug Gemalmaz; Ming Yin; |
| 239 | An Entanglement-driven Fusion Neural Network for Video Sentiment Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To fill this gap, we propose a transparent quantum probabilistic neural model. |
Dimitris Gkoumas; Qiuchi Li; Yijun Yu; Dawei Song; |
| 240 | Event-based Action Recognition Using Motion Information and Spiking Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by the dorsal stream in visual cortex, we propose a hierarchical SNN architecture for event-based action recognition using motion information. |
Qianhui Liu; Dong Xing; Huajin Tang; De Ma; Gang Pan; |
| 241 | Item Response Ranking for Cognitive Diagnosis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose Item Response Ranking framework (IRR), aiming at introducing pairwise learning into cognitive diagnosis to well model the monotonicity between item responses. |
Shiwei Tong; Qi Liu; Runlong Yu; Wei Huang; Zhenya Huang; Zachary A. Pardos; Weijie Jiang; |
| 242 | Type Anywhere You Want: An Introduction to Invisible Mobile Keyboard Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome these limitations, we propose an Invisible Mobile Keyboard (IMK), which lets users freely type on the desired area without any constraints. |
Sahng-Min Yoo; Ue-Hwan Kim; Yewon Hwang; Jong-Hwan Kim; |
| 243 | Best-Effort Synthesis: Doing Your Best Is Not Harder Than Giving Up Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We provide optimal algorithms for computing best-effort strategies, both in the case of LTL over infinite traces and LTL over finite traces (i.e., LTLf). |
Benjamin Aminof; Giuseppe De Giacomo; Sasha Rubin; |
| 244 | A Game-Theoretic Account of Responsibility Allocation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We model strategic multi-agent interaction as an extensive form game of imperfect information and define notions of forward (prospective) and backward (retrospective) responsibility. |
Christel Baier; Florian Funke; Rupak Majumdar; |
| 245 | On Cycles, Attackers and Supporters — A Contribution to The Investigation of Dynamics in Abstract Argumentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In particular, we investigate the impact of addition or removal of arguments, a line of research that has been around for more than a decade. |
Ringo Baumann; Markus Ulbricht; |
| 246 | Reasoning About Agents That May Know Other Agents’ Strategies Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the semantics of knowledge in strategic reasoning. |
Francesco Belardinelli; Sophia Knight; Alessio Lomuscio; Bastien Maubert; Aniello Murano; Sasha Rubin; |
| 247 | Choice Logics and Their Computational Properties Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In particular, we investigate strong equivalence, a core concept in non-classical logics for understanding formula simplification, and computational complexity. |
Michael Bernreiter; Jan Maly; Stefan Woltran; |
| 248 | Cardinality Queries Over DL-Lite Ontologies Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we advance this line of research by investigating cardinality queries (which correspond to Boolean atomic counting queries) coupled with DL-Lite ontologies. |
Meghyn Bienvenu; Quentin Manière; Michaël Thomazo; |
| 249 | Budget-Constrained Coalition Strategies with Discounting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The paper proposes a sound and complete logical system for reasoning about budget-constrained strategic abilities that incorporates discounting into its semantics. |
Lia Bozzone; Pavel Naumov; |
| 250 | Abductive Learning with Ground Knowledge Base Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes Grounded Abductive Learning (GABL) to enhance machine learning models with abductive reasoning in a ground domain knowledge base, which offers inexact supervision through a set of logic propositions. |
Le-Wen Cai; Wang-Zhou Dai; Yu-Xuan Huang; Yu-Feng Li; Stephen Muggleton; Yuan Jiang; |
| 251 | Intensional and Extensional Views in DL-Lite Ontologies Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we study views in Ontology-Based Data Access (OBDA) systems. |
Marco Console; Giuseppe De Giacomo; Maurizio Lenzerini; Manuel Namici; |
| 252 | On Belief Change for Multi-Label Classifier Encodings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: However, usual change operations are not suited to the task of modifying the classifier encoding S in a minimal way, to make it complying with T. To fill the gap, we present a new belief change operation, called rectification. |
Sylvie Coste-Marquis; Pierre Marquis; |
| 253 | A Uniform Abstraction Framework for Generalized Planning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, by extending their abstraction framework, we propose a uniform abstraction framework for generalized planning. |
Zhenhe Cui; Yongmei Liu; Kailun Luo; |
| 254 | Abductive Knowledge Induction from Raw Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present Abductive Meta-Interpretive Learning (MetaAbd) that unites abduction and induction to learn neural networks and logic theories jointly from raw data. |
Wang-Zhou Dai; Stephen Muggleton; |
| 255 | Finite-Trace and Generalized-Reactivity Specifications in Temporal Synthesis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We first study the case in which we have an LTLf agent goal and a GR(1) assumption. We then add to the framework safety conditions for both the environment and the agent, obtaining a highly expressive yet still scalable form of LTL synthesis. |
Giuseppe De Giacomo; Antonio Di Stasio; Lucas M. Tabajara; Moshe Vardi; Shufang Zhu; |
| 256 | HyperLDLf: A Logic for Checking Properties of Finite Traces Process Logs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, motivated by BPM, we introduce HyperLDLf, a logic that extends LDLf with the hyper features of HyperLTL. |
Giuseppe De Giacomo; Paolo Felli; Marco Montali; Giuseppe Perelli; |
| 257 | How Hard to Tell? Complexity of Belief Manipulation Through Propositional Announcements Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper studies the problem of the existence of such an announcement in the context of model-preference definable revision operators. |
Thomas Eiter; Aaron Hunter; Francois Schwarzentruber; |
| 258 | Improved Algorithms for Allen’s Interval Algebra: A Dynamic Programming Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we narrow this gap by presenting two novel algorithms for temporal CSPs based on dynamic programming. |
Leif Eriksson; Victor Lagerkvist; |
| 259 | Decomposition-Guided Reductions for Argumentation and Treewidth Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address whether one can design reductions from argumentation problems to SAT-problems while linearly preserving the treewidth, which results in decomposition-guided (DG) reductions. |
Johannes Fichte; Markus Hecher; Yasir Mahmood; Arne Meier; |
| 260 | Actively Learning Concepts and Conjunctive Queries Under ELr-Ontologies Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We consider the problem to learn a concept or a query in the presence of an ontology formulated in the description logic ELr, in Angluin’s framework of active learning that allows the learning algorithm to interactively query an oracle (such as a domain expert). |
Maurice Funk; Jean Christoph Jung; Carsten Lutz; |
| 261 | Program Synthesis As Dependency Quantified Formula Modulo Theory Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper investigates the feasibility of synthesis techniques without grammar, a sub-class defined as T constrained synthesis. |
Priyanka Golia; Subhajit Roy; Kuldeep S. Meel; |
| 262 | Updating The Belief Promotion Operator Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this note, we introduce the local version of the operator for belief promotion proposed by Schwind et al. We propose a set of postulates and provide a representation theorem that characterizes the proposal. |
Daniel A. Grimaldi; M. Vanina Martinez; Ricardo O. Rodriguez; |
| 263 | Using Platform Models for A Guided Explanatory Diagnosis Generation for Mobile Robots Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel approach to explanatory diagnosis, based on the assumption that most failures occur due to some robot hardware failure. |
Daniel Habering; Till Hofmann; Gerhard Lakemeyer; |
| 264 | HIP Network: Historical Information Passing Network for Extrapolation Reasoning on Temporal Knowledge Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose the Historical Information Passing (HIP) network to predict future events. |
Yongquan He; Peng Zhang; Luchen Liu; Qi Liang; Wenyuan Zhang; Chuang Zhang; |
| 265 | Multi-Agent Abstract Argumentation Frameworks With Incomplete Knowledge of Attacks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a multi-agent, dynamic extension of abstract argumentation frameworks (AFs), strongly inspired by epistemic logic, where agents have only partial information about the conflicts between arguments. |
Andreas Herzig; Antonio Yuste Ginel; |
| 266 | Signature-Based Abduction with Fresh Individuals and Complex Concepts for Description Logics Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the computational complexity of this form of abduction—allowing either fresh individuals, complex concepts, or both—for various description logics, and give size bounds on the hypotheses if they exist. |
Patrick Koopmann; |
| 267 | Scalable Non-observational Predicate Learning in ASP Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In particular, we present our new FastNonOPL system, which upgrades FastLAS with the new possibility generation. |
Mark Law; Alessandra Russo; Krysia Broda; Elisa Bertino; |
| 268 | Inferring Time-delayed Causal Relations in POMDPs from The Principle of Independence of Cause and Mechanism Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces an algorithm for discovering implicit and delayed causal relations between events observed by a robot at regular or arbitrary times, with the objective of improving data-efficiency and interpretability of model-based reinforcement learning (RL) techniques. |
Junchi Liang; Abdeslam Boularias; |
| 269 | Reasoning About Beliefs and Meta-Beliefs By Regression in An Expressive Probabilistic Action Logic Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we will address this and other shortcomings by extending the language and modifying the semantics of belief and only-believing. |
Daxin Liu; Gerhard Lakemeyer; |
| 270 | Multi-Agent Belief Base Revision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a generalization of belief base revision to the multi-agent case. |
Emiliano Lorini; Francois Schwarzentruber; |
| 271 | Bounded Predicates in Description Logics with Counting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper investigates reasoning about upper bounds on predicate sizes for ontologies written in the expressive DL ALCHOIQ extended with closed predicates. |
Sanja Lukumbuzya; Mantas Simkus; |
| 272 | On The Relation Between Approximation Fixpoint Theory and Justification Theory Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To be precise, we show that every justification frame induces an approximator and that this mapping from JT to AFT preserves all major semantics. |
Simon Marynissen; Bart Bogaerts; Marc Denecker; |
| 273 | Faster Smarter Proof By Induction in Isabelle/HOL Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present sem_ind, a recommendation tool for proof by induction in Isabelle/HOL. |
Yutaka Nagashima; |
| 274 | Two Forms of Responsibility in Strategic Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The paper shows that being blamable is definable through seeing to it, but not the other way around. |
Pavel Naumov; Jia Tao; |
| 275 | Compressing Exact Cover Problems with Zero-suppressed Binary Decision Diagrams Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our proposal can improve DLX by exploiting a novel data structure, DanceDD, which extends the zero-suppressed binary decision diagram (ZDD) by adding links to enable efficient modifications of the data structure. |
Masaaki Nishino; Norihito Yasuda; Kengo Nakamura; |
| 276 | Modeling Precomputation In Games Played Under Computational Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a novel model for the precomputation (preparing moves in advance) aspect of computationally constrained games. |
Thomas Orton; |
| 277 | A Ladder of Causal Distances Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we overcome this limitation by defining distances derived from the causal distributions induced by the models, rather than exclusively from their graphical structure. |
Maxime Peyrard; Robert West; |
| 278 | Unsupervised Knowledge Graph Alignment By Probabilistic Reasoning and Semantic Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we aim at combining the above two solutions and thus propose an iterative framework named PRASE which is based on probabilistic reasoning and semantic embedding. |
Zhiyuan Qi; Ziheng Zhang; Jiaoyan Chen; Xi Chen; Yuejia Xiang; Ningyu Zhang; Yefeng Zheng; |
| 279 | Efficient PAC Reinforcement Learning in Regular Decision Processes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our main contribution is to show that a near-optimal policy can be PAC-learned in polynomial time in a set of parameters that describe the underlying decision process. |
Alessandro Ronca; Giuseppe De Giacomo; |
| 280 | Inconsistency Measurement for Paraconsistent Inference Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce an approach based on inconsistency measurement for defining non-monotonic paraconsistent consequence relations. |
Yakoub Salhi; |
| 281 | A Description Logic for Analogical Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To mitigate this issue, we present a mechanism to infer plausible missing knowledge, which relies on reasoning by analogy. |
Steven Schockaert; Yazmin Ibanez-Garcia; Victor Gutierrez-Basulto; |
| 282 | Ranking Extensions in Abstract Argumentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present the notion of extension-ranking semantics, which determines a preordering over sets of arguments, where one set is deemed more plausible than another if it is somehow more acceptable. |
Kenneth Skiba; Tjitze Rienstra; Matthias Thimm; Jesse Heyninck; Gabriele Kern-Isberner; |
| 283 | Physics-informed Spline Learning for Nonlinear Dynamics Discovery Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this fundamental challenge, we propose a novel Physics-informed Spline Learning (PiSL) framework to discover parsimonious governing equations for nonlinear dynamics, based on sparsely sampled noisy data. |
Fangzheng Sun; Yang Liu; Hao Sun; |
| 284 | Lifting Symmetry Breaking Constraints with Inductive Logic Programming Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome these limitations, we introduce a new model-oriented approach for Answer Set Programming that lifts the SBCs of small problem instances into a set of interpretable first-order constraints using the Inductive Logic Programming paradigm. |
Alice Tarzariol; Martin Gebser; Konstantin Schekotihin; |
| 285 | Skeptical Reasoning with Preferred Semantics in Abstract Argumentation Without Computing Preferred Extensions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We address the problem of deciding skeptical acceptance wrt. preferred semantics of an argument in abstract argumentation frameworks, i.e., the problem of deciding whether an argument is contained in all maximally admissible sets, a.k.a. preferred extensions. |
Matthias Thimm; Federico Cerutti; Mauro Vallati; |
| 286 | Abstract Argumentation Frameworks with Domain Assignments Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an argumentation formalism that allows associating arguments with a domain of application. |
Alexandros Vassiliades; Theodore Patkos; Giorgos Flouris; Antonis Bikakis; Nick Bassiliades; Dimitris Plexousakis; |
| 287 | Transforming Robotic Plans with Timed Automata to Solve Temporal Platform Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we describe how to transform an abstract plan into a platform-specific action sequence that satisfies all platform constraints. |
Tarik Viehmann; Till Hofmann; Gerhard Lakemeyer; |
| 288 | Neighborhood Intervention Consistency: Measuring Confidence for Knowledge Graph Link Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To fill this critical gap, we propose a novel confidence measurement method based on causal intervention, called Neighborhood Intervention Consistency (NIC). |
Kai Wang; Yu Liu; Quan Z. Sheng; |
| 289 | Causal Discovery with Multi-Domain LiNGAM for Latent Factors Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Multi-Domain Linear Non-Gaussian Acyclic Models for LAtent Factors (MD-LiNA), where the causal structure among latent factors of interest is shared for all domains, and we provide its identification results. |
Yan Zeng; Shohei Shimizu; Ruichu Cai; Feng Xie; Michio Yamamoto; Zhifeng Hao; |
| 290 | AMEIR: Automatic Behavior Modeling, Interaction Exploration and MLP Investigation in The Recommender System Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To relieve those efforts, we explore the potential of neural architecture search (NAS) and introduce AMEIR for Automatic behavior Modeling, interaction Exploration and multi-layer perceptron (MLP) Investigation in the Recommender system. |
Pengyu Zhao; Kecheng Xiao; Yuanxing Zhang; Kaigui Bian; Wei Yan; |
| 291 | The Surprising Power of Graph Neural Networks with Random Node Initialization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we analyze the expressive power of GNNs with RNI, and prove that these models are universal, a first such result for GNNs not relying on computationally demanding higher-order properties. |
Ralph Abboud; İsmail İlkan Ceylan; Martin Grohe; Thomas Lukasiewicz; |
| 292 | Likelihood-free Out-of-Distribution Detection with Invertible Generative Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a different framework for generative model–based OOD detection that employs the model in constructing a new representation space, instead of using it directly in computing typicality scores, where it is emphasized that the score function should be interpretable as the similarity between the input and training data in the new space. |
Amirhossein Ahmadian; Fredrik Lindsten; |
| 293 | Simulation of Electron-Proton Scattering Events By A Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN) Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We develop a GAN that selects a set of transformed features from particle momenta that can be generated easily by the generator, and uses these to produce a set of augmented features that improve the sensitivity of the discriminator. |
Yasir Alanazi; Nobuo Sato; Tianbo Liu; Wally Melnitchouk; Pawel Ambrozewicz; Florian Hauenstein; Michelle P. Kuchera; Evan Pritchard; Michael Robertson; Ryan Strauss; Luisa Velasco; Yaohang Li; |
| 294 | Deep Reinforcement Learning for Navigation in AAA Video Games Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: As an alternative to the NavMesh, we propose to use Deep Reinforcement Learning (Deep RL) to learn how to navigate 3D maps in video games using any navigation ability. |
Eloi Alonso; Maxim Peter; David Goumard; Joshua Romoff; |
| 295 | Conditional Self-Supervised Learning for Few-Shot Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose conditional self-supervised learning (CSS) to use auxiliary information to guide the representation learning of self-supervised tasks. |
Yuexuan An; Hui Xue; Xingyu Zhao; Lu Zhang; |
| 296 | DEHB: Evolutionary Hyberband for Scalable, Robust and Efficient Hyperparameter Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we combine the advantages of the popular bandit-based HPO method Hyperband (HB) and the evolutionary search approach of Differential Evolution (DE) to yield a new HPO method which we call DEHB. |
Noor Awad; Neeratyoy Mallik; Frank Hutter; |
| 297 | Verifying Reinforcement Learning Up to Infinity Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce the first method for verifying the time-unbounded safety of neural networks controlling dynamical systems. |
Edoardo Bacci; Mirco Giacobbe; David Parker; |
| 298 | Robustly Learning Composable Options in Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose three methods for improving the composability of learned skills: representing skill initiation regions using a combination of pessimistic and optimistic classifiers; learning re-targetable policies that are robust to non-stationary subgoal regions; and learning robust option policies using model-based RL. |
Akhil Bagaria; Jason Senthil; Matthew Slivinski; George Konidaris; |
| 299 | Reconciling Rewards with Predictive State Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose reward-predictive state representations (R-PSRs), a generalization of PSRs which accurately models both observations and rewards, and develop value iteration for R-PSRs. |
Andrea Baisero; Christopher Amato; |
| 300 | Optimal Algorithms for Range Searching Over Multi-Armed Bandits Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper studies a multi-armed bandit (MAB) version of the range-searching problem. |
Siddharth Barman; Ramakrishnan Krishnamurthy; Saladi Rahul; |
| 301 | Efficient Neural Network Verification Via Layer-based Semidefinite Relaxations and Linear Cuts Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce an efficient and tight layer-based semidefinite relaxation for verifying local robustness of neural networks. |
Ben Batten; Panagiotis Kouvaros; Alessio Lomuscio; Yang Zheng; |
| 302 | Fast Pareto Optimization for Subset Selection with Dynamic Cost Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new algorithm FPOMC by combining the merits of the generalized greedy algorithm and POMC. |
Chao Bian; Chao Qian; Frank Neumann; Yang Yu; |
| 303 | Partial Multi-Label Optimal Margin Distribution Machine Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose the Partial Multi-Label Optimal margin Distribution Machine (PML-ODM), which distinguishs the noisy labels through explicitly optimizing the distribution of ranking margin, and exhibits better generalization performance than minimum margin based counterparts. |
Nan Cao; Teng Zhang; Hai Jin; |
| 304 | Towards Understanding The Spectral Bias of Deep Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we give a comprehensive and rigorous explanation for spectral bias and relate it with the neural tangent kernel function proposed in recent work. |
Yuan Cao; Zhiying Fang; Yue Wu; Ding-Xuan Zhou; Quanquan Gu; |
| 305 | Thompson Sampling for Bandits with Clustered Arms Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose algorithms based on a multi-level Thompson sampling scheme, for the stochastic multi-armed bandit and its contextual variant with linear expected rewards, in the setting where arms are clustered. |
Emil Carlsson; Devdatt Dubhashi; Fredrik D. Johansson; |
| 306 | Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in A First-person Simulated 3D Environment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we formulate learning an attentive object dynamics model as a classification problem, using random object-images to define incorrect labels for our object-dynamics model. |
Wilka Carvalho; Anthony Liang; Kimin Lee; Sungryull Sohn; Honglak Lee; Richard Lewis; Satinder Singh; |
| 307 | Generative Adversarial Neural Architecture Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Generative Adversarial NAS (GA-NAS) with theoretically provable convergence guarantees, promoting stability and reproducibility in neural architecture search. |
Seyed Saeed Changiz Rezaei; Fred X. Han; Di Niu; Mohammad Salameh; Keith Mills; Shuo Lian; Wei Lu; Shangling Jui; |
| 308 | AMA-GCN: Adaptive Multi-layer Aggregation Graph Convolutional Network for Disease Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an encoder that automatically selects the appropriate phenotypic measures according to their spatial distribution, and uses the text similarity awareness mechanism to calculate the edge weights between nodes. |
Hao Chen; Fuzhen Zhuang; Li Xiao; Ling Ma; Haiyan Liu; Ruifang Zhang; Huiqin Jiang; Qing He; |
| 309 | Learning Attributed Graph Representation with Communicative Message Passing Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For this sake, we propose a Communicative Message Passing Transformer (CoMPT) neural network to improve the molecular graph representation by reinforcing message interactions between nodes and edges based on the Transformer architecture. |
Jianwen Chen; Shuangjia Zheng; Ying Song; Jiahua Rao; Yuedong Yang; |
| 310 | Understanding Structural Vulnerability in Graph Convolutional Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we theoretically and empirically demonstrate that structural adversarial examples can be attributed to the non-robust aggregation scheme (i.e., the weighted mean) of GCNs. |
Liang Chen; Jintang Li; Qibiao Peng; Yang Liu; Zibin Zheng; Carl Yang; |
| 311 | Monte Carlo Filtering Objectives Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Monte Carlo filtering objectives (MCFOs), a family of variational objectives for jointly learning parametric generative models and amortized adaptive importance proposals of time series. |
Shuangshuang Chen; Sihao Ding; Yiannis Karayiannidis; Mårten Björkman; |
| 312 | Dependent Multi-Task Learning with Causal Intervention for Image Captioning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a dependent multi-task learning framework with the causal intervention (DMTCI). |
Wenqing Chen; Jidong Tian; Caoyun Fan; Hao He; Yaohui Jin; |
| 313 | Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we show that such inductive bias can be learned from a flat collection of unlabeled images, and instantiated as transferable representations among seen and unseen classes. |
Wentao Chen; Chenyang Si; Wei Wang; Liang Wang; Zilei Wang; Tieniu Tan; |
| 314 | On Self-Distilling Graph Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose the first teacher-free knowledge distillation method for GNNs, termed GNN Self-Distillation (GNN-SD), that serves as a drop-in replacement of the standard training process. |
Yuzhao Chen; Yatao Bian; Xi Xiao; Yu Rong; Tingyang Xu; Junzhou Huang; |
| 315 | Time-Aware Multi-Scale RNNs for Time Series Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose Time-Aware Multi-Scale Recurrent Neural Networks (TAMS-RNNs), which disentangle representations of different scales and adaptively select the most important scale for each sample at each time step. |
Zipeng Chen; Qianli Ma; Zhenxi Lin; |
| 316 | Variational Model-based Policy Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we leverage the connection between RL and probabilistic inference, and formulate such an objective function as a variational lower-bound of a log-likelihood. |
Yinlam Chow; Brandon Cui; Moonkyung Ryu; Mohammad Ghavamzadeh; |
| 317 | CuCo: Graph Representation with Curriculum Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the impact of negative samples on learning graph-level representations, and a novel curriculum contrastive learning framework for self-supervised graph-level representation, called CuCo, is proposed. |
Guanyi Chu; Xiao Wang; Chuan Shi; Xunqiang Jiang; |
| 318 | Convexified Graph Neural Networks for Distributed Control in Robotic Swarms Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces novel architectures which solve the convexity restriction and can be easily updated in a distributed, online manner. |
Saar Cohen; Noa Agmon; |
| 319 | Isotonic Data Augmentation for Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we denote eliminating rank violations in data augmentation for knowledge distillation as isotonic data augmentation (IDA). |
Wanyun Cui; Sen Yan; |
| 320 | Graph-Free Knowledge Distillation for Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to our best knowledge the first dedicated approach to distilling knowledge from a GNN without graph data. |
Xiang Deng; Zhongfei Zhang; |
| 321 | Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we theoretically analyze ANN-SNN conversion and derive sufficient conditions of the optimal conversion. |
Jianhao Ding; Zhaofei Yu; Yonghong Tian; Tiejun Huang; |
| 322 | Boosting Variational Inference With Locally Adaptive Step-Sizes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We characterize how the global curvature impacts time and memory consumption, address the problem with the notion of local curvature, and provide a novel approximate backtracking algorithm for estimating local curvature. |
Gideon Dresdner; Saurav Shekhar; Fabian Pedregosa; Francesco Locatello; Gunnar Rätsch; |
| 323 | Automatic Translation of Music-to-Dance for In-Game Characters Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper provides a new solution to this task where we re-formulate the translation as a piece-wise dance phrase retrieval problem based on the choreography theory. |
Yinglin Duan; Tianyang Shi; Zhipeng Hu; Zhengxia Zou; Changjie Fan; Yi Yuan; Xi Li; |
| 324 | Time-Series Representation Learning Via Temporal and Contextual Contrasting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an unsupervised Time-Series representation learning framework via Temporal and Contextual Contrasting (TS-TCC), to learn time-series representation from unlabeled data. |
Emadeldeen Eldele; Mohamed Ragab; Zhenghua Chen; Min Wu; Chee Keong Kwoh; Xiaoli Li; Cuntai Guan; |
| 325 | Jointly Learning Prices and Product Features Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study this problem from the viewpoint of online learning: a firm repeatedly interacts with a buyer by choosing a product configuration as well as a price and observing the buyer’s purchasing decision. |
Ehsan Emamjomeh-Zadeh; Renato Paes Leme; Jon Schneider; Balasubramanian Sivan; |
| 326 | BAMBOO: A Multi-instance Multi-label Approach Towards VDI User Logon Behavior Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, a novel formulation towards VDI user logon behavior modeling is proposed by employing the multi-instance multi-label (MIML) techniques. |
Wenping Fan; Yao Zhang; Qichen Hao; Xinya Wu; Min-Ling Zhang; |
| 327 | Contrastive Model Invertion for Data-Free Knolwedge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Contrastive Model Inversion (CMI), where the data diversity is explicitly modeled as an optimizable objective, to alleviate the mode collapse issue. |
Gongfan Fang; Jie Song; Xinchao Wang; Chengchao Shen; Xingen Wang; Mingli Song; |
| 328 | Deep Reinforcement Learning for Multi-contact Motion Planning of Hexapod Robots Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, a novel Deep Reinforcement Learning (DRL) method is proposed to implement multi-contact motion planning for hexapod robots moving on uneven plum-blossom piles. |
Huiqiao Fu; Kaiqiang Tang; Peng Li; Wenqi Zhang; Xinpeng Wang; Guizhou Deng; Tao Wang; Chunlin Chen; |
| 329 | On The Convergence of Stochastic Compositional Gradient Descent Ascent Method Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop a novel efficient stochastic compositional gradient descent ascent method for optimizing the compositional minimax problem. |
Hongchang Gao; Xiaoqian Wang; Lei Luo; Xinghua Shi; |
| 330 | Learning Groupwise Explanations for Black-Box Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We illustrate that the two user demands correspond to two major sub-processes in the human cognitive process and propose a unified framework to fulfill them simultaneously. |
Jingyue Gao; Xiting Wang; Yasha Wang; Yulan Yan; Xing Xie; |
| 331 | Video Summarization Via Label Distributions Dual-Reward Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve this problem, in this paper label distributions are mapped from the CNN and LSTM-based state representation to capture the subjectiveness of video summaries. |
Yongbiao Gao; Ning Xu; Xin Geng; |
| 332 | BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose BOBCAT, a Bilevel Optimization-Based framework for CAT to directly learn a data-driven question selection algorithm from training data. |
Aritra Ghosh; Andrew Lan; |
| 333 | Method of Moments for Topic Models with Mixed Discrete and Continuous Features Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we extend this line of work to topic models that feature discrete as well as continuous observable variables (features). |
Joachim Giesen; Paul Kahlmeyer; Sören Laue; Matthias Mitterreiter; Frank Nussbaum; Christoph Staudt; Sina Zarrieß; |
| 334 | Bayesian Experience Reuse for Learning from Multiple Demonstrators Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this, we formulate a quadratic program whose solution yields an adaptive weighting over experts, that can be used to sample experts with relevant goals. |
Mike Gimelfarb; Scott Sanner; Chi-Guhn Lee; |
| 335 | Fast Multi-label Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The goal of this paper is to provide a simple method, yet with provable guarantees, which can achieve competitive performance without a complex training process. |
Xiuwen Gong; Dong Yuan; Wei Bao; |
| 336 | InverseNet: Augmenting Model Extraction Attacks with Training Data Inversion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we put forth a novel and effective attack strategy, dubbed InverseNet, that steals the functionality of black-box cloud-based models with only a small number of queries. |
Xueluan Gong; Yanjiao Chen; Wenbin Yang; Guanghao Mei; Qian Wang; |
| 337 | Hierarchical Class-Based Curriculum Loss Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a loss function, hierarchical curriculum loss, with two properties: (i) satisfy hierarchical constraints present in the label space, and (ii) provide non-uniform weights to labels based on their levels in the hierarchy, learned implicitly by the training paradigm. |
Palash Goyal; Divya Choudhary; Shalini Ghosh; |
| 338 | The Successful Ingredients of Policy Gradient Algorithms Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we identify these mechanisms – which we call ingredients – in on-policy policy gradient methods and empirically determine their impact on the learning. |
Sven Gronauer; Martin Gottwald; Klaus Diepold; |
| 339 | Learning Nash Equilibria in Zero-Sum Stochastic Games Via Entropy-Regularized Policy Approximation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new Q-learning type algorithm that uses a sequence of entropy-regularized soft policies to approximate the Nash policy during the Q-function updates. |
Yue Guan; Qifan Zhang; Panagiotis Tsiotras; |
| 340 | Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we theoretically explain why the widely-used small-loss criterion works. |
Xian-Jin Gui; Wei Wang; Zhang-Hao Tian; |
| 341 | Hindsight Value Function for Variance Reduction in Stochastic Dynamic Environment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to replace the state value function with a novel hindsight value function, which leverages the information from the future to reduce the variance of the gradient estimate for stochastic dynamic environments. |
Jiaming Guo; Rui Zhang; Xishan Zhang; Shaohui Peng; Qi Yi; Zidong Du; Xing Hu; Qi Guo; Yunji Chen; |
| 342 | DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel graph-based solution, namely DA-GCN, to address the above challenges. |
Lei Guo; Li Tang; Tong Chen; Lei Zhu; Quoc Viet Hung Nguyen; Hongzhi Yin; |
| 343 | Robust Regularization with Adversarial Labelling of Perturbed Samples Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Revisiting Vicinal Risk Minimization (VRM) as a unifying regularization principle, we propose Adversarial Labelling of Perturbed Samples (ALPS) as a regularization scheme that aims at improving the generalization ability and adversarial robustness of the trained model. |
Xiaohui Guo; Richong Zhang; Yaowei Zheng; Yongyi Mao; |
| 344 | Enabling Retrain-free Deep Neural Network Pruning Using Surrogate Lagrangian Relaxation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop a systematic weight-pruning optimization approach based on Surrogate Lagrangian relaxation (SLR), which is tailored to overcome difficulties caused by the discrete nature of the weight-pruning problem while ensuring fast convergence. |
Deniz Gurevin; Mikhail Bragin; Caiwen Ding; Shanglin Zhou; Lynn Pepin; Bingbing Li; Fei Miao; |
| 345 | Riemannian Stochastic Recursive Momentum Method for Non-Convex Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a stochastic recursive momentum method for Riemannian non-convex optimization that achieves a nearly-optimal complexity to find epsilon-approximate solution with one sample. |
Andi Han; Junbin Gao; |
| 346 | Fine-Grained Air Quality Inference Via Multi-Channel Attention Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the problem of fine-grained air quality inference that predicts the air quality level of any location from air quality readings of nearby monitoring stations. |
Qilong Han; Dan Lu; Rui Chen; |
| 347 | Model-Based Reinforcement Learning for Infinite-Horizon Discounted Constrained Markov Decision Processes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose two model-based constrained reinforcement learning (CRL) algorithms for learning a safe policy, namely, (i) GM-CRL algorithm, where the algorithm has access to a generative model, and (ii) UC-CRL algorithm, where the algorithm learns the model using an upper confidence style online exploration method. |
Aria HasanzadeZonuzy; Dileep Kalathil; Srinivas Shakkottai; |
| 348 | State-Based Recurrent SPMNs for Decision-Theoretic Planning Under Partial Observability Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present an algorithm for learning compact template structures by identifying unique belief states and the transitions between them through a state matching process that utilizes augmented data. |
Layton Hayes; Prashant Doshi; Swaraj Pawar; Hari Teja Tatavarti; |
| 349 | Beyond The Spectrum: Detecting Deepfakes Via Re-Synthesis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to overcome this issue, we propose a novel fake detection that is designed to re-synthesize testing images and extract visual cues for detection. |
Yang He; Ning Yu; Margret Keuper; Mario Fritz; |
| 350 | Interpretable Minority Synthesis for Imbalanced Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a novel oversampling approach that strives to balance the class priors with a considerably imbalanced data distribution of high dimensionality. |
Yi He; Fudong Lin; Xu Yuan; Nian-Feng Tzeng; |
| 351 | DEEPSPLIT: An Efficient Splitting Method for Neural Network Verification Via Indirect Effect Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel, complete algorithm for the verification and analysis of feed-forward, ReLU-based neural networks. |
Patrick Henriksen; Alessio Lomuscio; |
| 352 | Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a novel Federated Learning algorithm (called IGFL), which leverages both Individual and Group behaviors to mimic distribution, thereby improving the ability to deal with heterogeneity. |
Hua Huang; Fanhua Shang; Yuanyuan Liu; Hongying Liu; |
| 353 | UniGNN: A Unified Framework for Graph and Hypergraph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose UniGNN, a unified framework for interpreting the message passing process in graph and hypergraph neural networks, which can generalize general GNN models into hypergraphs. |
Jing Huang; Jie Yang; |
| 354 | Asynchronous Active Learning with Distributed Label Querying Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome this challenge, we propose a multi-server multi-worker framework for asynchronous active learning in the distributed environment. |
Sheng-Jun Huang; Chen-Chen Zong; Kun-Peng Ning; Hai-Bo Ye; |
| 355 | On The Neural Tangent Kernel of Deep Networks with Orthogonal Initialization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we study the dynamics of ultra-wide networks across a range of architectures, including Fully Connected Networks (FCNs) and Convolutional Neural Networks (CNNs) with orthogonal initialization via neural tangent kernel (NTK). |
Wei Huang; Weitao Du; Richard Yi Da Xu; |
| 356 | On Explaining Random Forests with SAT Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Hence, one question is whether finding explanations of RFs can be solved in polynomial time. This paper answers this question negatively, by proving that computing one PI-explanation of an RF is D^P-hard. |
Yacine Izza; Joao Marques-Silva; |
| 357 | Reinforcement Learning for Route Optimization with Robustness Guarantees Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we address robust optimization, which is a more complex variant where a max-min problem is to be solved. |
Tobias Jacobs; Francesco Alesiani; Gulcin Ermis; |
| 358 | Learning CNF Theories Using MDL and Predicate Invention Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel algorithm, called Mistle — Minimal SAT Theory Learner, for learning such theories. |
Arcchit Jain; Clément Gautrais; Angelika Kimmig; Luc De Raedt; |
| 359 | Learning to Learn Personalized Neural Network for Ventricular Arrhythmias Detection on Intracardiac EGMs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a one-dimensional convolutional neural network (1D-CNN) based life-threatening VAs detection on IEGMs. |
Zhenge Jia; Zhepeng Wang; Feng Hong; Lichuan PING; Yiyu Shi; Jingtong Hu; |
| 360 | SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Staging Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these challenges, we propose SalientSleepNet, a multimodal salient wave detection network for sleep staging. |
Ziyu Jia; Youfang Lin; Jing Wang; Xuehui Wang; Peiyi Xie; Yingbin Zhang; |
| 361 | Knowledge Consolidation Based Class Incremental Online Learning with Limited Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel approach for class incremental online learning in a limited data setting. |
Mohammed Asad Karim; Vinay Kumar Verma; Pravendra Singh; Vinay Namboodiri; Piyush Rai; |
| 362 | Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we theoretically show that the KL divergence loss focuses on the logit matching when ? increases and the label matching when ? goes to 0 and empirically show that the logit matching is positively correlated to performance improvement in general. |
Taehyeon Kim; Jaehoon Oh; Nak Yil Kim; Sangwook Cho; Se-Young Yun; |
| 363 | Epsilon Best Arm Identification in Spectral Bandits Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an analysis of Probably Approximately Correct (PAC) identification of an ?-best arm in graph bandit models with Gaussian distributions. |
Tomáš Kocák; Aurélien Garivier; |
| 364 | Towards Scalable Complete Verification of Relu Neural Networks Via Dependency-based Branching Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce an efficient method for the complete verification of ReLU-based feed-forward neural networks. |
Panagiotis Kouvaros; Alessio Lomuscio; |
| 365 | Solving Continuous Control with Episodic Memory Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our study aims to answer the question: can episodic memory be used to improve agent’s performance in continuous control? |
Igor Kuznetsov; Andrey Filchenkov; |
| 366 | On Guaranteed Optimal Robust Explanations for NLP Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present two solution algorithms, respectively based on implicit hitting sets and maximum universal subsets, introducing a number of algorithmic improvements to speed up convergence of hard instances. |
Emanuele La Malfa; Rhiannon Michelmore; Agnieszka M. Zbrzezny; Nicola Paoletti; Marta Kwiatkowska; |
| 367 | Topological Uncertainty: Monitoring Trained Neural Networks Through Persistence of Activation Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop a method to monitor trained neural networks based on the topological properties of their activation graphs. |
Théo Lacombe; Yuichi Ike; Mathieu Carrière; Frédéric Chazal; Marc Glisse; Yuhei Umeda; |
| 368 | RetCL: A Selection-based Approach for Retrosynthesis Via Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new approach that mitigates the issues by reformulating retrosynthesis into a selection problem of reactants from a candidate set of commercially available molecules. |
Hankook Lee; Sungsoo Ahn; Seung-Woo Seo; You Young Song; Eunho Yang; Sung Ju Hwang; Jinwoo Shin; |
| 369 | TextGTL: Graph-based Transductive Learning for Semi-supervised Text Classification Via Structure-Sensitive Interpolation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, beyond the existing architecture of heterogeneous word-document graphs, for the first time, we investigate how to construct lightweight non-heterogeneous graphs based on different linguistic information to better serve free text representation learning. |
Chen Li; Xutan Peng; Hao Peng; Jianxin Li; Lihong Wang; |
| 370 | Regularising Knowledge Transfer By Meta Functional Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, this work proposes a novel Meta Functional Learning (MFL) by meta-learning a generalisable functional model from data-rich tasks whilst simultaneously regularising knowledge transfer to data-scarce tasks. |
Pan Li; Yanwei Fu; Shaogang Gong; |
| 371 | Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a simple and effective self-supervised pre-training strategy, named Pairwise Half-graph Discrimination (PHD), that explicitly pre-trains a graph neural network at graph-level. |
Pengyong Li; Jun Wang; Ziliang Li; Yixuan Qiao; Xianggen Liu; Fei Ma; Peng Gao; Sen Song; Guotong Xie; |
| 372 | SHPOS: A Theoretical Guaranteed Accelerated Particle Optimization Sampling Method Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an accelerated particle optimization sampling method called Stochastic Hamiltonian Particle Optimization Sampling (SHPOS). |
Zhijian Li; Chao Zhang; Hui Qian; Xin Du; Lingwei Peng; |
| 373 | An Adaptive News-Driven Method for CVaR-sensitive Online Portfolio Selection in Non-Stationary Financial Markets Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the CS-OLPS problem in non-stationary markets, we propose an effective news-driven method, named CAND, which adaptively exploits news to determine the adjustment tendency and adjustment scale for tracking the dynamic optimal portfolio with minimal CVaR in each trading round. |
Qianqiao Liang; Mengying Zhu; Xiaolin Zheng; Yan Wang; |
| 374 | Residential Electric Load Forecasting Via Attentive Transfer of Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an attentive transfer learning-based GNN model that can utilize the learned prior knowledge to improve the learning process in a new area. |
Weixuan Lin; Di Wu; |
| 375 | Graph Filter-based Multi-view Attributed Graph Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Multi-view Attributed Graph Clustering (MvAGC) method, which is simple yet effective. |
Zhiping Lin; Zhao Kang; |
| 376 | On The Intrinsic Differential Privacy of Bagging Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our major theoretical results show that such intrinsic randomness already makes Bagging differentially private without the needs of additional noise. |
Hongbin Liu; Jinyuan Jia; Neil Zhenqiang Gong; |
| 377 | Two-stage Training for Learning from Label Proportions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we regard these problems as noisy pseudo labeling, and instead impose the strict proportion consistency on the classifier with a constrained optimization as a continuous training stage for existing LLP classifiers. |
Jiabin Liu; Bo Wang; Xin Shen; Zhiquan Qi; Yingjie Tian; |
| 378 | Adversarial Spectral Kernel Matching for Unsupervised Time Series Domain Adaptation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an Adversarial Spectral Kernel Matching (AdvSKM) method, where a hybrid spectral kernel network is specifically designed as inner kernel to reform the Maximum Mean Discrepancy (MMD) metric for UTSDA. |
Qiao Liu; Hui Xue; |
| 379 | Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore combining deep learning with expert patterns in an explainable fashion. |
Zhenguang Liu; Peng Qian; Xiang Wang; Lei Zhu; Qinming He; Shouling Ji; |
| 380 | Transfer Learning Via Optimal Transportation for Integrative Cancer Patient Stratification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel unsupervised multi-view transfer learning algorithm to simultaneously analyze multiple biomarkers in different cancer types. |
Ziyu Liu; Wei Shao; Jie Zhang; Min Zhang; Kun Huang; |
| 381 | Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we revisit NEDS from the perspective of minimum entropy principle. |
Gongxu Luo; Jianxin Li; Hao Peng; Carl Yang; Lichao Sun; Philip S. Yu; Lifang He; |
| 382 | Stochastic Actor-Executor-Critic for Image-to-Image Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we draw inspiration from the recent success of the maximum entropy reinforcement learning framework designed for challenging continuous control problems to develop stochastic policies over high dimensional continuous spaces including image representation, generation, and control simultaneously. |
Ziwei Luo; Jing Hu; Xin Wang; Siwei Lyu; Bin Kong; Youbing Yin; Qi Song; Xi Wu; |
| 383 | Hierarchical Temporal Multi-Instance Learning for Video-based Student Learning Engagement Assessment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome such a challenge, this paper proposes a novel hierarchical multiple instance learning (MIL) solution, which only requires labels anchored on full-length videos to learn to assess student engagement at an arbitrary temporal granularity and for an arbitrary duration in a study session. |
Jiayao Ma; Xinbo Jiang; Songhua Xu; Xueying Qin; |
| 384 | Multi-Cause Effect Estimation with Disentangled Confounder Representation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on the multi-cause effect estimation problem from a new perspective by learning disentangled representations of confounders. |
Jing Ma; Ruocheng Guo; Aidong Zhang; Jundong Li; |
| 385 | Average-Reward Reinforcement Learning with Trust Region Methods Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the reinforcement learning problem with the long-run average criterion. |
Xiaoteng Ma; Xiaohang Tang; Li Xia; Jun Yang; Qianchuan Zhao; |
| 386 | Temporal and Object Quantification Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present Temporal and Object Quantification Networks (TOQ-Nets), a new class of neuro-symbolic networks with a structural bias that enables them to learn to recognize complex relational-temporal events. |
Jiayuan Mao; Zhezheng Luo; Chuang Gan; Joshua B. Tenenbaum; Jiajun Wu; Leslie Pack Kaelbling; Tomer D. Ullman; |
| 387 | Evaluating Relaxations of Logic for Neural Networks: A Comprehensive Study Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the question of how best to relax logical expressions that represent labeled examples and knowledge about a problem; we focus on sub-differentiable t-norm relaxations of logic. |
Mattia Medina Grespan; Ashim Gupta; Vivek Srikumar; |
| 388 | Minimization of Limit-Average Automata Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the minimization problem for (deterministic) LimAvg-automata. |
Jakub Michaliszyn; Jan Otop; |
| 389 | Details (Don’t) Matter: Isolating Cluster Information in Deep Embedded Spaces Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose our framework, ACe/DeC, that is compatible with Autoencoder Centroid based Deep Clustering methods and automatically learns a latent representation consisting of two separate spaces. |
Lukas Miklautz; Lena G. M. Bauer; Dominik Mautz; Sebastian Tschiatschek; Christian Böhm; Claudia Plant; |
| 390 | Contrastive Losses and Solution Caching for Predict-and-Optimize Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this context, we provide two distinct contributions. |
Maxime Mulamba; Jayanta Mandi; Michelangelo Diligenti; Michele Lombardi; Victor Bucarey; Tias Guns; |
| 391 | Fine-grained Generalization Analysis of Structured Output Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we significantly improve the state of the art by developing novel high-probability bounds with a logarithmic dependency on d. Furthermore, we leverage the lens of algorithmic stability to develop generalization bounds in expectation without any dependency on d. |
Waleed Mustafa; Yunwen Lei; Antoine Ledent; Marius Kloft; |
| 392 | Accelerating Neural Architecture Search Via Proxy Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: By analyzing proxy data constructed using various selection methods through data entropy, we propose a novel proxy data selection method tailored for NAS. |
Byunggook Na; Jisoo Mok; Hyeokjun Choe; Sungroh Yoon; |
| 393 | What Changed? Interpretable Model Comparison Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a method for interpretable comparison of binary classification models by approximating them with Boolean decision rules. |
Rahul Nair; Massimiliano Mattetti; Elizabeth Daly; Dennis Wei; Oznur Alkan; Yunfeng Zhang; |
| 394 | TIDOT: A Teacher Imitation Learning Approach for Domain Adaptation with Optimal Transport Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Using the principle of imitation learning and the theory of optimal transport we propose in this paper a novel model for unsupervised domain adaptation named Teacher Imitation Domain Adaptation with Optimal Transport (TIDOT). |
Tuan Nguyen; Trung Le; Nhan Dam; Quan Hung Tran; Truyen Nguyen; Dinh Phung; |
| 395 | Learning Embeddings from Knowledge Graphs With Numeric Edge Attributes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel method that injects numeric edge attributes into the scoring layer of a traditional knowledge graph embedding architecture. |
Sumit Pai; Luca Costabello; |
| 396 | Explaining Deep Neural Network Models with Adversarial Gradient Integration Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we propose an Adversarial Gradient Integration (AGI) method that integrates the gradients from adversarial examples to the target example along the curve of steepest ascent to calculate the resulting contributions from all input features. |
Deng Pan; Xin Li; Dongxiao Zhu; |
| 397 | Two Birds with One Stone: Series Saliency for Accurate and Interpretable Multivariate Time Series Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a new scheme of series saliency to boost both accuracy and interpretability. |
Qingyi Pan; Wenbo Hu; Ning Chen; |
| 398 | Learning Aggregation Functions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To mitigate this problem, we introduce LAF (Learning Aggregation Function), a learnable aggregator for sets of arbitrary cardinality. |
Giovanni Pellegrini; Alessandro Tibo; Paolo Frasconi; Andrea Passerini; Manfred Jaeger; |
| 399 | Meta-Reinforcement Learning By Tracking Task Non-stationarity Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel algorithm (TRIO) that optimizes for the future by explicitly tracking the task evolution through time. |
Riccardo Poiani; Andrea Tirinzoni; Marcello Restelli; |
| 400 | Multi-version Tensor Completion for Time-delayed Spatio-temporal Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a low-rank tensor model to predict the updates over time. |
Cheng Qian; Nikos Kargas; Cao Xiao; Lucas Glass; Nicholas Sidiropoulos; Jimeng Sun; |
| 401 | Multi-Agent Reinforcement Learning for Automated Peer-to-Peer Energy Trading in Double-Side Auction Market Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For this reason, in this paper we model this task as a multi-agent reinforcement learning (MARL) problem and propose an algorithm called DA-MADDPG that is modified based on MADDPG by abstracting the other agents’ observations and actions through the DA market public information for each agent’s critic. |
Dawei Qiu; Jianhong Wang; Junkai Wang; Goran Strbac; |
| 402 | Source-free Domain Adaptation Via Avatar Prototype Generation and Adaptation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a Contrastive Prototype Generation and Adaptation (CPGA) method. |
Zhen Qiu; Yifan Zhang; Hongbin Lin; Shuaicheng Niu; Yanxia Liu; Qing Du; Mingkui Tan; |
| 403 | Exact Acceleration of K-Means++ and K-Means|| Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We focus on using triangle inequality based pruning methods to accelerate both of these algorithms to yield comparable or better run-time without sacrificing any of the benefits of these approaches. |
Edward Raff; |
| 404 | Stochastic Shortest Path with Adversarially Changing Costs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we present the adversarial SSP model that also accounts for adversarial changes in the costs over time, while the underlying transition function remains unchanged. |
Aviv Rosenberg; Yishay Mansour; |
| 405 | Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a framework, physics-aware meta-learning with auxiliary tasks, whose spatial modules incorporate PDE-independent knowledge and temporal modules utilize the generalized features from the spatial modules to be adapted to the limited data, respectively. |
Sungyong Seo; Chuizheng Meng; Sirisha Rambhatla; Yan Liu; |
| 406 | Don’t Do What Doesn’t Matter: Intrinsic Motivation with Action Usefulness Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new exploration method, called Don’t Do What Doesn’t Matter (DoWhaM), shifting the emphasis from state novelty to state with relevant actions. |
Mathieu Seurin; Florian Strub; Philippe Preux; Olivier Pietquin; |
| 407 | Towards Robust Model Reuse in The Presence of Latent Domains Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the above issue, in this paper we propose the MRL (Model Reuse for multiple Latent domains) method. |
Jie-Jing Shao; Zhanzhan Cheng; Yu-Feng Li; Shiliang Pu; |
| 408 | Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, in this paper, we propose an alternative model, DU-VAE, for learning a more Diverse and less Uncertain latent space, and thus the representation can be learned in a meaningful and compact manner. |
Dazhong Shen; Chuan Qin; Chao Wang; Hengshu Zhu; Enhong Chen; Hui Xiong; |
| 409 | Interpretable Compositional Convolutional Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a method to modify a traditional convolutional neural network (CNN) into an interpretable compositional CNN, in order to learn filters that encode meaningful visual patterns in intermediate convolutional layers. |
Wen Shen; Zhihua Wei; Shikun Huang; Binbin Zhang; Jiaqi Fan; Ping Zhao; Quanshi Zhang; |
| 410 | Unsupervised Progressive Learning and The STAM Architecture Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve the UPL problem we propose the Self-Taught Associative Memory (STAM) architecture. |
James Smith; Cameron Taylor; Seth Baer; Constantine Dovrolis; |
| 411 | Online Risk-Averse Submodular Maximization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a polynomial-time online algorithm for maximizing the conditional value at risk (CVaR) of a monotone stochastic submodular function. |
Tasuku Soma; Yuichi Yoshida; |
| 412 | Positive-Unlabeled Learning from Imbalanced Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore this problem and propose a general learning objective for PU learning targeting specially at imbalanced data. |
Guangxin Su; Weitong Chen; Miao Xu; |
| 413 | Neural Architecture Search of SPD Manifold Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new neural architecture search (NAS) problem of Symmetric Positive Definite (SPD) manifold networks, aiming to automate the design of SPD neural architectures. |
Rhea Sanjay Sukthanker; Zhiwu Huang; Suryansh Kumar; Erik Goron Endsjo; Yan Wu; Luc Van Gool; |
| 414 | TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel Time Encoding (TE) mechanism. |
Chenxi Sun; Shenda Hong; Moxian Song; Yen-Hsiu Chou; Yongyue Sun; Derun Cai; Hongyan Li; |
| 415 | MFNP: A Meta-optimized Model for Few-shot Next POI Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel meta-optimized model MFNP, which can rapidly adapt to users with few check-in records. |
Huimin Sun; Jiajie Xu; Kai Zheng; Pengpeng Zhao; Pingfu Chao; Xiaofang Zhou; |
| 416 | Towards Reducing Biases in Combining Multiple Experts Online Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we aim to accomplish approximate group fairness in an online stochastic decision-making process, where the fairness metric we consider is equalized odds. |
Yi Sun; Iván Ramírez Díaz; Alfredo Cuesta Infante; Kalyan Veeramachaneni; |
| 417 | Predicting Traffic Congestion Evolution: A Deep Meta Learning Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a representation learning framework to characterize and predict congestion evolution between any pair of road segments (connected via single or multiple paths). |
Yidan Sun; Guiyuan Jiang; Siew Kei Lam; Peilan He; |
| 418 | Hyperspectral Band Selection Via Spatial-Spectral Weighted Region-wise Multiple Graph Fusion-Based Spectral Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a hyperspectral band selection method via spatial-spectral weighted region-wise multiple graph fusion-based spectral clustering, referred to as RMGF briefly. |
Chang Tang; Xinwang Liu; En Zhu; Lizhe Wang; Albert Zomaya; |
| 419 | Self-supervised Network Evolution for Few-shot Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate this problem, we propose to evolve the network (for the base set) via label propagation and self-supervision to shrink the distribution difference between the base set and the novel set. |
Xuwen Tang; Zhu Teng; Baopeng Zhang; Jianping Fan; |
| 420 | Dual Active Learning for Both Model and Data Selection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle with this practical challenge, this paper proposes a novel framework of dual active learning (DUAL) to simultaneously perform model search and data selection. |
Ying-Peng Tang; Sheng-Jun Huang; |
| 421 | Compositional Neural Logic Programming Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces Compositional Neural Logic Programming (CNLP), a framework that integrates neural networks and logic programming for symbolic and sub-symbolic reasoning. |
Son N. Tran; |
| 422 | Sensitivity Direction Learning with Neural Networks Using Domain Knowledge As Soft Shape Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Sensitivity Direction Learning (SDL) for learning about the neural network model with user-specified relationships (e.g., monotonicity, convexity) between each input feature and the output of the model by imposing soft shape constraints which represent domain knowledge. |
Kazuyuki Wakasugi; |
| 423 | Learning from Complementary Labels Via Partial-Output Consistency Regularization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we give the first attempt to leverage regularization techniques for CLL. |
Deng-Bao Wang; Lei Feng; Min-Ling Zhang; |
| 424 | Probabilistic Sufficient Explanations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce probabilistic sufficient explanations, which formulate explaining an instance of classification as choosing the "simplest" subset of features such that only observing those features is "sufficient" to explain the classification. |
Eric Wang; Pasha Khosravi; Guy Van den Broeck; |
| 425 | Multi-hop Attention Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we propose Multi-hop Attention Graph Neural Network (MAGNA), a principled way to incorporate multi-hop context information into every layer of attention computation. |
Guangtao Wang; Rex Ying; Jing Huang; Jure Leskovec; |
| 426 | Learn The Highest Label and Rest Label Description Degrees Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To improve classification performance and solve the objective mismatch, we propose a new LDL algorithm called LDL-HR. |
Jing Wang; Xin Geng; |
| 427 | Stability and Generalization for Randomized Coordinate Descent Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we initialize the generalization analysis of RCD by leveraging the powerful tool of algorithmic stability. |
Puyu Wang; Liang Wu; Yunwen Lei; |
| 428 | Discrete Multiple Kernel K-means Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we elaborate a novel Discrete Multiple Kernel k-means (DMKKM) model solved by an optimization algorithm that directly obtains the cluster indicator matrix without subsequent discretization procedures. |
Rong Wang; Jitao Lu; Yihang Lu; Feiping Nie; Xuelong Li; |
| 429 | Mean Field Equilibrium in Multi-Armed Bandit Game with Continuous Reward Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the mean field bandit game with a continuous reward function. |
Xiong Wang; Riheng Jia; |
| 430 | Demiguise Attack: Crafting Invisible Semantic Adversarial Perturbations with Perceptual Similarity Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve these problems, we propose Demiguise Attack, crafting "unrestricted" perturbations with Perceptual Similarity. |
Yajie Wang; Shangbo Wu; Wenyi Jiang; Shengang Hao; Yu-an Tan; Quanxin Zhang; |
| 431 | Self-Supervised Adversarial Distribution Regularization for Medication Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In pursuit of a valuable model for a safe recommendation, we propose the Self-Supervised Adversarial Regularization Model for Medication Recommendation (SARMR). |
Yanda Wang; Weitong Chen; Dechang PI; Lin Yue; Sen Wang; Miao Xu; |
| 432 | Against Membership Inference Attack: Pruning Is All You Need Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a pruning algorithm, and we show that the proposed algorithm can find a subnetwork that can prevent privacy leakage from MIA and achieves competitive accuracy with the original DNNs. |
Yijue Wang; Chenghong Wang; Zigeng Wang; Shanglin Zhou; Hang Liu; Jinbo Bi; Caiwen Ding; Sanguthevar Rajasekaran; |
| 433 | Layer-Assisted Neural Topic Modeling Over Document Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To resolve this kind of data, we develop a novel neural topic model , namely Layer-Assisted Neural Topic Model (LANTM), which can be interpreted from the perspective of variational auto-encoders. |
Yiming Wang; Ximing Li; Jihong Ouyang; |
| 434 | Robust Adversarial Imitation Learning Via Adaptively-Selected Demonstrations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a robust method within the framework of Generative Adversarial Imitation Learning (GAIL) to address imperfect demonstration issue, in which good demonstrations can be adaptively selected for training while bad demonstrations are abandoned. |
Yunke Wang; Chang Xu; Bo Du; |
| 435 | Reinforcement Learning Based Sparse Black-box Adversarial Attack on Video Recognition Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to speed up the attack process, we propose a reinforcement learning based frame selection strategy. |
Zeyuan Wang; Chaofeng Sha; Su Yang; |
| 436 | Reward-Constrained Behavior Cloning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome this problem, we present a novel method named Reward-Constrained Behavior Cloning (RCBC) which synthesizes imitation learning and constrained reinforcement learning. |
Zhaorong Wang; Meng Wang; Jingqi Zhang; Yingfeng Chen; Chongjie Zhang; |
| 437 | Closing The BIG-LID: An Effective Local Intrinsic Dimensionality Defense for Nonlinear Regression Poisoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a new analysis of local intrinsic dimensionality (LID) of nonlinear regression under such poisoning attacks within a Stackelberg game, leading to a practical defense. |
Sandamal Weerasinghe; Tamas Abraham; Tansu Alpcan; Sarah M. Erfani; Christopher Leckie; Benjamin I. P. Rubinstein; |
| 438 | GSPL: A Succinct Kernel Model for Group-Sparse Projections Learning of Multiview Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve the optimization problem involved in GSPL, a novel iterative algorithm is proposed with rigorously theoretical guarantees. |
Danyang Wu; Jin Xu; Xia Dong; Meng Liao; Rong Wang; Feiping Nie; Xuelong Li; |
| 439 | Deep Reinforcement Learning Boosted Partial Domain Adaptation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a deep reinforcement learning based source data selector for PDA, which is capable of eliminating less relevant source samples automatically to boost existing adaptation methods. |
Keyu Wu; Min Wu; Jianfei Yang; Zhenghua Chen; Zhengguo Li; Xiaoli Li; |
| 440 | Learning Deeper Non-Monotonic Networks By Softly Transferring Solution Space Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate the optimization dilemma, in this paper, we propose a non-trivial soft transfer approach. |
Zheng-Fan Wu; Hui Xue; Weimin Bai; |
| 441 | Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we present a general spike-based modeling framework that enables the direct training of SNNs for graph learning. |
Mingkun Xu; Yujie Wu; Lei Deng; Faqiang Liu; Guoqi Li; Jing Pei; |
| 442 | K-Nearest Neighbors By Means of Sequence to Sequence Deep Neural Networks and Memory Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose two families of models built on a sequence to sequence model and a memory network model to mimic the k-Nearest Neighbors model, which generate a sequence of labels, a sequence of out-of-sample feature vectors and a final label for classification, and thus they could also function as oversamplers. |
Yiming Xu; Diego Klabjan; |
| 443 | Evolutionary Gradient Descent for Non-convex Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an evolutionary GD (EGD) algorithm by combining typical components, i.e., population and mutation, of EAs with GD. |
Ke Xue; Chao Qian; Ling Xu; Xudong Fei; |
| 444 | KDExplainer: A Task-oriented Attention Model for Explaining Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a novel task-oriented attention model, termed as KDExplainer, to shed light on the working mechanism underlying the vanilla KD. |
Mengqi Xue; Jie Song; Xinchao Wang; Ying Chen; Xingen Wang; Mingli Song; |
| 445 | Clustering-Induced Adaptive Structure Enhancing Network for Incomplete Multi-View Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a Clustering-induced Adaptive Structure Enhancing Network (CASEN) for incomplete multi-view clustering, which is an end-to-end trainable framework that jointly conducts multi-view structure enhancing and data clustering. |
Zhe Xue; Junping Du; Changwei Zheng; Jie Song; Wenqi Ren; Meiyu Liang; |
| 446 | Differentially Private Pairwise Learning Revisited Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the sub-optimal utility issue, in this paper, we proposed new pure or approximate DP algorithms for pairwise learning. |
Zhiyu Xue; Shaoyang Yang; Mengdi Huai; Di Wang; |
| 447 | Decomposable-Net: Scalable Low-Rank Compression for Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Decomposable-Net (the network decomposable in any size), which allows flexible changes to model size without retraining. |
Atsushi Yaguchi; Taiji Suzuki; Shuhei Nitta; Yukinobu Sakata; Akiyuki Tanizawa; |
| 448 | A Clustering-based Framework for Classifying Data Streams Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we proposed a clustering-based data stream classification framework to handle non-stationary data streams without utilizing an initial label set. |
Xuyang Yan; Abdollah Homaifar; Mrinmoy Sarkar; Abenezer Girma; Edward Tunstel; |
| 449 | Multi-level Generative Models for Partial Label Learning with Non-random Label Noise Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel multi-level generative model for partial label learning (MGPLL), which tackles the PL problem by learning both a label level adversarial generator and a feature level adversarial generator under a bi-directional mapping framework between the label vectors and the data samples. |
Yan Yan; Yuhong Guo; |
| 450 | Secure Deep Graph Generation with Link Differential Privacy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we leverage the differential privacy (DP) framework to formulate and enforce rigorous privacy constraints on deep graph generation models, with a focus on edge-DP to guarantee individual link privacy. |
Carl Yang; Haonan Wang; Ke Zhang; Liang Chen; Lichao Sun; |
| 451 | Progressive Open-Domain Response Generation with Multiple Controllable Attributes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Progressively trained Hierarchical Encoder-Decoder (PHED) to tackle this task. |
Haiqin Yang; Xiaoyuan Yao; Yiqun Duan; Jianping Shen; Jie Zhong; Kun Zhang; |
| 452 | Unsupervised Path Representation Learning with Curriculum Negative Sampling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an unsupervised learning framework Path InfoMax (PIM) to learn generic path representations that work for different downstream tasks. |
Sean Bin Yang; Chenjuan Guo; Jilin Hu; Jian Tang; Bin Yang; |
| 453 | BESA: BERT-based Simulated Annealing for Adversarial Text Attacks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this research, we propose BESA, a BERT-based Simulated Annealing algorithm, to address these two problems. |
Xinghao Yang; Weifeng Liu; Dacheng Tao; Wei Liu; |
| 454 | Rethinking Label-Wise Cross-Modal Retrieval from A Semantic Sharing Perspective Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, in this paper, we argue that what really needed for supervised cross-modal retrieval is a good shared classification model. |
Yang Yang; Chubing Zhang; Yi-Chu Xu; Dianhai Yu; De-Chuan Zhan; Jian Yang; |
| 455 | Blocking-based Neighbor Sampling for Large-scale Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel neighbor sampling strategy, dubbed blocking-based neighbor sampling (BNS), for efficient training of GNNs on large-scale graphs. |
Kai-Lang Yao; Wu-Jun Li; |
| 456 | Understanding The Effect of Bias in Deep Anomaly Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we aim to understand the effect of a biased anomaly set on anomaly detection. Our study demonstrates scenarios in which the biased anomaly set can be useful or problematic, and provides a solid benchmark for future research. |
Ziyu Ye; Yuxin Chen; Haitao Zheng; |
| 457 | Improving Sequential Recommendation Consistency with Self-Supervised Imitation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a model, SSI, to improve sequential recommendation consistency with Self-Supervised Imitation. |
Xu Yuan; Hongshen Chen; Yonghao Song; Xiaofang Zhao; Zhuoye Ding; |
| 458 | Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Worse, the bad actors found for one graph model severely compromise other models as well. We call the bad actors “anchor nodes” and propose an algorithm, named GUA, to identify them. |
Xiao Zang; Yi Xie; Jie Chen; Bo Yuan; |
| 459 | Hindsight Trust Region Policy Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We pro- pose Hindsight Trust Region Policy Optimization (HTRPO), a new RL algorithm that extends the highly successful TRPO algorithm with hindsight to tackle the challenge of sparse rewards. |
Hanbo Zhang; Site Bai; Xuguang Lan; David Hsu; Nanning Zheng; |
| 460 | Deep Descriptive Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose deep descriptive clustering that performs sub-symbolic representation learning on complex data while generating explanations based on symbolic data. |
Hongjing Zhang; Ian Davidson; |
| 461 | Independence-aware Advantage Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this challenge, we propose to identify the independence property between current action and future states in environments, which can be further leveraged to effectively reduce the variance of the advantage estimation. |
Pushi Zhang; Li Zhao; Guoqing Liu; Jiang Bian; Minlie Huang; Tao Qin; Tie-Yan Liu; |
| 462 | UNBERT: User-News Matching BERT for News Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore the use of the successful BERT pre-training technique in NLP for news recommendation and propose a BERT-based user-news matching model, called UNBERT. |
Qi Zhang; Jingjie Li; Qinglin Jia; Chuyuan Wang; Jieming Zhu; Zhaowei Wang; Xiuqiang He; |
| 463 | Correlation-Guided Representation for Multi-Label Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we view the task as a correlation-guided text representation problem: an attention-based two-step framework is proposed to integrate text information and label semantics by jointly learning words and labels in the same space. |
Qian-Wen Zhang; Ximing Zhang; Zhao Yan; Ruifang Liu; Yunbo Cao; Min-Ling Zhang; |
| 464 | Private Stochastic Non-convex Optimization with Improved Utility Rates Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we progress towards an affirmative answer to this open problem: DP nonconvex optimization is indeed capable of achieving the same excess population risk as to the nonprivate algorithm in most common parameter regimes, under certain conditions (i.e., well-conditioned nonconvexity). |
Qiuchen Zhang; Jing Ma; Jian Lou; Li Xiong; |
| 465 | Non-I.I.D. Multi-Instance Learning for Predicting Instance and Bag Labels with Variational Auto-Encoder Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose the Multi-Instance Variational Autoencoder (MIVAE) algorithm which explicitly models the dependencies among the instances for predicting both bag labels and instance labels. |
Weijia Zhang; |
| 466 | Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise Rollouts Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To reduce the upper bound with the intention of low sample complexity during the whole learning process, we propose a novel decentralized model-based MARL method, named Adaptive Opponent-wise Rollout Policy Optimization (AORPO). |
Weinan Zhang; Xihuai Wang; Jian Shen; Ming Zhou; |
| 467 | Rethink The Connections Among Generalization, Memorization, and The Spectral Bias of DNNs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: However, we show that the monotonicity of the learning bias does not always hold: under the experimental setup of deep double descent, the high-frequency components of DNNs diminish in the late stage of training, leading to the second descent of the test error. |
Xiao Zhang; Haoyi Xiong; Dongrui Wu; |
| 468 | User Retention: A Causal Approach with Triple Task Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the above challenges, we propose a novel method named UR-IPW (User Retention Modeling with Inverse Propensity Weighting), which 1) makes full use of both explicit and implicit interactions in the observed data. |
Yang Zhang; Dong Wang; Qiang Li; Yue Shen; Ziqi Liu; Xiaodong Zeng; Zhiqiang Zhang; Jinjie Gu; Derek F. Wong; |
| 469 | Neural Relation Inference for Multi-dimensional Temporal Point Processes Via Message Passing Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a neural relation inference model namely TPP-NRI. |
Yunhao Zhang; Junchi Yan; |
| 470 | Combining Tree Search and Action Prediction for State-of-the-Art Performance in DouDiZhu Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we extend AlphaZero to multiplayer IIGs by developing a new MCTS method, Action-Prediction MCTS (AP-MCTS). |
Yunsheng Zhang; Dong Yan; Bei Shi; Haobo Fu; Qiang Fu; Hang Su; Jun Zhu; Ning Chen; |
| 471 | Uncertainty-Aware Few-Shot Image Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose Uncertainty-Aware Few-Shot framework for image classification by modeling uncertainty of the similarities of query-support pairs and performing uncertainty-aware optimization. |
Zhizheng Zhang; Cuiling Lan; Wenjun Zeng; Zhibo Chen; Shih-Fu Chang; |
| 472 | Automatic Mixed-Precision Quantization Search of BERT Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we proposed an automatic mixed-precision quantization framework designed for BERT that can conduct quantization and pruning simultaneously. |
Changsheng Zhao; Ting Hua; Yilin Shen; Qian Lou; Hongxia Jin; |
| 473 | Graph Debiased Contrastive Learning with Joint Representation Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a graph debiased contrastive learning framework, which can jointly perform representation learning and clustering. |
Han Zhao; Xu Yang; Zhenru Wang; Erkun Yang; Cheng Deng; |
| 474 | Uncertainty-aware Binary Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we investigate the intrinsic uncertainty of vanishing near-zero weights, making the training vulnerable to instability. |
Junhe Zhao; Linlin Yang; Baochang Zhang; Guodong Guo; David Doermann; |
| 475 | Few-Shot Partial-Label Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce an approach called FsPLL (Few-shot PLL). |
Yunfeng Zhao; Guoxian Yu; Lei Liu; Zhongmin Yan; Lizhen Cui; Carlotta Domeniconi; |
| 476 | Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper attempts to provide some new perspectives to encourage the future in-depth studies in these two fields. |
Fan Zhou; Zhoufan Zhu; Qi Kuang; Liwen Zhang; |
| 477 | Multi-Target Invisibly Trojaned Networks for Visual Recognition and Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we take image recognition and detection as the demonstration tasks, building trojaned networks that are significantly less human-perceptible and can simultaneously attack multiple targets in an image. |
Xinzhe Zhou; Wenhao Jiang; Sheng Qi; Yadong Mu; |
| 478 | AutoReCon: Neural Architecture Search-based Reconstruction for Data-free Compression Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we propose the AutoReCon method, which is a neural architecture search-based reconstruction method. |
Baozhou Zhu; Peter Hofstee; Johan Peltenburg; Jinho Lee; Zaid Alars; |
| 479 | You Get What You Sow: High Fidelity Image Synthesis with A Single Pretrained Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a novel strategy for high fidelity image synthesis with a single pretrained classification network. |
Kefeng Zhu; Peilin Tong; Hongwei Kan; Rengang Li; |
| 480 | MapGo: Model-Assisted Policy Optimization for Goal-Oriented Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, to enhance the diversity of relabeled goals, we develop FGI (Foresight Goal Inference), a new relabeling strategy that relabels the goals by looking into the future with a learned dynamics model. |
Menghui Zhu; Minghuan Liu; Jian Shen; Zhicheng Zhang; Sheng Chen; Weinan Zhang; Deheng Ye; Yong Yu; Qiang Fu; Wei Yang; |
| 481 | Toward Optimal Solution for The Context-Attentive Bandit Problem Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we analyze and extend an online learning framework known as Context-Attentive Bandit, We derive a novel algorithm, called Context-Attentive Thompson Sampling (CATS), which builds upon the Linear Thompson Sampling approach, adapting it to Context-Attentive Bandit setting. |
Djallel Bouneffouf; Raphael Feraud; Sohini Upadhyay; Irina Rish; Yasaman Khazaeni; |
| 482 | Sample Efficient Decentralized Stochastic Frank-Wolfe Methods for Continuous DR-Submodular Maximization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose two novel sample efficient decentralized Frank-Wolfe methods to address this challenge. |
Hongchang Gao; Hanzi Xu; Slobodan Vucetic; |
| 483 | Self-Guided Community Detection on Networks with Missing Edges Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a community self-guided generative model which jointly completes the edges-missing network and identifies communities. |
Dongxiao He; Shuai Li; Di Jin; Pengfei Jiao; Yuxiao Huang; |
| 484 | Two-Sided Wasserstein Procrustes Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these challenges, we propose a new method to jointly learn the optimal coupling between twosets, and the optimal transformations (e.g. rotation, projection and scaling) of each set based on a two-sided Wassertein Procrustes analysis (TWP). |
Kun Jin; Chaoyue Liu; Cathy Xia; |
| 485 | Solving Math Word Problems with Teacher Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We thus designed a teacher module to make the MWP encoding vector match the correct solution and disaccord from the wrong solutions, which are manipulated from the correct solution. |
Zhenwen Liang; Xiangliang Zhang; |
| 486 | Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these issues, we propose a collaborative graph learning model to explore patient-disease interactions and medical domain knowledge. |
Chang Lu; Chandan K Reddy; Prithwish Chakraborty; Samantha Kleinberg; Yue Ning; |
| 487 | MDNN: A Multimodal Deep Neural Network for Predicting Drug-Drug Interaction Events Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we propose a Multimodal Deep Neural Network (MDNN) for DDI events prediction. |
Tengfei Lyu; Jianliang Gao; Ling Tian; Zhao Li; Peng Zhang; Ji Zhang; |
| 488 | SPADE: A Semi-supervised Probabilistic Approach for Detecting Errors in Tables Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present SPADE, a novel semi-supervised probabilistic approach for error detection. |
Minh Pham; Craig A. Knoblock; Muhao Chen; Binh Vu; Jay Pujara; |
| 489 | TEC: A Time Evolving Contextual Graph Model for Speaker State Analysis in Political Debates Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose TEC, a time evolving graph based model that jointly employs links between motions, speakers, and temporal politician states. |
Ramit Sawhney; Shivam Agarwal; Arnav Wadhwa; Rajiv Shah; |
| 490 | Adaptive Residue-wise Profile Fusion for Low Homologous Protein Secondary Structure Prediction Using External Knowledge Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explicitly import external self-supervised knowledge for low homologous PSSP under the guidance of residue-wise (amino acid wise) profile fusion. |
Qin Wang; Jun Wei; Boyuan Wang; Zhen Li; Sheng Wang; |