Paper Digest: IJCAI 2023 Highlights
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TABLE 1: Paper Digest: IJCAI 2023 Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | Learning Dissemination Strategies for External Sources in Opinion Dynamic Models with Cognitive Biases Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a model of opinion evolution that uses prospect theory to represent perception of information from the external source along each channel. |
Abdullah Al Maruf; Luyao Niu; Bhaskar Ramasubramanian; Andrew Clark; Radha Poovendran; |
| 2 | Artificial Agents Inspired By Human Motivation Psychology for Teamwork in Hazardous Environments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we demonstrate the potential of endowing artificial agents with a motivation, using human implicit motivation psychology theory that introduces 3 motive profiles – power, achievement and affiliation, to create diverse, risk-aware agents. |
Anupama Arukgoda; Erandi Lakshika; Michael Barlow; Kasun Gunawardana; |
| 3 | Proportionally Fair Online Allocation of Public Goods with Predictions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We design online algorithms for fair allocation of public goods to a set of N agents over a sequence of T rounds and focus on improving their performance using predictions. |
Siddhartha Banerjee; Vasilis Gkatzelis; Safwan Hossain; Billy Jin; Evi Micha; Nisarg Shah; |
| 4 | On The Role of Memory in Robust Opinion Dynamics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We investigate opinion dynamics in a fully-connected system, consisting of n agents, where one of the opinions, called correct, represents a piece of information to disseminate. |
Luca Becchetti; Andrea Clementi; Amos Korman; Francesco Pasquale; Luca Trevisan; Robin Vacus; |
| 5 | On A Voter Model with Context-Dependent Opinion Adoption Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose and study a context-dependent opinion spreading process on an arbitrary social graph, in which the probability that an agent abandons opinion a in favor of opinion b depends on both a and b. |
Luca Becchetti; Vincenzo Bonifaci; Emilio Cruciani; Francesco Pasquale; |
| 6 | Scalable Verification of Strategy Logic Through Three-Valued Abstraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a three-valued semantics for Strategy Logic upon which we define an abstraction method. |
Francesco Belardinelli; Angelo Ferrando; Wojciech Jamroga; Vadim Malvone; Aniello Murano; |
| 7 | Explainable Multi-Agent Reinforcement Learning for Temporal Queries Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work presents an approach for generating policy-level contrastive explanations for MARL to answer a temporal user query, which specifies a sequence of tasks completed by agents with possible cooperation. |
Kayla Boggess; Sarit Kraus; Lu Feng; |
| 8 | Efficient and Equitable Deployment of Mobile Vaccine Distribution Centers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Lack of access caused many people not getting vaccinated early, so states such as Virginia deployed mobile vaccination sites in order to distribute vaccines across the state. Here we study the problem of deciding where these facilities should be placed and moved over time in order to minimize the distance each person needs to travel in order to be vaccinated. |
Da Qi Chen; Ann Li; George Z. Li; Madhav Marathe; Aravind Srinivasan; Leonidas Tsepenekas; Anil Vullikanti; |
| 9 | Anticipatory Fictitious Play Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a simple modification of fictitious play which is a strict improvement over the original: it has the same theoretical worst-case convergence rate, is equally applicable in a machine learning context, and enjoys superior empirical performance. |
Alex Cloud; Albert Wang; Wesley Kerr; |
| 10 | Safe Multi-agent Learning Via Trapping Regions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we apply the concept of trapping regions, known from qualitative theory of dynamical systems, to create safety sets in the joint strategy space for decentralized learning. |
Aleksander Czechowski; Frans A. Oliehoek; |
| 11 | Multi-Agent Intention Recognition and Progression Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Previous work in multi-agent intention scheduling assumes a priori knowledge of the current goals of other agents. In this paper, we present a new approach to multi-agent intention scheduling in which an agent uses online goal recognition to identify the goals currently being pursued by other agents while acting in pursuit of its own goals. |
Michael Dann; Yuan Yao; Natasha Alechina; Brian Logan; Felipe Meneguzzi; John Thangarajah; |
| 12 | Controlling Neural Style Transfer with Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the first deep Reinforcement Learning (RL) based architecture that splits one-step style transfer into a step-wise process for the NST task. |
Chengming Feng; Jing Hu; Xin Wang; Shu Hu; Bin Zhu; Xi Wu; Hongtu Zhu; Siwei Lyu; |
| 13 | Cross-community Adapter Learning (CAL) to Understand The Evolving Meanings of Norm Violation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In such scenarios, a normative system that intends to regulate online interactions faces the challenge of continuously learning the meaning of norm violation as communities’ views evolve, either with changes in the understanding of what it means to violate a norm or with the emergence of new violation classes. To address this issue, we propose the Cross-community Adapter Learning (CAL) framework, which combines adapters and transformer-based models to learn the meaning of norm violations expressed as textual sentences. |
Thiago Freitas dos Santos; Stephen Cranefield; Bastin Tony Roy Savarimuthu; Nardine Osman; Marco Schorlemmer; |
| 14 | Learning in Multi-Memory Games Triggers Complex Dynamics Diverging from Nash Equilibrium Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This study extends two major learning algorithms in games, i.e., replicator dynamics and gradient ascent, into multi-memory games. |
Yuma Fujimoto; Kaito Ariu; Kenshi Abe; |
| 15 | Scalable Communication for Multi-Agent Reinforcement Learning Via Transformer-Based Email Mechanism Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, to tackle the scalability problem of MARL communication for partially-observed tasks, we propose a novel framework Transformer-based Email Mechanism (TEM). |
Xudong Guo; Daming Shi; Wenhui Fan; |
| 16 | Beyond Strict Competition: Approximate Convergence of Multi-agent Q-Learning Dynamics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nonetheless, many relevant competitive games do not satisfy the zero-sum assumption. Motivated by this, we study a smooth variant of Q-Learning, a popular reinforcement learning dynamics which balances the agents’ tendency to maximise their payoffs with their propensity to explore the state space. |
Aamal Hussain; Francesco Belardinelli; Georgios Piliouras; |
| 17 | Principal-Agent Boolean Games Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The principal’s challenge is to design a contract so that, firstly, the principal’s goal is achieved in some or all Nash equilibrium choices, and secondly, that the principal is able to verify that their goal is satisfied. In this paper, we formally define this problem and completely characterise the computational complexity of the most relevant decision problems associated with it. |
David Hyland; Julian Gutierrez; Michael Wooldridge; |
| 18 | Learning to Send Reinforcements: Coordinating Multi-Agent Dynamic Police Patrol Dispatching and Rescheduling Via Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We address the problem of coordinating multiple agents in a dynamic police patrol scheduling via a Reinforcement Learning (RL) approach. |
Waldy Joe; Hoong Chuin Lau; |
| 19 | Decentralized Anomaly Detection in Cooperative Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a decentralized scheme that allows agents to detect the abnormal behavior of one compromised agent. |
Kiarash Kazari; Ezzeldin Shereen; Gyorgy Dan; |
| 20 | Synthesizing Resilient Strategies for Infinite-Horizon Objectives in Multi-Agent Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We consider the problem of synthesizing resilient and stochastically stable strategies for systems of cooperating agents striving to minimize the expected time between consecutive visits to selected locations in a known environment. |
David Klaška; Antonín Kučera; Martin Kurečka; Vít Musil; Petr Novotný; Vojtěch Řehák; |
| 21 | In Which Graph Structures Can We Efficiently Find Temporally Disjoint Paths and Walks? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We extend Klobas et al.�s research by providing parameterized hardness results for very restricted cases, with a focus on structural parameters of the so-called underlying graph. |
Pascal Kunz; Hendrik Molter; Meirav Zehavi; |
| 22 | Probabilistic Planning with Prioritized Preferences Over Temporal Logic Objectives Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a systematic translation from the new language to a weighted deterministic finite automaton. |
Lening Li; Hazhar Rahmani; Jie Fu; |
| 23 | GPLight: Grouped Multi-agent Reinforcement Learning for Large-scale Traffic Signal Control Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This creates a difficult balance between accuracy and complexity, especially in large-scale CTL. To address this challenge, we propose a grouped MARL method named GPLight. |
Yilin Liu; Guiyang Luo; Quan Yuan; Jinglin Li; Lei Jin; Bo Chen; Rui Pan; |
| 24 | Deep Hierarchical Communication Graph in Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the static graph is not general for complex cooperative tasks, and the parallel message-passing update in the undirected graph with cycles cannot guarantee convergence. To solve this problem, we propose Deep Hierarchical Communication Graph (DHCG) to learn the dependency relationships between agents based on their messages. |
Zeyang Liu; Lipeng Wan; Xue Sui; Zhuoran Chen; Kewu Sun; Xuguang Lan; |
| 25 | The #DNN-Verification Problem: Counting Unsafe Inputs for Deep Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce the #DNN-Verification problem, which involves counting the number of input configurations of a DNN that result in a violation of a particular safety property. We analyze the complexity of this problem and propose a novel approach that returns the exact count of violations. |
Luca Marzari; Davide Corsi; Ferdinando Cicalese; Alessandro Farinelli; |
| 26 | Discounting in Strategy Logic Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we augment Strategy Logic with future discounting over a set of discounted functions D, denoted SL[D]. |
Munyque Mittelmann; Aniello Murano; Laurent Perrussel; |
| 27 | Why Rumors Spread Fast in Social Networks, and How to Stop It Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce four fundamental criteria which a countermeasure ideally should possess. |
Ahad N. Zehmakan; Charlotte Out; Sajjad Hesamipour Khelejan; |
| 28 | Improving LaCAM for Scalable Eventually Optimal Multi-Agent Pathfinding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study extends the recently-developed LaCAM algorithm for multi-agent pathfinding (MAPF). |
Keisuke Okumura; |
| 29 | Quick Multi-Robot Motion Planning By Combining Sampling and Search Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel algorithm to solve multi-robot motion planning (MRMP) rapidly, called Simultaneous Sampling-and-Search Planning (SSSP). |
Keisuke Okumura; Xavier Défago; |
| 30 | Asynchronous Communication Aware Multi-Agent Task Allocation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose FMC_ATA, an asynchronous version of FMC_TA, which is robust to message latency and message loss. |
Ben Rachmut; Sofia Amador Nelke; Roie Zivan; |
| 31 | Towards A Better Understanding of Learning with Multiagent Teams Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study why and under which conditions certain team structures promote effective learning for a population of individual learning agents. |
David Radke; Kate Larson; Tim Brecht; Kyle Tilbury; |
| 32 | Multi-Agent Systems with Quantitative Satisficing Goals Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the study of reactive systems, qualitative properties are usually easier to model and analyze than quantitative properties. |
Senthil Rajasekaran; Suguman Bansal; Moshe Y. Vardi; |
| 33 | Norm Deviation in Multiagent Systems: A Foundation for Responsible Autonomy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a conceptual foundation for norm deviation. |
Amika M. Singh; Munindar P. Singh; |
| 34 | CVTP3D: Cross-view Trajectory Prediction Using Shared 3D Queries for Autonomous Driving Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We presented a cross-view trajectory prediction method using shared 3D Queries (XVTP3D). |
Zijian Song; Huikun Bi; Ruisi Zhang; Tianlu Mao; Zhaoqi Wang; |
| 35 | Optimal Anytime Coalition Structure Generation Utilizing Compact Solution Space Representation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel method for coalition structure generation by introducing a compact and efficient representation of coalition structures. |
Redha Taguelmimt; Samir Aknine; Djamila Boukredera; Narayan Changder; Tuomas Sandholm; |
| 36 | Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present a systematic analysis of the choices made by intrinsically-motivated RL agents whose rewards are based on moral theories. |
Elizaveta Tennant; Stephen Hailes; Mirco Musolesi; |
| 37 | Exploration Via Joint Policy Diversity for Sparse-Reward Multi-Agent Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we show that introducing constrained joint policy diversity into a classical count-based method can significantly improve exploration when agents are parameterized by neural networks. |
Pei Xu; Junge Zhang; Kaiqi Huang; |
| 38 | Measuring Acoustics with Collaborative Multiple Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To the best of our knowledge, we present the very first problem formulation and solution to the task of collaborative environment acoustics measurements with multiple agents. |
Yinfeng Yu; Changan Chen; Lele Cao; Fangkai Yang; Fuchun Sun; |
| 39 | Dynamic Belief for Decentralized Multi-Agent Cooperative Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the decentralized training of multi-agent is of great difficulty due to non-stationarity, especially when other agents’ policies are also in learning during training. To overcome this, we propose to learn a dynamic policy belief for each agent to predict the current policies of other agents and accordingly condition the policy of its own. |
Yunpeng Zhai; Peixi Peng; Chen Su; Yonghong Tian; |
| 40 | Inducing Stackelberg Equilibrium Through Spatio-Temporal Sequential Decision-Making in Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In view of the advantages of Stackelberg equilibrium (SE) over Nash equilibrium, we construct a spatio-temporal sequential decision-making structure derived from the MG and propose an N-level policy model based on a conditional hypernetwork shared by all agents. |
Bin Zhang; Lijuan Li; Zhiwei Xu; Dapeng Li; Guoliang Fan; |
| 41 | Quantifying Harm Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We first present a quantitative definition of harm in a deterministic context involving a single individual, then we consider the issues involved in dealing with uncertainty regarding the context and going from a notion of harm for a single individual to a notion of societal harm, which involves aggregating the harm to individuals. |
Sander Beckers; Hana Chockler; Joseph Y. Halpern; |
| 42 | Analyzing Intentional Behavior in Autonomous Agents Under Uncertainty Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose analyzing the behavior of autonomous agents through a quantitative measure of the evidence of intentional behavior. |
Filip Cano Córdoba; Samuel Judson; Timos Antonopoulos; Katrine Bjørner; Nicholas Shoemaker; Scott J. Shapiro; Ruzica Piskac; Bettina Könighofer; |
| 43 | Choose Your Data Wisely: A Framework for Semantic Counterfactuals Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Instead, there are recent ideas that the notion of minimality in the context of counterfactuals should refer to the semantics of the data sample, and not to the feature space. In this work, we build on these ideas, and propose a framework that provides counterfactual explanations in terms of knowledge graphs. |
Edmund Dervakos; Konstantinos Thomas; Giorgos Filandrianos; Giorgos Stamou; |
| 44 | Group Fairness in Set Packing Problems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work we propose algorithms that take arbitrary proportionality vectors (i.e., policy-informed demands of how to prioritize different groups) and return a probabilistically fair solution with provable guarantees. |
Sharmila Duppala; Juan Luque; John Dickerson; Aravind Srinivasan; |
| 45 | Incentivizing Recourse Through Auditing in Strategic Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In particular, we propose auditing as a means to incentivize recourse actions over attribute manipulation, and characterize optimal audit policies for two types of principals, utility-maximizing and recourse-maximizing. |
Andrew Estornell; Yatong Chen; Sanmay Das; Yang Liu; Yevgeniy Vorobeychik; |
| 46 | Sampling Ex-Post Group-Fair Rankings Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose two algorithms to sample a random group-fair ranking from the distribution D mentioned above. |
Sruthi Gorantla; Amit Deshpande; Anand Louis; |
| 47 | Moral Planning Agents with LTL Values Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce different evaluation criteria for individual plans including an optimistic (risk-seeking) criterion, a pessimistic (risk-averse) one, and two criteria based on the use of anticipated responsibility. |
Umberto Grandi; Emiliano Lorini; Timothy Parker; |
| 48 | Advancing Post-Hoc Case-Based Explanation with Feature Highlighting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, for such post-hoc XAI methods dealing with images, there has been no attempt to improve their scope by using multiple clear feature parts of the images to explain the predictions while linking back to relevant cases in the training data, thus allowing for more comprehensive explanations that are faithful to the underlying model. Here, we address this gap by proposing two general algorithms (latent and superpixel-based) which can isolate multiple clear feature parts in a test image, and then connect them to the explanatory cases found in the training data, before testing their effectiveness in a carefully designed user study. |
Eoin M. Kenny; Eoin Delaney; Mark T. Keane; |
| 49 | Fairness Via Group Contribution Matching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although methods for mitigating unfairness are constantly proposed, little research has been conducted to understand how discrimination and bias develop during the standard training process. In this study, we propose analyzing the contribution of each subgroup (i.e., a group of data with the same sensitive attribute) in the training process to understand the cause of such bias development process. |
Tianlin Li; Zhiming Li; Anran Li; Mengnan Du; Aishan Liu; Qing Guo; Guozhu Meng; Yang Liu; |
| 50 | Negative Flux Aggregation to Estimate Feature Attributions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Inspired by the divergence theorem in vector analysis, we develop a novel Negative Flux Aggregation (NeFLAG) formulation and an efficient approximation algorithm to estimate attribution map. |
Xin Li; Deng Pan; Chengyin Li; Yao Qiang; Dongxiao Zhu; |
| 51 | Robust Reinforcement Learning Via Progressive Task Sequence Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the worst-case optimization either leads to overly conservative solutions or unstable training process, which further affects the policy robustness and generalization performance. In this paper, we tackle this problem from both formulation definition and algorithm design. |
Yike Li; Yunzhe Tian; Endong Tong; Wenjia Niu; Jiqiang Liu; |
| 52 | Towards Robust Gan-Generated Image Detection: A Multi-View Completion Representation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To overcome the issue, we propose a robust detection framework based on a novel multi-view image completion representation. |
Chi Liu; Tianqing Zhu; Sheng Shen; Wanlei Zhou; |
| 53 | Explanation-Guided Reward Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a framework for verifying and improving reward alignment using explanations and show how explanations can help detect misalignment and reveal failure cases in novel scenarios. |
Saaduddin Mahmud; Sandhya Saisubramanian; Shlomo Zilberstein; |
| 54 | Adversarial Behavior Exclusion for Safe Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We demonstrate the robustness of AdvEx-RL via comprehensive experiments in standard constrained Markov decision processes (CMDP) environments under 2 white-box action space perturbations as well as with changes in environment dynamics against 7 baselines. |
Md Asifur Rahman; Tongtong Liu; Sarra Alqahtani; |
| 55 | FEAMOE: Fair, Explainable and Adaptive Mixture of Experts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose FEAMOE, a novel "mixture-of-experts" inspired framework aimed at learning fairer, more interpretable models that can also rapidly adjust to drifts in both the accuracy and the fairness of a classifier. |
Shubham Sharma; Jette Henderson; Joydeep Ghosh; |
| 56 | SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In learning tasks, knowledge of the group attributes is essential to ensure non-discrimination, but in practice, these attributes may not be available due to legal and ethical requirements. To address this challenge, this paper studies a model that protects the privacy of individuals’ sensitive information while also allowing it to learn non-discriminatory predictors. |
Cuong Tran; Keyu Zhu; Ferdinando Fioretto; Pascal Van Hentenryck; |
| 57 | On The Fairness Impacts of Private Ensembles Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper explores whether the use of PATE can result in unfairness, and demonstrates that it can lead to accuracy disparities among groups of individuals. |
Cuong Tran; Ferdinando Fioretto; |
| 58 | Statistically Significant Concept-based Explanation of Image Classifiers Via Model Knockoffs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose a method using a deep learning model to learn the image concept and then using the knockoff sample to select the important concepts for prediction by controlling the False Discovery Rate (FDR) under a certain value. |
Kaiwen Xu; Kazuto Fukuchi; Youhei Akimoto; Jun Sakuma; |
| 59 | On Adversarial Robustness of Demographic Fairness in Face Attribute Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Therefore, this paper explores the adversarial robustness of demographic fairness in FAR applications from both attacking and defending perspectives. |
Huimin Zeng; Zhenrui Yue; Lanyu Shang; Yang Zhang; Dong Wang; |
| 60 | Towards Semantics- and Domain-Aware Adversarial Attacks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing word-level attacks have two major deficiencies: (1) They may change the semantics of the original sentence. (2) The generated adversarial sample can appear unnatural to humans due to the introduction of out-of-domain substitute words. In this paper, to address such drawbacks, we propose a semantics- and domain-aware word-level attack method. |
Jianping Zhang; Yung-Chieh Huang; Weibin Wu; Michael R. Lyu; |
| 61 | Tracking Different Ant Species: An Unsupervised Domain Adaptation Framework and A Dataset for Multi-object Tracking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a data-driven multi-object tracker that, for the first time, employs domain adaptation to achieve the required generalisation. |
Chamath Abeysinghe; Chris Reid; Hamid Rezatofighi; Bernd Meyer; |
| 62 | RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The key to tackling such a challenging task is to learn powerful multi-modal representations. Towards this, we propose a Relation and Sensitivity aware representation learning method (RaSa), including two novel tasks: Relation-Aware learning (RA) and Sensitivity-Aware learning (SA). |
Yang Bai; Min Cao; Daming Gao; Ziqiang Cao; Chen Chen; Zhenfeng Fan; Liqiang Nie; Min Zhang; |
| 63 | A Novel Learnable Interpolation Approach for Scale-Arbitrary Image Super-Resolution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To achieve a better balance between the reconstruction quality and parameter amounts, we proposes a learnable interpolation method that leverages the advantages of neural networks and interpolation methods to tackle the scale-arbitrary super-resolution task. |
Jiahao Chao; Zhou Zhou; Hongfan Gao; Jiali Gong; Zhenbing Zeng; Zhengfeng Yang; |
| 64 | MMPN: Multi-supervised Mask Protection Network for Pansharpening Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The deep learning-based pansharpening methods usually apply the convolution operation to extract features and only consider the similarity of gradient information between PAN and HRMS images, resulting in the problems of edge blur and spectral distortion in the fusion results. To solve this problem, a multi-supervised mask protection network (MMPN) is proposed to prevent spatial information from being damaged and overcome spectral distortion in the learning process. |
Changjie Chen; Yong Yang; Shuying Huang; Wei Tu; Weiguo Wan; Shengna Wei; |
| 65 | HDFormer: High-order Directed Transformer for 3D Human Pose Estimation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing methods primarily focus on pair-wise interaction of body joints, which is insufficient for scenarios involving overlapping joints and rapidly changing poses. To overcome these issues, we introduce a novel approach, the High-order Directed Transformer (HDFormer), which leverages high-order bone and joint relationships for improved pose estimation. |
Hanyuan Chen; Jun-Yan He; Wangmeng Xiang; Zhi-Qi Cheng; Wei Liu; Hanbing Liu; Bin Luo; Yifeng Geng; Xuansong Xie; |
| 66 | Fluid Dynamics-Inspired Network for Infrared Small Target Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The directional evolution of image pixels influenced by convolution, pooling and surrounding pixels is analogous to the movement of fluid elements constrained by surrounding variables ang particles. Inspired by this, we explore a novel research routine by abstracting the movement of pixels in the ISTD process as the flow of fluid in fluid dynamics (FD). |
Tianxiang Chen; Qi Chu; Bin Liu; Nenghai Yu; |
| 67 | CostFormer:Cost Transformer for Cost Aggregation in Multi-view Stereo Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, another problem may occur due to the quadratically growing computational complexity caused by Transformer, resulting in memory overflow and inference latency. In this paper, we overcome these limits with an efficient Transformer-based cost aggregation network, namely CostFormer. |
Weitao Chen; Hongbin Xu; Zhipeng Zhou; Yang Liu; Baigui Sun; Wenxiong Kang; Xuansong Xie; |
| 68 | Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, due to the high degree of structural locality in EM images, as well as the existence of considerable noise, many voxels contain little discriminative information, making MIM pretraining inefficient on the neuron segmentation task. To overcome this challenge, we propose a decision-based MIM that utilizes reinforcement learning (RL) to automatically search for optimal image masking ratio and masking strategy. |
Yinda Chen; Wei Huang; Shenglong Zhou; Qi Chen; Zhiwei Xiong; |
| 69 | Null-Space Diffusion Sampling for Zero-Shot Point Cloud Completion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel framework named Null-Space Diffusion Sampling (NSDS) to solve the point cloud completion task in a zero-shot manner. |
Xinhua Cheng; Nan Zhang; Jiwen Yu; Yinhuai Wang; Ge Li; Jian Zhang; |
| 70 | Robust Image Ordinal Regression with Controllable Image Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the issues of class imbalance and category overlap that are very common in ordinal regression were largely overlooked. As a result, the performance on minority categories is often unsatisfactory. In this paper, we propose a novel framework called CIG based on controllable image generation to directly tackle these two issues. |
Yi Cheng; Haochao Ying; Renjun Hu; Jinhong Wang; Wenhao Zheng; Xiao Zhang; Danny Chen; Jian Wu; |
| 71 | WiCo: Win-win Cooperation of Bottom-up and Top-down Referring Image Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Top-down methods are chiefly disturbed by Polar Negative (PN) errors owing to the lack of fine-grained cross-modal alignment. Bottom-up methods are mainly perturbed by Inferior Positive (IP) errors due to the lack of prior object information. Nevertheless, we discover that two types of methods are highly complementary for restraining respective weaknesses but the direct average combination leads to harmful interference. |
Zesen Cheng; Peng Jin; Hao Li; Kehan Li; Siheng Li; Xiangyang Ji; Chang Liu; Jie Chen; |
| 72 | Strip Attention for Image Restoration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Strip Attention Network (SANet) for image restoration to integrate information in a more efficient and effective manner. |
Yuning Cui; Yi Tao; Luoxi Jing; Alois Knoll; |
| 73 | RZCR: Zero-shot Character Recognition Via Radical-based Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a zero-shot character recognition framework via radical-based reasoning, called RZCR, to improve the recognition performance of few-sample character categories in the tail. |
Xiaolei Diao; Daqian Shi; Hao Tang; Qiang Shen; Yanzeng Li; Lei Wu; Hao Xu; |
| 74 | Decoupling with Entropy-based Equalization for Semi-Supervised Semantic Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the class imbalance problem makes the model favor the head classes with sufficient training samples, resulting in poor performance of the tail classes. To address this issue, we propose a Decoupled Semi-Supervise Semantic Segmentation (DeS4) framework based on the teacher-student model. |
Chuanghao Ding; Jianrong Zhang; Henghui Ding; Hongwei Zhao; Zhihui Wang; Tengfei Xing; Runbo Hu; |
| 75 | ICDA: Illumination-Coupled Domain Adaptation Framework for Unsupervised Nighttime Semantic Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the illumination-coupled domain adaptation framework(ICDA) to effectively avoid the illumination gap and mitigate the dataset gap by coupling daytime and nighttime images as a whole with semantic relevance. |
Chenghao Dong; Xuejing Kang; Anlong Ming; |
| 76 | DFVSR: Directional Frequency Video Super-Resolution Via Asymmetric and Enhancement Alignment Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most of these frequency-based methods mainly have three major limitations: 1) insufficient exploration of object motion information, 2) inadequate enhancement for high-fidelity regions, and 3) loss of spatial information during convolution. In this paper, we propose a novel network, Directional Frequency Video Super-Resolution (DFVSR), to address these limitations. |
Shuting Dong; Feng Lu; Zhe Wu; Chun Yuan; |
| 77 | Timestamp-Supervised Action Segmentation from The Perspective of Clustering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, these methods suffer from incorrect pseudo-labels, especially for the semantically unclear frames in the transition region between two consecutive actions, which we call ambiguous intervals. To address this issue, we propose a novel framework from the perspective of clustering, which includes the following two parts. |
Dazhao Du; Enhan Li; Lingyu Si; Fanjiang Xu; Fuchun Sun; |
| 78 | LION: Label Disambiguation for Semi-supervised Facial Expression Recognition with Progressive Negative Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel SS-DFER method, including a Label DIsambiguation module and a PrOgressive Negative Learning module, namely LION, to simultaneously address both problems. |
Zhongjing Du; Xu Jiang; Peng Wang; Qizheng Zhou; Xi Wu; Jiliu Zhou; Yan Wang; |
| 79 | Improve Video Representation with Temporal Adversarial Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce Temporal Adversarial Augmentation (TA), a novel video augmentation technique that utilizes temporal attention. |
Jinhao Duan; Quanfu Fan; Hao Cheng; Xiaoshuang Shi; Kaidi Xu; |
| 80 | RFENet: Towards Reciprocal Feature Evolution for Glass Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The transparent property makes it difficult to be distinguished from background, while the tiny separation boundary further impedes the acquisition of their exact contour. In this paper, by revealing the key co-evolution demand of semantic and boundary learning, we propose a Selective Mutual Evolution (SME) module to enable the reciprocal feature learning between them. |
Ke Fan; Changan Wang; Yabiao Wang; Chengjie Wang; Ran Yi; Lizhuang Ma; |
| 81 | Reconstruction-Aware Prior Distillation for Semi-supervised Point Cloud Completion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes RaPD, a novel semi-supervised point cloud completion method that reduces the need for paired datasets. |
Zhaoxin Fan; Yulin He; Zhicheng Wang; Kejian Wu; Hongyan Liu; Jun He; |
| 82 | Sub-Band Based Attention for Robust Polyp Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This article proposes a novel spectral domain based solution to the challenging polyp segmentation. |
Xianyong Fang; Yuqing Shi; Qingqing Guo; Linbo Wang; Zhengyi Liu; |
| 83 | Incorporating Unlikely Negative Cues for Distinctive Image Captioning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a approach to improve image captioning by integrating negative knowledge that focuses on preventing the model from producing undesirable generic descriptions while addressing previous limitations. |
Zhengcong Fei; Junshi Huang; |
| 84 | BPNet: Bézier Primitive Segmentation on 3D Point Clouds Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes BPNet, a novel end-to-end deep learning framework to learn Bézier primitive segmentation on 3D point clouds. |
Rao Fu; Cheng Wen; Qian Li; Xiao Xiao; Pierre Alliez; |
| 85 | Contrastive Learning for Sign Language Recognition and Translation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The other one is the exposure bias problem which leads to the accumulation of translation errors during inference in Sign Language Translation (SLT). In this paper, we tackle these issues by introducing contrast learning, aiming to enhance both visual-level feature representation and semantic-level error tolerance. |
Shiwei Gan; Yafeng Yin; Zhiwei Jiang; Kang Xia; Lei Xie; Sanglu Lu; |
| 86 | LISSNAS: Locality-based Iterative Search Space Shrinkage for Neural Architecture Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose LISSNAS, an automated algorithm that shrinks a large space into a diverse, small search space with SOTA search performance. |
Bhavna Gopal; Arjun Sridhar; Tunhou Zhang; Yiran Chen; |
| 87 | Towards Robust Scene Text Image Super-resolution Via Explicit Location Enhancement Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To model the location of characters effectively, we propose the location enhancement module to extract character region features based on the attention map sequence. |
Hang Guo; Tao Dai; Guanghao Meng; Shu-Tao Xia; |
| 88 | Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explore how the 2D modality can benefit 3D masked autoencoding, and propose Joint-MAE, a 2D-3D joint MAE framework for self-supervised 3D point cloud pre-training. |
Ziyu Guo; Renrui Zhang; Longtian Qiu; Xianzhi Li; Pheng-Ann Heng; |
| 89 | SS-BSN: Attentive Blind-Spot Network for Self-Supervised Denoising with Nonlocal Self-Similarity Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents self-similarity attention (SS-Attention), a novel self-attention module that can capture nonlocal self-similarities to solve the problem. |
Young-Joo Han; Ha-Jin Yu; |
| 90 | DAMO-StreamNet: Optimizing Streaming Perception in Autonomous Driving Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This research introduces DAMO-StreamNet, a novel framework that merges the cutting-edge elements of the YOLO series with a detailed examination of spatial and temporal perception techniques. |
Jun-Yan He; Zhi-Qi Cheng; Chenyang Li; Wangmeng Xiang; Binghui Chen; Bin Luo; Yifeng Geng; Xuansong Xie; |
| 91 | Independent Feature Decomposition and Instance Alignment for Unsupervised Domain Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In practice, however, the obtained features is often mixed with domain-specific information which causes performance degradation. To overcome this fundamental limitation, this article presents a novel independent feature decomposition and instance alignment method (IndUDA in short). |
Qichen He; Siying Xiao; Mao Ye; Xiatian Zhu; Ferrante Neri; Dongde Hou; |
| 92 | MILD: Modeling The Instance Learning Dynamics for Learning with Noisy Labels Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose an iterative selection approach based on the Weibull mixture model, which identifies clean data by considering the overall learning dynamics of each data instance. |
Chuanyang Hu; Shipeng Yan; Zhitong Gao; Xuming He; |
| 93 | Diagram Visual Grounding: Learning to See with Gestalt-Perceptual Attention Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The phenomenon brings challenges to the diagram visual grounding. To solve the above issues, we propose a gestalt-perceptual attention model to align the diagram objects and language expressions. |
Xin Hu; Lingling Zhang; Jun Liu; Xinyu Zhang; Wenjun Wu; Qianying Wang; |
| 94 | Dual Video Summarization: From Frames to Captions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a dual video summarization framework and verify it in the context of video captioning. |
Zhenzhen Hu; Zhenshan Wang; Zijie Song; Richang Hong; |
| 95 | Part Aware Contrastive Learning for Self-Supervised Action Recognition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes an attention-based contrastive learning framework for skeleton representation learning, called SkeAttnCLR, which integrates local similarity and global features for skeleton-based action representations. |
Yilei Hua; Wenhan Wu; Ce Zheng; Aidong Lu; Mengyuan Liu; Chen Chen; Shiqian Wu; |
| 96 | Orion: Online Backdoor Sample Detection Via Evolution Deviance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel detection framework, dubbed Orion (online backdoor sample detection via evolution deviance). |
Huayang Huang; Qian Wang; Xueluan Gong; Tao Wang; |
| 97 | Discovering Sounding Objects By Audio Queries for Audio Visual Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The previous method applies multi-frame cross-modal attention to conduct pixel-level interactions between audio features and visual features of multiple frames simultaneously, which is both redundant and implicit. In this paper, we propose an Audio-Queried Transformer architecture, AQFormer, where we define a set of object queries conditioned on audio information and associate each of them to particular sounding objects. |
Shaofei Huang; Han Li; Yuqing Wang; Hongji Zhu; Jiao Dai; Jizhong Han; Wenge Rong; Si Liu; |
| 98 | Semi-supervised Domain Adaptation Via Prototype-based Multi-level Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Many existing methods ignore the benefits of making full use of the labeled target samples from multi-level. To make better use of this additional data, we propose a novel Prototype-based Multi-level Learning (ProML) framework to better tap the potential of labeled target samples. |
Xinyang Huang; Chuang Zhu; Wenkai Chen; |
| 99 | Enriching Phrases with Coupled Pixel and Object Contexts for Panoptic Narrative Grounding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To realize more comprehensive visual-linguistic interaction, we propose to enrich phrases with coupled pixel and object contexts by designing a Phrase-Pixel-Object Transformer Decoder (PPO-TD), where both fine-grained part details and coarse-grained entity clues are aggregated to phrase features. |
Tianrui Hui; Zihan Ding; Junshi Huang; Xiaoming Wei; Xiaolin Wei; Jiao Dai; Jizhong Han; Si Liu; |
| 100 | StackFLOW: Monocular Human-Object Reconstruction By Stacked Normalizing Flow with Offset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose to use the Human-Object Offset between anchors which are densely sampled from the surface of human mesh and object mesh to represent human-object spatial relation. |
Chaofan Huo; Ye Shi; Yuexin Ma; Lan Xu; Jingyi Yu; Jingya Wang; |
| 101 | GeNAS: Neural Architecture Search with Better Generalization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we investigate a new neural architecture search measure for excavating architectures with better generalization. |
Joonhyun Jeong; Joonsang Yu; Geondo Park; Dongyoon Han; YoungJoon Yoo; |
| 102 | Guided Patch-Grouping Wavelet Transformer with Spatial Congruence for Ultra-High Resolution Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, GPWFormer is a Transformer (T)-CNN (C) mutual leaning framework, where T takes the whole UHR image as input and harvests both local details and fine-grained long-range contextual dependencies, while C takes downsampled image as input for learning the category-wise deep context. |
Deyi Ji; Feng Zhao; Hongtao Lu; |
| 103 | ContrastMotion: Self-supervised Scene Motion Learning for Large-Scale LiDAR Point Clouds Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel self-supervised motion estimator for LiDAR-based autonomous driving via BEV representation. |
Xiangze Jia; Hui Zhou; Xinge Zhu; Yandong Guo; Ji Zhang; Yuexin Ma; |
| 104 | Text-Video Retrieval with Disentangled Conceptualization and Set-to-Set Alignment Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose the Disentangled Conceptualization and Set-to-set Alignment (DiCoSA) to simulate the conceptualizing and reasoning process of human beings. |
Peng Jin; Hao Li; Zesen Cheng; Jinfa Huang; Zhennan Wang; Li Yuan; Chang Liu; Jie Chen; |
| 105 | Physics-Guided Human Motion Capture with Pose Probability Modeling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, due to the depth ambiguity, monocular motion capture inevitably suffers from noises, and the noisy reference often leads to failure for physics-based tracking. To address the obstacles, our key-idea is to employ physics as denoising guidance in the reverse diffusion process to reconstruct physically plausible human motion from a modeled pose probability distribution. |
Jingyi Ju; Buzhen Huang; Chen Zhu; Zhihao Li; Yangang Wang; |
| 106 | SWAT: Spatial Structure Within and Among Tokens Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we argue that models can have significant gains when spatial structure is preserved during tokenization, and is explicitly used during the mixing stage. |
Kumara Kahatapitiya; Michael S. Ryoo; |
| 107 | Spatially Constrained Adversarial Attack Detection and Localization in The Representation Space of Optical Flow Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these flow networks have recently been shown to be vulnerable to patch-based adversarial attacks, which poses security risks in real-world applications, such as self-driving cars and robotics. We propose SADL, a Spatially constrained adversarial Attack Detection and Localization framework, to detect and localize these patch-based attack without requiring a dedicated training. |
Hannah Kim; Celia Cintas; Girmaw Abebe Tadesse; Skyler Speakman; |
| 108 | IMF: Integrating Matched Features Using Attentive Logit in Knowledge Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, to address the student model’s limitation, we propose a novel flexible KD framework, Integrating Matched Features using Attentive Logit in Knowledge Distillation (IMF). |
Jeongho Kim; Hanbeen Lee; Simon S. Woo; |
| 109 | Character As Pixels: A Controllable Prompt Adversarial Attacking Framework for Black-Box Text Guided Image Generation Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study a controllable prompt adversarial attacking problem for text guided image generation (Text2Image) models in the black-box scenario, where the goal is to attack specific visual subjects (e.g., changing a brown dog to white) in a generated image by slightly, if not imperceptibly, perturbing the characters of the driven prompt (e.g., “brown” to “br0wn”). |
Ziyi Kou; Shichao Pei; Yijun Tian; Xiangliang Zhang; |
| 110 | Clustered-patch Element Connection for Few-shot Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: What is worse, these mismatches result in unreliable similarity confidences, and complex dense connection exacerbates the problem. According to this, we propose a novel Clustered-patch Element Connection (CEC) layer to correct the mismatch problem. |
Jinxiang Lai; Siqian Yang; Junhong Zhou; Wenlong Wu; Xiaochen Chen; Jun Liu; Bin-Bin Gao; Chengjie Wang; |
| 111 | RaMLP: Vision MLP Via Region-aware Mixing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent methods tried to address it but brought two new problems, long-range dependencies or important visual cues are ignored. This paper presents a new MLP-based architecture, Region-aware MLP (RaMLP), to satisfy various vision tasks and address the above three problems. |
Shenqi Lai; Xi Du; Jia Guo; Kaipeng Zhang; |
| 112 | Deep Unfolding Convolutional Dictionary Model for Multi-Contrast MRI Super-resolution and Reconstruction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a multi-contrast convolutional dictionary (MC-CDic) model under the guidance of the optimization algorithm with a well-designed data fidelity term. |
Pengcheng Lei; Faming Fang; Guixu Zhang; Ming Xu; |
| 113 | CiT-Net: Convolutional Neural Networks Hand in Hand with Vision Transformers for Medical Image Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address these challenges, we propose a novel hybrid architecture of convolutional neural networks hand in hand with vision Transformers (CiT-Net) for medical image segmentation. |
Tao Lei; Rui Sun; Xuan Wang; Yingbo Wang; Xi He; Asoke Nandi; |
| 114 | WBFlow: Few-shot White Balance for SRGB Images Via Reversible Neural Flows Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the network named WBFlow that not only performs superior white balance for sRGB images but also generalizes well to multiple cameras. |
Chunxiao Li; Xuejing Kang; Anlong Ming; |
| 115 | Learning Attention from Attention: Efficient Self-Refinement Transformer for Face Super-Resolution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: But it also limits the capability of recovering high-frequency details when flat areas dominate the local searching window. To improve the above issues, we propose a novel self-refinement mechanism which could adaptively achieve texture-aware reconstruction in a coarse-to-fine procedure. |
Guanxin Li; Jingang Shi; Yuan Zong; Fei Wang; Tian Wang; Yihong Gong; |
| 116 | TG-VQA: Ternary Game of Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we innovatively resort to game theory, which can simulate complicated relationships among multiple players with specific interaction strategies, e.g., video, question, and answer as ternary players, to achieve fine-grained alignment for VideoQA task. |
Hao Li; Peng Jin; Zesen Cheng; Songyang Zhang; Kai Chen; Zhennan Wang; Chang Liu; Jie Chen; |
| 117 | Contact2Grasp: 3D Grasp Synthesis Via Hand-Object Contact Constraint Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing works tackle the problem by learning a direct mapping from objects to the distributions of grasping poses. However, because the physical contact is sensitive to small changes in pose, the high-nonlinear mapping between 3D object representation to valid poses is considerably non-smooth, leading to poor generation efficiency and restricted generality. To tackle the challenge, we introduce an intermediate variable for grasp contact areas to constrain the grasp generation; in other words, we factorize the mapping into two sequential stages by assuming that grasping poses are fully constrained given contact maps: 1) we first learn contact map distributions to generate the potential contact maps for grasps; 2) then learn a mapping from the contact maps to the grasping poses. |
Haoming Li; Xinzhuo Lin; Yang Zhou; Xiang Li; Yuchi Huo; Jiming Chen; Qi Ye; |
| 118 | ALL-E: Aesthetics-guided Low-light Image Enhancement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new paradigm, i.e., aesthetics-guided low-light image enhancement (ALL-E), which introduces aesthetic preferences to LLE and motivates training in a reinforcement learning framework with an aesthetic reward. |
Ling Li; Dong Liang; Yuanhang Gao; Sheng-Jun Huang; Songcan Chen; |
| 119 | Local-Global Transformer Enhanced Unfolding Network for Pan-sharpening Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: For one thing, the universally adopted black box principle limits the model interpretability. For another thing, existing DL-based methods fail to efficiently capture local and global dependencies at the same time, inevitably limiting the overall performance. To address these mentioned issues, we first formulate the degradation process of the high-resolution multispectral (HrMS) image as a unified variational optimization problem, and alternately solve its data and prior subproblems by the designed iterative proximal gradient descent (PGD) algorithm. |
Mingsong Li; Yikun Liu; Tao Xiao; Yuwen Huang; Gongping Yang; |
| 120 | PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird’s-Eye View Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing approaches for BEV instance prediction from surround cameras rely on a multi-task auto-regressive setup coupled with complex post-processing to predict future instances in a spatio-temporally consistent manner. In this paper, we depart from this paradigm and propose an efficient novel end-to-end framework named PowerBEV, which differs in several design choices aimed at reducing the inherent redundancy in previous methods. |
Peizheng Li; Shuxiao Ding; Xieyuanli Chen; Niklas Hanselmann; Marius Cordts; Juergen Gall; |
| 121 | On Efficient Transformer-Based Image Pre-training for Low-Level Vision Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we tailor transformer-based pre-training regimes that boost various low-level tasks. |
Wenbo Li; Xin Lu; Shengju Qian; Jiangbo Lu; |
| 122 | Compositional Zero-Shot Artistic Font Synthesis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, due to less exploration in style disentanglement, it is difficult for existing methods to envision a kind of unseen style (glyph-effect) compositions of artistic font, and thus can only learn the seen style compositions. To solve this problem, we propose a novel compositional zero-shot artistic font synthesis gan (CAFS-GAN), which allows the synthesis of unseen style compositions by exploring the visual independence and joint compatibility of encoding semantics between glyph and effect. |
Xiang Li; Lei Wu; Changshuo Wang; Lei Meng; Xiangxu Meng; |
| 123 | VS-Boost: Boosting Visual-Semantic Association for Generalized Zero-Shot Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on the generation method and relational metric learning, we proposed a novel GZSL method, termed VS-Boost, which can effectively boost the association between vision and semantics. |
Xiaofan Li; Yachao Zhang; Shiran Bian; Yanyun Qu; Yuan Xie; Zhongchao Shi; Jianping Fan; |
| 124 | Locate, Refine and Restore: A Progressive Enhancement Network for Camouflaged Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Progressive Enhancement Network (PENet) for COD by imitating the human visual detection system, which follows a three-stage detection process: locate objects, refine textures and restore boundary. |
Xiaofei Li; Jiaxin Yang; Shuohao Li; Jun Lei; Jun Zhang; Dong Chen; |
| 125 | SGAT4PASS: Spherical Geometry-Aware Transformer for PAnoramic Semantic Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To be more robust to 3D disturbance, we propose our Spherical Geometry-Aware Transformer for PAnoramic Semantic Segmentation (SGAT4PASS), considering 3D spherical geometry knowledge. |
Xuewei Li; Tao Wu; Zhongang Qi; Gaoang Wang; Ying Shan; Xi Li; |
| 126 | Image Composition with Depth Registration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper addresses the challenge by presenting a depth registration method for merging the source contents seamlessly into the 3D space that the target image represents. |
Zan Li; Wencheng Wang; Fei Hou; |
| 127 | Complete Instances Mining for Weakly Supervised Instance Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: For example, we feed a picture of a dog and proposals into the network and expect to output only one proposal containing a dog, but the network outputs multiple proposals. To address this problem, we propose a novel approach for WSIS that focuses on the online refinement of complete instances through the use of MaskIoU heads to predict the integrity scores of proposals and a Complete Instances Mining (CIM) strategy to explicitly model the redundant segmentation problem and generate refined pseudo labels. |
Zecheng Li; Zening Zeng; Yuqi Liang; Jin-Gang Yu; |
| 128 | Analyzing and Combating Attribute Bias for Face Restoration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, we argue that FR should consider not only image quality as in existing works but also attribute bias. |
Zelin Li; Dan Zeng; Xiao Yan; Qiaomu Shen; Bo Tang; |
| 129 | A Large-Scale Film Style Dataset for Learning Multi-frequency Driven Film Enhancement Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Inspired by the features of FilmSet images, we propose a novel framework called FilmNet based on Laplacian Pyramid for stylizing images across frequency bands and achieving film style outcomes. |
Zinuo Li; Xuhang Chen; Shuqiang Wang; Chi-Man Pun; |
| 130 | U-Match: Two-view Correspondence Learning with Hierarchy-aware Local Context Aggregation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent advances have devised various local context extractors whereas typically adopting explicit neighborhood relation modeling that is restricted and inflexible. To address this issue, we introduce U-Match, an attentional graph neural network that has the flexibility to enable implicit local context awareness at multiple levels. |
Zizhuo Li; Shihua Zhang; Jiayi Ma; |
| 131 | GTR: A Grafting-Then-Reassembling Framework for Dynamic Scene Graph Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a Grafting-Then-Reassembling framework (GTR), which explicitly extracts intra-frame spatial information and inter-frame temporal information in two separate stages to decouple spatio-temporal contextual information. |
Jiafeng Liang; Yuxin Wang; Zekun Wang; Ming Liu; Ruiji Fu; Zhongyuan Wang; Bing Qin; |
| 132 | Low-Confidence Samples Mining for Semi-supervised Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Low-confidence Samples Mining (LSM) method to utilize low confidence pseudo labels efficiently. |
Guandu Liu; Fangyuan Zhang; Tianxiang Pan; Jun-Hai Yong; Bin Wang; |
| 133 | Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Decision-based Black-box Attack framework with Gradient Priors (DBA-GP), which seamlessly integrates the data-dependent gradient prior and time-dependent prior into the gradient estimation procedure. |
Han Liu; Xingshuo Huang; Xiaotong Zhang; Qimai Li; Fenglong Ma; Wei Wang; Hongyang Chen; Hong Yu; Xianchao Zhang; |
| 134 | APR: Online Distant Point Cloud Registration Through Aggregated Point Cloud Reconstruction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel feature extraction framework, called APR, for online distant point cloud registration. |
Quan Liu; Yunsong Zhou; Hongzi Zhu; Shan Chang; Minyi Guo; |
| 135 | Cross-Domain Facial Expression Recognition Via Disentangling Identity Representation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, existing methods suffer from the interference of identity information, thus limiting the discriminative ability of the expression features. We exploit the idea of domain generalization (DG) and propose a representation disentanglement model to address the above problems. |
Tong Liu; Jing Li; Jia Wu; Lefei Zhang; Shanshan Zhao; Jun Chang; Jun Wan; |
| 136 | Adaptive Sparse ViT: Towards Learnable Adaptive Token Pruning By Fully Exploiting Self-Attention Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose an adaptive sparse token pruning framework with a minimal cost. |
Xiangcheng Liu; Tianyi Wu; Guodong Guo; |
| 137 | Sph2Pob: Boosting Object Detection on Spherical Images with Planar Oriented Boxes Methods Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Taking the two problems into account, we propose a new sphere-plane boxes transform, called Sph2Pob. |
Xinyuan Liu; Hang Xu; Bin Chen; Qiang Zhao; Yike Ma; Chenggang Yan; Feng Dai; |
| 138 | Bi-level Dynamic Learning for Jointly Multi-modality Image Fusion and Beyond Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, early efforts always consider boosting a single task unilaterally and neglecting others, seldom investigating their underlying connections for joint promotion. To overcome these limitations, we establish the hierarchical dual tasks-driven deep model to bridge these tasks. |
Zhu Liu; Jinyuan Liu; Guanyao Wu; Long Ma; Xin Fan; Risheng Liu; |
| 139 | Non-Lambertian Multispectral Photometric Stereo Via Spectral Reflectance Decomposition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a deep neural network named NeuralMPS to solve the MPS problem under non-Lambertian spectral reflectances. |
Jipeng Lv; Heng Guo; Guanying Chen; Jinxiu Liang; Boxin Shi; |
| 140 | Semantic-Aware Generation of Multi-View Portrait Drawings Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Besides, portrait drawings usually present little 3D information and suffer from insufficient training data. To combat this challenge, in this paper, we propose a Semantic-Aware GEnerator (SAGE) for synthesizing multi-view portrait drawings. |
Biao Ma; Fei Gao; Chang Jiang; Nannan Wang; Gang Xu; |
| 141 | Invertible Residual Neural Networks with Conditional Injector and Interpolator for Point Cloud Upsampling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel invertible residual neural network for point cloud upsampling, called PU-INN, which allows unconstrained architectures to learn more expressive feature transformations. |
Aihua Mao; Yaqi Duan; Yu-Hui Wen; Zihui Du; Hongmin Cai; Yong-Jin Liu; |
| 142 | Dual Relation Knowledge Distillation for Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, it is still a challenging topic to apply knowledge distillation to detection tasks. There are two key points resulting in poor distillation performance for detection tasks. One is the serious imbalance between foreground and background features, another one is that small object lacks enough feature representation. To solve the above issues, we propose a new distillation method named dual relation knowledge distillation (DRKD), including pixel-wise relation distillation and instance-wise relation distillation. |
Zhen-Liang Ni; Fukui Yang; Shengzhao Wen; Gang Zhang; |
| 143 | OSP2B: One-Stage Point-to-Box Network for 3D Siamese Tracking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, such a network suffers from tedious hyper-parameter tuning and task misalignment, limiting the tracking performance. Towards these concerns, we propose a simple yet effective one-stage point-to-box network for point cloud-based 3D single object tracking. |
Jiahao Nie; Zhiwei He; Yuxiang Yang; Zhengyi Bao; Mingyu Gao; Jing Zhang; |
| 144 | SLViT: Scale-Wise Language-Guided Vision Transformer for Referring Image Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In addition, they employ sequential structures and hence lack multi-scale information interaction. To address these limitations, we propose a Scale-Wise Language-Guided Vision Transformer (SLViT) with two appealing designs: (1) Language-Guided Multi-Scale Fusion Attention, a novel attention mechanism module for extracting rich local visual information and modeling global visual-linguistic relationships in an integrated manner. |
Shuyi Ouyang; Hongyi Wang; Shiao Xie; Ziwei Niu; Ruofeng Tong; Yen-Wei Chen; Lanfen Lin; |
| 145 | Active Visual Exploration Based on Attention-Map Entropy Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Active visual exploration addresses the issue of limited sensor capabilities in real-world scenarios, where successive observations are actively chosen based on the environment. To tackle this problem, we introduce a new technique called Attention-Map Entropy (AME). |
Adam Pardyl; Grzegorz Rypeść; Grzegorz Kurzejamski; Bartosz Zieliński; Tomasz Trzciński; |
| 146 | Answer Mining from A Pool of Images: Towards Retrieval-Based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Towards solving the RETVQA task, we propose a unified Multi Image BART (MI-BART) that takes a question and retrieved images using our relevance encoder for free-form fluent answer generation. |
Abhirama Subramanyam Penamakuri; Manish Gupta; Mithun Das Gupta; Anand Mishra; |
| 147 | Contour-based Interactive Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, using clicks may require too many user interactions, especially when selecting small ob- jects, minor parts of an object, or a group of ob- jects of the same type. In this paper, we consider such a natural form of user interaction as a loose contour, and introduce a contour-based IS method. |
Polina Popenova; Danil Galeev; Anna Vorontsova; Anton Konushin; |
| 148 | XFormer: Fast and Accurate Monocular 3D Body Capture Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present XFormer, a novel human mesh and motion capture method that achieves real-time performance on consumer CPUs given only monocular images as input. |
Lihui Qian; Xintong Han; Faqiang Wang; Hongyu Liu; Haoye Dong; Zhiwen Li; Huawei Wei; Zhe Lin; Cheng-Bin Jin; |
| 149 | ViT-P3DE∗: Vision Transformer Based Multi-Camera Instance Association with Pseudo 3D Position Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by Vision Transformer (ViT), we first develop a pure ViT-based framework for robust feature extraction through self-attention and residual connection. We then propose two novel methods to achieve robust feature learning. |
Minseok Seo; Hyuk-Jae Lee; Xuan Truong Nguyen; |
| 150 | Teaching What You Should Teach: A Data-Based Distillation Method Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This gives the student the best assistance in making up for his (or her) weaknesses and becoming a good one overall. Enlightened by this, we introduce the "Teaching what you Should Teach" strategy into a knowledge distillation framework, and propose a data-based distillation method named "TST" that searches for desirable augmented samples to assist in distilling more efficiently and rationally. |
Shitong Shao; Huanran Chen; Zhen Huang; Linrui Gong; Shuai Wang; Xinxiao Wu; |
| 151 | Learning Prototype Classifiers for Long-Tailed Recognition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we show that learning prototype classifiers addresses the biased softmax problem in LTR. |
Saurabh Sharma; Yongqin Xian; Ning Yu; Ambuj Singh; |
| 152 | Divide Rows and Conquer Cells: Towards Structure Recognition for Large Tables Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel image-to-text based TSR method that relieves error accumulation problems and improves performance noticeably. |
Huawen Shen; Xiang Gao; Jin Wei; Liang Qiao; Yu Zhou; Qiang Li; Zhanzhan Cheng; |
| 153 | Data Level Lottery Ticket Hypothesis for Vision Transformers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We use a ticket selector to generate the winning tickets based on the informativeness of patches for various types of ViT, including DeiT, LV-ViT, and Swin Transformers. |
Xuan Shen; Zhenglun Kong; Minghai Qin; Peiyan Dong; Geng Yuan; Xin Meng; Hao Tang; Xiaolong Ma; Yanzhi Wang; |
| 154 | Discrepancy-Guided Reconstruction Learning for Image Forgery Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel image forgery detection paradigm for boosting the model learning capacity on both forgery-sensitive and genuine compact visual patterns. |
Zenan Shi; Haipeng Chen; Long Chen; Dong Zhang; |
| 155 | Depth-Relative Self Attention for Monocular Depth Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel depth estimation model named RElative Depth Transformer (RED-T) that uses relative depth as guidance in self-attention. |
Kyuhong Shim; Jiyoung Kim; Gusang Lee; Byonghyo Shim; |
| 156 | Acoustic NLOS Imaging with Cross Modal Knowledge Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite recent developments in the field, existing methods still have limitations such as sensitivity to noise in a physical model and difficulty in reconstructing unseen objects in a deep learning model. To address these limitations, we propose a novel cross-modal knowledge distillation (CMKD) approach for acoustic NLOS imaging. |
Ui-Hyeon Shin; Seungwoo Jang; Kwangsu Kim; |
| 157 | VGOS: Voxel Grid Optimization for View Synthesis from Sparse Inputs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Although using the voxel grids to represent the radiance field can significantly accelerate the optimization process, we observe that for sparse inputs, the voxel grids are more prone to overfitting to the training views and will have holes and floaters, which leads to artifacts. In this paper, we propose VGOS, an approach for fast (3-5 minutes) radiance field reconstruction from sparse inputs (3-10 views) to address these issues. |
Jiakai Sun; Zhanjie Zhang; Jiafu Chen; Guangyuan Li; Boyan Ji; Lei Zhao; Wei Xing; |
| 158 | Appearance Prompt Vision Transformer for Connectome Reconstruction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a coherent and unified Appearance Prompt Vision Transformer (APViT) to integrate affinity and metric learning to exploit the complementarity by learning long-range spatial dependencies. |
Rui Sun; Naisong Luo; Yuwen Pan; Huayu Mai; Tianzhu Zhang; Zhiwei Xiong; Feng Wu; |
| 159 | Domain-Adaptive Self-Supervised Face & Body Detection in Drawings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we show how self-supervised learning, based on a teacher-student network with a modified student network update design, can be used to build face and body detectors. |
Barış Batuhan Topal; Deniz Yuret; Tevfik Metin Sezgin; |
| 160 | Shaken, and Stirred: Long-Range Dependencies Enable Robust Outlier Detection with PixelCNN++ Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose two families of bijective transformations — “stirring” and “shaking” — which ameliorate low-level biases and isolate the contribution of long-range dependencies to PixelCNN++ likelihoods. |
Barath Mohan Umapathi; Kushal Chauhan; Pradeep Shenoy; Devarajan Sridharan; |
| 161 | Temporal Constrained Feasible Subspace Learning for Human Pose Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we consider the temporal constrained feasible solutions for human pose forecasting, where the predicted poses of input historical poses are guaranteed to obey the temporal constraints strictly in the inference stage. |
Gaoang Wang; Mingli Song; |
| 162 | Learning Calibrated Uncertainties for Domain Shift: A Distributionally Robust Learning Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a framework for learning calibrated uncertainties under domain shifts, considering the case where the source (training) distribution differs from the target (test) distribution. |
Haoxuan Wang; Zhiding Yu; Yisong Yue; Animashree Anandkumar; Anqi Liu; Junchi Yan; |
| 163 | Hierarchical Prompt Learning for Compositional Zero-Shot Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Compositional Zero-Shot Learning (CZSL) aims to imitate the powerful generalization ability of human beings to recognize novel compositions of known primitive concepts that correspond to a state and an object, e.g., purple apple. To fully capture the intra- and inter-class correlations between compositional concepts, in this paper, we propose to learn them in a hierarchical manner. |
Henan Wang; Muli Yang; Kun Wei; Cheng Deng; |
| 164 | A Dual Semantic-Aware Recurrent Global-Adaptive Network for Vision-and-Language Navigation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While significant advancements have been achieved recently, there are still two broad limitations: (1) The explicit information mining for significant guiding semantics concealed in both vision and language is still under-explored; (2) The previously structured map method provides the average historical appearance of visited nodes, while it ignores distinctive contributions of various images and potent information retention in the reasoning process. This work proposes a dual semantic-aware recurrent global-adaptive network (DSRG) to address the above problems. |
Liuyi Wang; Zongtao He; Jiagui Tang; Ronghao Dang; Naijia Wang; Chengju Liu; Qijun Chen; |
| 165 | Detecting Adversarial Faces Using Only Real Face Self-Perturbations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we investigate the intrinsic generality of adv-faces and propose to generate pseudo adv-faces by perturbing real faces with three heuristically designed noise patterns. |
Qian Wang; Yongqin Xian; Hefei Ling; Jinyuan Zhang; Xiaorui Lin; Ping Li; Jiazhong Chen; Ning Yu; |
| 166 | Align, Perturb and Decouple: Toward Better Leverage of Difference Information for RSI Change Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we revisit the importance of feature difference for change detection in RSI, and propose a series of operations to fully exploit the difference information: Alignment, Perturbation and Decoupling (APD). |
Supeng Wang; Yuxi Li; Ming Xie; Mingmin Chi; Yabiao Wang; Chengjie Wang; Wenbing Zhu; |
| 167 | Learning 3D Photography Videos Via Self-supervised Diffusion on Single Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To reduce the training and inference gap, we propose a novel self-supervised diffusion model as the inpainting module. |
Xiaodong Wang; Chenfei Wu; Shengming Yin; Minheng Ni; Jianfeng Wang; Linjie Li; Zhengyuan Yang; Fan Yang; Lijuan Wang; Zicheng Liu; Yuejian Fang; Nan Duan; |
| 168 | Dual-view Correlation Hybrid Attention Network for Robust Holistic Mammogram Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a dual-view correlation hybrid attention network (DCHA-Net) for robust holistic mammogram classification. |
Zhiwei Wang; Junlin Xian; Kangyi Liu; Xin Li; Qiang Li; Xin Yang; |
| 169 | Accurate MRI Reconstruction Via Multi-Domain Recurrent Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This hinders the further reconstruction performance improvement. To tackle this issue, in this work, we propose a new multi-domain recurrent network (MDR-Net) with multi-domain learning (MDL) blocks as its basic units to reconstruct the undersampled MR image progressively. |
Jinbao Wei; Zhijie Wang; Kongqiao Wang; Li Guo; Xueyang Fu; Ji Liu; Xun Chen; |
| 170 | From Generation to Suppression: Towards Effective Irregular Glow Removal for Nighttime Visibility Enhancement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Glow effects are inevitable in the presence of artificial light sources and cause further diffused blurring when directly enhanced. To settle this issue, we innovatively consider the glow suppression task as learning physical glow generation via multiple scattering estimation according to the Atmospheric Point Spread Function (APSF). |
Wanyu Wu; Wei Wang; Zheng Wang; Kui Jiang; Xin Xu; |
| 171 | Hierarchical Semantic Contrast for Weakly Supervised Semantic Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, they generally exploit single-level semantics, which may hamper the model to learn a comprehensive semantic structure. Motivated by the prior that each image has multiple levels of semantics, we propose hierarchical semantic contrast (HSC) to ameliorate the above problem. |
Yuanchen Wu; Xiaoqiang Li; Songmin Dai; Jide Li; Tong Liu; Shaorong Xie; |
| 172 | Learning Monocular Depth in Dynamic Environment Via Context-aware Temporal Attention Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present CTA-Depth, a Context-aware Temporal Attention guided network for multi-frame monocular Depth estimation. |
Zizhang Wu; Zhuozheng Li; Zhi-Gang Fan; Yunzhe Wu; Yuanzhu Gan; Jian Pu; |
| 173 | Hyperspectral Image Denoising Using Uncertainty-Aware Adjustor Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work presents the UA-Adjustor, a comprehensive adjustor that enhances denoising performance by considering both the band-to-pixel and enhancement-to-adjustment aspects. |
Jiahua Xiao; Xing Wei; |
| 174 | ViT-CX: Causal Explanation of Vision Transformers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a novel method for explaining ViTs called ViT-CX. |
Weiyan Xie; Xiao-Hui Li; Caleb Chen Cao; Nevin L. Zhang; |
| 175 | 3D Surface Super-resolution from Enhanced 2D Normal Images: A Multimodal-driven Variational AutoEncoder Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we establish a multimodal-driven variational autoencoder (mmVAE) framework to perform 3D surface enhancement based on 2D normal images. |
Wuyuan Xie; Tengcong Huang; Miaohui Wang; |
| 176 | Diagnose Like A Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, unlike human pathologists who selectively observe specific regions of histopathology tissues under different magnifications, most methods do not incorporate multiple resolutions of the WSIs, hierarchically and attentively, thereby leading to a loss of focus on the WSIs and information from other resolutions. To resolve this issue, we propose a Hierarchical Attention-Guided Multiple Instance Learning framework to fully exploit the WSIs. |
Conghao Xiong; Hao Chen; Joseph J.Y. Sung; Irwin King; |
| 177 | Universal Adaptive Data Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we design a novel data augmentation strategy called “Universal Adaptive Data Augmentation" (UADA). |
Xiaogang Xu; Hengshuang Zhao; |
| 178 | Video Object Segmentation in Panoptic Wild Scenes Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce semi-supervised video object segmentation (VOS) to panoptic wild scenes and present a large-scale benchmark as well as a baseline method for it. |
Yuanyou Xu; Zongxin Yang; Yi Yang; |
| 179 | RuleMatch: Matching Abstract Rules for Semi-supervised Learning of Human Standard Intelligence Tests Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we develop a semi-supervised learning (SSL) method, called RuleMatch, to train deep models with a small number of labeled RPM questions along with other unlabeled questions. |
Yunlong Xu; Lingxiao Yang; Hongzhi You; Zonglei Zhen; Da-Hui Wang; Xiaohong Wan; Xiaohua Xie; Ru-Yuan Zhang; |
| 180 | Prompt Learns Prompt: Exploring Knowledge-Aware Generative Prompt Collaboration For Video Captioning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we explore whether prompts in the fine-tuning can learn knowledge-aware prompts from the pre-training, by designing two different sets of prompts in pre-training and fine-tuning phases respectively. |
Liqi Yan; Cheng Han; Zenglin Xu; Dongfang Liu; Qifan Wang; |
| 181 | Few-shot Classification Via Ensemble Learning with Multi-Order Statistics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address this need, we prove theoretically that leveraging ensemble learning on the base classes can correspondingly reduce the true error in the novel classes. Following this principle, a novel method named Ensemble Learning with Multi-Order Statistics (ELMOS) is proposed in this paper. |
Sai Yang; Fan Liu; Delong Chen; Jun Zhou; |
| 182 | Video Diffusion Models with Local-Global Context Guidance Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a Local-Global Context guided Video Diffusion model (LGC-VD) to capture multi-perception conditions for producing high-quality videos in both conditional/unconditional settings. |
Siyuan Yang; Lu Zhang; Yu Liu; Zhizhuo Jiang; You He; |
| 183 | Exploring Safety Supervision for Continual Test-time Domain Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unfortunately, existing methods based on pseudo-label learning suffer from the changing target domain environment, and the quality of generated pseudo-labels is attenuated due to the domain shift, leading to instantaneous negative learning and long-term knowledge forgetting. To solve these problems, in this paper, we propose a simple yet effective framework for exploring safety supervision with three elaborate strategies: Label Safety, Sample Safety, and Parameter Safety. |
Xu Yang; Yanan Gu; Kun Wei; Cheng Deng; |
| 184 | Action Recognition with Multi-stream Motion Modeling and Mutual Information Maximization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, current methods typically attend to each channel equally and lack theoretical constrains on extracting task-relevant features from the input. In this paper, we seek to tackle these challenges from three aspects: (1) We propose to incorporate an acceleration representation, explicitly modeling the higher-order variations in motion. |
Yuheng Yang; Haipeng Chen; Zhenguang Liu; Yingda Lyu; Beibei Zhang; Shuang Wu; Zhibo Wang; Kui Ren; |
| 185 | Orientation-Independent Chinese Text Recognition in Scene Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we take the first attempt to extract orientation-independent visual features by disentangling content and orientation information of text images, thus recognizing both horizontal and vertical texts robustly in natural scenes. |
Haiyang Yu; Xiaocong Wang; Bin Li; Xiangyang Xue; |
| 186 | Actor-Multi-Scale Context Bidirectional Higher Order Interactive Relation Network for Spatial-Temporal Action Localization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Actor-Multi-Scale Context Bidirectional Higher Order Interactive Relation Network (AMCRNet) that extracts multi-scale context through multiple pooling layers with different sizes. |
Jun Yu; Yingshuai Zheng; Shulan Ruan; Qi Liu; Zhiyuan Cheng; Jinze Wu; |
| 187 | Black-box Prompt Tuning for Vision-Language Model As A Service Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a black-box prompt tuning framework for VLMs to learn task-relevant prompts without back-propagation. |
Lang Yu; Qin Chen; Jiaju Lin; Liang He; |
| 188 | DenseDINO: Boosting Dense Self-Supervised Learning with Token-Based Point-Level Consistency Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a simple yet effective transformer framework for self-supervised learning called DenseDINO to learn dense visual representations. |
Yike Yuan; Xinghe Fu; Yunlong Yu; Xi Li; |
| 189 | Linguistic More: Taking A Further Step Toward Efficient and Accurate Scene Text Recognition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, due to lacking the perception of linguistic knowledge and information, recent vision models suffer from two problems: (1) the pure vision-based query results in attention drift, which usually causes poor recognition and is summarized as linguistic insensitive drift (LID) problem in this paper. (2) the visual feature is suboptimal for the recognition in some vision-missing cases (e.g. occlusion, etc.). To address these issues, we propose a Linguistic Perception Vision model (LPV), which explores the linguistic capability of vision model for accurate text recognition. |
Boqiang Zhang; Hongtao Xie; Yuxin Wang; Jianjun Xu; Yongdong Zhang; |
| 190 | Spatially Covariant Lesion Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose spatially covariant pixel-aligned classifier (SCP) to improve the computational efficiency and meantime maintain or increase accuracy for lesion segmentation. |
Hang Zhang; Rongguang Wang; Jinwei Zhang; Dongdong Liu; Chao Li; Jiahao Li; |
| 191 | HOI-aware Adaptive Network for Weakly-supervised Action Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an HOI-aware adaptive network named AdaAct for weakly-supervised action segmentation. |
Runzhong Zhang; Suchen Wang; Yueqi Duan; Yansong Tang; Yue Zhang; Yap-Peng Tan; |
| 192 | Learning Object Consistency and Interaction in Image Generation from Scene Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper is concerned with synthesizing images conditioned on a scene graph (SG), a set of object nodes and their edges of interactive relations. |
Yangkang Zhang; Chenye Meng; Zejian Li; Pei Chen; Guang Yang; Changyuan Yang; Lingyun Sun; |
| 193 | Manifold-Aware Self-Training for Unsupervised Domain Adaptation on Regressing 6D Object Pose Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Domain gap between synthetic and real data in visual regression (e.g., 6D pose estimation) is bridged in this paper via global feature alignment and local refinement on the coarse classification of discretized anchor classes in target space, which imposes a piece-wise target manifold regularization into domain-invariant representation learning. |
Yichen Zhang; Jiehong Lin; Ke Chen; Zelin Xu; Yaowei Wang; Kui Jia; |
| 194 | FGNet: Towards Filling The Intra-class and Inter-class Gaps for Few-shot Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a uniform network to fill both the gaps, termed FGNet. |
Yuxuan Zhang; Wei Yang; Shaowei Wang; |
| 195 | MM-PCQA: Multi-Modal Learning for No-reference Point Cloud Quality Assessment Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The 2D projections contain rich texture and semantic information but are highly dependent on viewpoints, while the 3D point clouds are more sensitive to geometry distortions and invariant to viewpoints. Therefore, to leverage the advantages of both point cloud and projected image modalities, we propose a novel no-reference Multi-Modal Point Cloud Quality Assessment (MM-PCQA) metric. |
Zicheng Zhang; Wei Sun; Xiongkuo Min; Qiyuan Wang; Jun He; Quan Zhou; Guangtao Zhai; |
| 196 | STS-GAN: Can We Synthesize Solid Texture with High Fidelity from Arbitrary 2D Exemplar? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel generative adversarial nets-based framework (STS-GAN) to extend the given 2D exemplar to arbitrary 3D solid textures. |
Xin Zhao; Jifeng Guo; Lin Wang; Fanqi Li; Jiahao Li; Junteng Zheng; Bo Yang; |
| 197 | TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce TPS++, an attention-enhanced TPS transformation that incorporates the attention mechanism to text rectification for the first time. |
Tianlun Zheng; Zhineng Chen; Jinfeng Bai; Hongtao Xie; Yu-Gang Jiang; |
| 198 | Video Frame Interpolation with Densely Queried Bilateral Correlation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To better model correlations and to produce more accurate motion fields, we propose the Densely Queried Bilateral Correlation (DQBC) that gets rid of the receptive field dependency problem and thus is more friendly to small and fast-moving objects. |
Chang Zhou; Jie Liu; Jie Tang; Gangshan Wu; |
| 199 | Pyramid Diffusion Models for Low-light Image Enhancement Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, we found two problems when doing this, i.e., 1) diffusion models keep constant resolution in one reverse process, which limits the speed; 2) diffusion models sometimes result in global degradation (e.g., RGB shift). To address the above problems, this paper proposes a Pyramid Diffusion model (PyDiff) for low-light image enhancement. |
Dewei Zhou; Zongxin Yang; Yi Yang; |
| 200 | CADParser: A Learning Approach of Sequence Modeling for B-Rep CAD Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a learning approach CADParser to infer the underlying modeling sequences given a B-Rep CAD model. |
Shengdi Zhou; Tianyi Tang; Bin Zhou; |
| 201 | RePaint-NeRF: NeRF Editting Via Semantic Masks and Diffusion Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel framework that can take RGB images as input and alter the 3D content in neural scenes. |
Xingchen Zhou; Ying He; F. Richard Yu; Jianqiang Li; You Li; |
| 202 | Dichotomous Image Segmentation with Frequency Priors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to tackle DIS with informative frequency priors. |
Yan Zhou; Bo Dong; Yuanfeng Wu; Wentao Zhu; Geng Chen; Yanning Zhang; |
| 203 | A Solution to Co-occurence Bias: Attributes Disentanglement Via Mutual Information Minimization for Pedestrian Attribute Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To render models robust in realistic scenes, we propose the attributes-disentangled feature learning to ensure the recognition of an attribute not inferring on the existence of others, and which is sequentially formulated as a problem of mutual information minimization. |
Yibo Zhou; Hai-Miao Hu; Jinzuo Yu; Zhenbo Xu; Weiqing Lu; Yuran Cao; |
| 204 | Vision Language Navigation with Knowledge-driven Environmental Dreamer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce the Knowledge-driven Environmental Dreamer (KED), a method that leverages the knowledge of the embodied environment and generates unseen scenes for a navigation agent to learn. |
Fengda Zhu; Vincent CS Lee; Xiaojun Chang; Xiaodan Liang; |
| 205 | Efficient Multi-View Inverse Rendering Using A Hybrid Differentiable Rendering Method Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a novel hybrid differentiable rendering method to efficiently reconstruct the 3D geometry and reflectance of a scene from multi-view images captured by conventional hand-held cameras. |
Xiangyang Zhu; Yiling Pan; Bailin Deng; Bin Wang; |
| 206 | Towards Accurate Video Text Spotting with Text-wise Semantic Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an accurate video text spotter, VLSpotter, that reads texts visually, linguistically, and semantically. |
Xinyan Zu; Haiyang Yu; Bin Li; Xiangyang Xue; |
| 207 | A Regular Matching Constraint for String Variables Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here we take a step forward by defining a specialised propagator for the match operation, returning the leftmost position where a pattern can match a given string. |
Roberto Amadini; Peter J. Stuckey; |
| 208 | Learning Constraint Networks Over Unknown Constraint Languages Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we propose a constraint acquisition method that computes a suitable constraint language as part of the learning process, eliminating the need for any advance knowledge. |
Christian Bessiere; Clément Carbonnel; Areski Himeur; |
| 209 | Faster Exact MPE and Constrained Optimization with Deterministic Finite State Automata Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a concise function representation based on deterministic finite state automata for exact most probable explanation and constrained optimization tasks in graphical models. |
Filippo Bistaffa; |
| 210 | Constraints First: A New MDD-based Model to Generate Sentences Under Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a new approach to generating strongly constrained texts. |
Alexandre Bonlarron; Aurélie Calabrèse; Pierre Kornprobst; Jean-Charles Régin; |
| 211 | Learning When to Use Automatic Tabulation in Constraint Model Reformulation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent work has shown learning techniques to be increasingly useful in the context of automatic model reformulation. The goal of this study is to understand whether it is possible to improve the performance of such heuristics, by learning a model to predict whether or not to activate them for a given instance. |
Carlo Cena; Özgür Akgün; Zeynep Kiziltan; Ian Miguel; Peter Nightingale; Felix Ulrich-Oltean; |
| 212 | A Fast Algorithm for Consistency Checking Partially Ordered Time Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we consider the problem of deciding if a (likely incomplete) description of a system of events is consistent, the network consistency problem for the point algebra of partially ordered time (POT). |
Leif Eriksson; Victor Lagerkvist; |
| 213 | Improved Algorithms for Allen’s Interval Algebra By Dynamic Programming with Sublinear Partitioning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we propose a novel framework for solving NP-hard qualitative reasoning problems which we refer to as dynamic programming with sublinear partitioning. |
Leif Eriksson; Victor Lagerkvist; |
| 214 | New Bounds and Constraint Programming Models for The Weighted Vertex Coloring Problem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper addresses the weighted vertex coloring problem (WVCP) which is an NP-hard variant of the graph coloring problem with various applications. |
Olivier Goudet; Cyril Grelier; David Lesaint; |
| 215 | Unifying Core-Guided and Implicit Hitting Set Based Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We develop a general unifying algorithmic framework, based on the recent notion of abstract cores, that captures both CG and IHS computations. |
Hannes Ihalainen; Jeremias Berg; Matti Järvisalo; |
| 216 | Co-Certificate Learning with SAT Modulo Symmetries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a new SAT-based method for generating all graphs up to isomorphism that satisfy a given co-NP property. |
Markus Kirchweger; Tomáš Peitl; Stefan Szeider; |
| 217 | Differentiable Model Selection for Ensemble Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a novel framework for differentiable selection of groups of models by integrating machine learning and combinatorial optimization. |
James Kotary; Vincenzo Di Vito; Ferdinando Fioretto; |
| 218 | Backpropagation of Unrolled Solvers with Folded Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper provides theoretical insights into the backward pass of unrolled optimization, leading to a system for generating efficiently solvable analytical models of backpropagation. |
James Kotary; My H Dinh; Ferdinando Fioretto; |
| 219 | Solving The Identifying Code Set Problem with Grouped Independent Support Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we study a generalised identifying code set (GICS) problem, where a unique signature must be found for each subset of S that has a cardinality of at most k (instead of just each element of S). |
Anna L.D. Latour; Arunabha Sen; Kuldeep S. Meel; |
| 220 | A New Variable Ordering for In-processing Bounded Variable Elimination in SAT Solvers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We define a new variable ordering based on variable activity, called ESA (variable Elimination Scheduled by Activity), for in-processing BVE in Conflict-Driven Clause Learning (CDCL) SAT solvers, and incorporate it into several state-of-the-art CDCL SAT solvers. |
Shuolin Li; Chu-Min Li; Mao Luo; Jordi Coll; Djamal Habet; Felip Manyà; |
| 221 | A Bitwise GAC Algorithm for Alldifferent Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To solve this problem, the key observation of this paper is that the GAC algorithm only cares whether a node of the graph model is in an SCC or not, rather than which SCCs it belongs to. Based on this observation, we propose AllDiffbit, which employs bitwise data structures and operations to efficiently determine if a node is in an SCC. |
Zhe Li; Yaohua Wang; Zhanshan Li; |
| 222 | Flaws of Termination and Optimality in ADOPT-based Algorithms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present counterexamples to the termination and optimality of ADOPT-based algorithms. |
Koji Noshiro; Koji Hasebe; |
| 223 | Fast Algorithms for SAT with Bounded Occurrences of Variables Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we show that SAT can be solved in time O*(1.1238^n) for d=3 and O*(1.2628^n) for d=4, improving the previous results O*(1.1279^n) and O*(1.2721^n) obtained by Wahlström (SAT 2005) nearly 20 years ago. |
Junqiang Peng; Mingyu Xiao; |
| 224 | Computing Twin-width with SAT and Branch & Bound Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose two new algorithmic approaches for computing twin-width that significantly improve the state of the art. |
André Schidler; Stefan Szeider; |
| 225 | Optimal Decision Trees For Interpretable Clustering with Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present a novel SAT-based framework for interpretable clustering that supports clustering constraints and that also provides strong theoretical guarantees on solution quality. |
Pouya Shati; Eldan Cohen; Sheila McIlraith; |
| 226 | Engineering An Efficient Approximate DNF-Counter Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Model counting is a fundamental problem with many practical applications, including query evaluation in probabilistic databases and failure-probability estimation of networks. In this work, we focus on a variant of this problem where the underlying formula is expressed in Disjunctive Normal Form (DNF), also known as #DNF. |
Mate Soos; Divesh Aggarwal; Sourav Chakraborty; Kuldeep S. Meel; Maciej Obremski; |
| 227 | Solving Quantum-Inspired Perfect Matching Problems Via Tutte-Theorem-Based Hybrid Boolean Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel encoding based on Tutte’s Theorem in graph theory as well as optimization techniques. |
Moshe Y. Vardi; Zhiwei Zhang; |
| 228 | Eliminating The Computation of Strongly Connected Components in Generalized Arc Consistency Algorithm for AllDifferent Constraint Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Calculating SCCs is time-consuming in the existing algorithms, so we propose a novel GAC algorithm for AllDifferent constraint in this paper, which eliminates the computation of SCCs. |
Luhan Zhen; Zhanshan Li; Yanzhi Li; Hongbo Li; |
| 229 | CSGCL: Community-Strength-Enhanced Graph Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To tackle this issue, we define ”community strength” to measure the difference of influence among communities. Under this premise, we propose a Community-Strength-enhanced Graph Contrastive Learning (CSGCL) framework to preserve community strength throughout the learning process. |
Han Chen; Ziwen Zhao; Yuhua Li; Yixiong Zou; Ruixuan Li; Rui Zhang; |
| 230 | Probabilistic Masked Attention Networks for Explainable Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here we propose a Probabilistic Masked Attention Network (PMAN) to identify the sparse pattern of attentions, which is more desirable for pruning noisy items in sequential recommendation. |
Huiyuan Chen; Kaixiong Zhou; Zhimeng Jiang; Chin-Chia Michael Yeh; Xiaoting Li; Menghai Pan; Yan Zheng; Xia Hu; Hao Yang; |
| 231 | Learning Gaussian Mixture Representations for Tensor Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we develop a novel TTS forecasting framework, which seeks to individually model each heterogeneity component implied in the time, the location, and the source variables. |
Jiewen Deng; Jinliang Deng; Renhe Jiang; Xuan Song; |
| 232 | Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the magnitude of TKG entities in real-world scenarios is considerable, and an increasing number of new entities will arise as time goes on. Therefore, we propose a novel architecture modeling with relation feature of TKG, namely aDAptivE path-MemOry Network (DaeMon), which adaptively models the temporal path information between query subject and each object candidate across history time. |
Hao Dong; Zhiyuan Ning; Pengyang Wang; Ziyue Qiao; Pengfei Wang; Yuanchun Zhou; Yanjie Fu; |
| 233 | Open Anomalous Trajectory Recognition Via Probabilistic Metric Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we investigate the novel Anomalous Trajectory Recognition problem in an Open-world scenario (ATRO) and introduce a novel probabilistic Metric learning model, namely ATROM, to address it. |
Qiang Gao; Xiaohan Wang; Chaoran Liu; Goce Trajcevski; Li Huang; Fan Zhou; |
| 234 | Beyond Homophily: Robust Graph Anomaly Detection Via Neural Sparsification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose SparseGAD, a novel GAD framework that sparsifies the structures of target graphs to effectively reduce noises and collaboratively learns node representations. |
Zheng Gong; Guifeng Wang; Ying Sun; Qi Liu; Yuting Ning; Hui Xiong; Jingyu Peng; |
| 235 | Targeting Minimal Rare Itemsets from Transaction Databases Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a novel approach to the computation of minimal rare itemsets. |
Amel Hidouri; Badran Raddaoui; Said Jabbour; |
| 236 | Enhancing Network By Reinforcement Learning and Neural Confined Local Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a more effective model with stronger generalization ability by incorporating domain knowledge including measurements of local network structures and decision criteria of heuristics. |
Qifu Hu; Ruyang Li; Qi Deng; Yaqian Zhao; Rengang Li; |
| 237 | A Symbolic Approach to Computing Disjunctive Association Rules from Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we first introduce the k-disjunctive support based itemset, a generalization of the traditional model of itemset by allowing the absence of up to k items in each transaction matching the itemset. Then, to discover more expressive rules from data, we define the concept of (k, k’)-disjunctive support based association rules by considering the antecedent and the consequent of the rule as k-disjunctive and k’-disjunctive support based itemsets, respectively. |
Said Jabbour; Badran Raddaoui; Lakhdar Sais; |
| 238 | OSDP: Optimal Sharded Data Parallel for Distributed Deep Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose Optimal Sharded Data Parallel (OSDP), an automated parallel training system that combines the advantages from both data and model parallelism. |
Youhe Jiang; Fangcheng Fu; Xupeng Miao; Xiaonan Nie; Bin Cui; |
| 239 | Hawkes Process Based on Controlled Differential Equations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we present the concept of Hawkes process based on controlled differential equations (HP-CDE), by adopting the neural controlled differential equation (neural CDE) technology which is an analogue to continuous RNNs. |
Minju Jo; Seungji Kook; Noseong Park; |
| 240 | Computing (1+epsilon)-Approximate Degeneracy in Sublinear Time Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a (1 + epsilon)-approximate solution to the degeneracy problem which runs in O(n log n) time, sublinear in the input size for dense graphs, by sampling a small number of neighbors adjacent to high degree nodes. |
Valerie King; Alex Thomo; Quinton Yong; |
| 241 | SMARTformer: Semi-Autoregressive Transformer with Efficient Integrated Window Attention for Long Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, time series data also contain abundant local temporal dependencies, which are often overlooked in the literature and significantly hinder forecasting performance. To address this issue, we introduce SMARTformer, which stands for SeMi-AutoRegressive Transformer. |
Yiduo Li; Shiyi Qi; Zhe Li; Zhongwen Rao; Lujia Pan; Zenglin Xu; |
| 242 | Do We Need An Encoder-Decoder to Model Dynamical Systems on Networks? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a simple embedding-free alternative based on parametrising two additive vector-field components. |
Bing Liu; Wei Luo; Gang Li; Jing Huang; Bo Yang; |
| 243 | Model Conversion Via Differentially Private Data-Free Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While massive valuable deep models trained on large-scale data have been released to facilitate the artificial intelligence community, they may encounter attacks in deployment which leads to privacy leakage of training data. In this work, we propose a learning approach termed differentially private data-free distillation (DPDFD) for model conversion that can convert a pretrained model (teacher) into its privacy-preserving counterpart (student) via an intermediate generator without access to training data. |
Bochao Liu; Pengju Wang; Shikun Li; Dan Zeng; Shiming Ge; |
| 244 | Gapformer: Graph Transformer with Graph Pooling for Node Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present Gapformer, a method for node classification that deeply incorporates Graph Transformer with Graph Pooling. |
Chuang Liu; Yibing Zhan; Xueqi Ma; Liang Ding; Dapeng Tao; Jia Wu; Wenbin Hu; |
| 245 | Federated Probabilistic Preference Distribution Modelling with Compactness Co-Clustering for Privacy-Preserving Multi-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on the Privacy-Preserving Multi-Domain Recommendation problem (PPMDR). |
Weiming Liu; Chaochao Chen; Xinting Liao; Mengling Hu; Jianwei Yin; Yanchao Tan; Longfei Zheng; |
| 246 | Multi-Scale Subgraph Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: By an experimental analysis, we discover the semantic information of an augmented graph structure may be not consistent as original graph structure, and whether two augmented graphs are positive or negative pairs is highly related with the multi-scale structures. Based on this finding, we propose a multi-scale subgraph contrastive learning architecture which is able to characterize the fine-grained semantic information. |
Yanbei Liu; Yu Zhao; Xiao Wang; Lei Geng; Zhitao Xiao; |
| 247 | Continuous-Time Graph Learning for Cascade Popularity Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a continuous-time graph learning method for cascade popularity prediction, which first connects different cascades via a universal sequence of user-cascade and user-user interactions and then chronologically learns on the sequence by maintaining the dynamic states of users and cascades. |
Xiaodong Lu; Shuo Ji; Le Yu; Leilei Sun; Bowen Du; Tongyu Zhu; |
| 248 | Dynamic Group Link Prediction in Continuous-Time Interaction Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel continuous-time group link prediction method CTGLP to capture the patterns of future link formation between individuals and groups. |
Shijie Luo; He Li; Jianbin Huang; |
| 249 | Capturing The Long-Distance Dependency in The Control Flow Graph Via Structural-Guided Attention for Bug Localization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we argue that the long-distance dependency is crucial for feature extraction from the CFG, and propose a novel bug localization model named sgAttention. |
Yi-Fan Ma; Yali Du; Ming Li; |
| 250 | Uncovering The Largest Community in Social Networks at Scale Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we present an efficient MPS algorithm called Branch-and-Merge (BnM), which outputs an exact maximum k-plex. |
Shohei Matsugu; Yasuhiro Fujiwara; Hiroaki Shiokawa; |
| 251 | Reinforcement Learning Approaches for Traffic Signal Control Under Missing Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we aim to control the traffic signals in a real-world setting, where some of the intersections in the road network are not installed with sensors and thus with no direct observations around them. |
Hao Mei; Junxian Li; Bin Shi; Hua Wei; |
| 252 | Discriminative-Invariant Representation Learning for Unbiased Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Worse still, current invariant representation learning methods in recommendation neglect even hurt the representation discriminability due to data sparsity and label shift. In this light, we propose a new Discriminative-Invariant Representation Learning framework for unbiased recommendation, which incorporates label-conditional clustering and prior-guided contrasting into conventional invariant representation learning to mitigate the impact of data sparsity and label shift, respectively. |
Hang Pan; Jiawei Chen; Fuli Feng; Wentao Shi; Junkang Wu; Xiangnan He; |
| 253 | Semi-supervised Domain Adaptation in Graph Transfer Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This imposes critical challenges on graph transfer learning due to serious domain shifts and label scarcity. To address these challenges, we propose a method named Semi-supervised Graph Domain Adaptation (SGDA). |
Ziyue Qiao; Xiao Luo; Meng Xiao; Hao Dong; Yuanchun Zhou; Hui Xiong; |
| 254 | Self-supervised Graph Disentangled Networks for Review-based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Self-supervised Graph Disentangled Networks for review-based recommendation (SGDN), to separately model the user-item interactions based on the latent factors through the textual review data. |
Yuyang Ren; Haonan Zhang; Qi Li; Luoyi Fu; Xinbing Wang; Chenghu Zhou; |
| 255 | CONGREGATE: Contrastive Graph Clustering in Curvature Spaces Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Correspondingly, we present a novel end-to-end contrastive graph clustering model named CONGREGATE, addressing geometric graph clustering with Ricci curvatures. |
Li Sun; Feiyang Wang; Junda Ye; Hao Peng; Philip S. Yu; |
| 256 | SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present semi-supervised anomaly detection (SAD), an end-to-end framework for anomaly detection on dynamic graphs. |
Sheng Tian; Jihai Dong; Jintang Li; Wenlong Zhao; Xiaolong Xu; Baokun Wang; Bowen Song; Changhua Meng; Tianyi Zhang; Liang Chen; |
| 257 | Causal-Based Supervision of Attention in Graph Neural Network: A Better and Simpler Choice Towards Powerful Attention Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a new framework which utilizes the tool of causality to provide a powerful supervision signal for the learning process of attention functions. |
Hongjun Wang; Jiyuan Chen; Lun Du; Qiang Fu; Shi Han; Xuan Song; |
| 258 | Denoised Self-Augmented Learning for Social Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, social information can be unavoidably noisy in characterizing user preferences due to the ubiquitous presence of interest-irrelevant social connections, such as colleagues or classmates who do not share many common interests. To address this challenge, we propose a novel social recommender called the Denoised Self-Augmented Learning paradigm (DSL). |
Tianle Wang; Lianghao Xia; Chao Huang; |
| 259 | A Canonicalization-Enhanced Known Fact-Aware Framework For Open Knowledge Graph Link Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, they neglect to canonicalize relation phrases (RPs) and triples, making OpenKG maintain high sparsity and impeding the performance. To address the above issues, we propose a Canonicalization-Enhanced Known Fact-Aware (CEKFA) framework that boosts link prediction performance through sparsity reduction of RPs and triples. |
Yilin Wang; Minghao Hu; Zhen Huang; Dongsheng Li; Wei Luo; Dong Yang; Xicheng Lu; |
| 260 | Intent-aware Recommendation Via Disentangled Graph Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present the Intent-aware Recommendation via Disentangled Graph Contrastive Learning (IDCL), which simultaneously learns interpretable intents and behavior distributions over those intents. |
Yuling Wang; Xiao Wang; Xiangzhou Huang; Yanhua Yu; Haoyang Li; Mengdi Zhang; Zirui Guo; Wei Wu; |
| 261 | Feature Staleness Aware Incremental Learning for CTR Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the feature embeddings of a CTR prediction model often get stale when the corresponding features do not appear in current incremental data. In the next period, the model would have a performance degradation on samples containing stale features, which we call the feature staleness problem. To mitigate this problem, we propose a Feature Staleness Aware Incremental Learning method for CTR prediction (FeSAIL) which adaptively replays samples containing stale features. |
Zhikai Wang; Yanyan Shen; Zibin Zhang; Kangyi Lin; |
| 262 | KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a Knowledge-Aware Multi-Faceted framework (KMF) that enhances the richness of label semantics via the extracted KG (Knowledge Graph)-based topics. |
Likang Wu; Junji Jiang; Hongke Zhao; Hao Wang; Defu Lian; Mengdi Zhang; Enhong Chen; |
| 263 | KDLGT: A Linear Graph Transformer Framework Via Kernel Decomposition Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose the Kernel Decomposition Linear Graph Transformer (KDLGT), an accelerating framework for building scalable and powerful GTs. |
Yi Wu; Yanyang Xu; Wenhao Zhu; Guojie Song; Zhouchen Lin; Liang Wang; Shaoguo Liu; |
| 264 | OptIForest: Optimal Isolation Forest for Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we establish a theory on isolation efficiency to answer the question and determine the optimal branching factor for an isolation tree. |
Haolong Xiang; Xuyun Zhang; Hongsheng Hu; Lianyong Qi; Wanchun Dou; Mark Dras; Amin Beheshti; Xiaolong Xu; |
| 265 | Hierarchical Apprenticeship Learning for Disease Progression Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate the incorporation of AL-derived patterns into DPM, utilizing a Time-aware Hierarchical EM Energy-based Subsequence (THEMES) AL approach. |
Xi Yang; Ge Gao; Min Chi; |
| 266 | Exploiting Non-Interactive Exercises in Cognitive Diagnosis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the Exercise-aware Informative Response Sampling (EIRS) framework to address the long-tail problem. |
Fangzhou Yao; Qi Liu; Min Hou; Shiwei Tong; Zhenya Huang; Enhong Chen; Jing Sha; Shijin Wang; |
| 267 | Curriculum Multi-Level Learning for Imbalanced Live-Stream Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The lack of inter-level streamer correlations and intra-level streamer characteristics modeling imposes obstacles to estimating the user behaviors. To tackle these challenges, we propose a curriculum multi-level learning framework for imbalanced recommendation. |
Shuodian Yu; Junqi Jin; Li Ma; Xiaofeng Gao; Xiaopeng Wu; Haiyang Xu; Jian Xu; |
| 268 | Basket Representation Learning By Hypergraph Convolution on Repeated Items for Next-basket Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Basket Representation Learning (BRL) model by leveraging the correlations among intra-basket items. |
Yalin Yu; Enneng Yang; Guibing Guo; Linying Jiang; Xingwei Wang; |
| 269 | Commonsense Knowledge Enhanced Sentiment Dependency Graph for Sarcasm Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we apply the pre-trained COMET model to generate relevant commonsense knowledge, and explore a novel scenario of constructing a commonsense-augmented sentiment graph and a commonsense-replaced dependency graph for each text. |
Zhe Yu; Di Jin; Xiaobao Wang; Yawen Li; Longbiao Wang; Jianwu Dang; |
| 270 | Sequential Recommendation with Probabilistic Logical Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we combine the Deep Neural Network (DNN) SR models with logical reasoning and propose a general framework named Sequential Recommendation with Probabilistic Logical Reasoning (short for SR-PLR). |
Huanhuan Yuan; Pengpeng Zhao; Xuefeng Xian; Guanfeng Liu; Yanchi Liu; Victor S. Sheng; Lei Zhao; |
| 271 | Towards An Integrated View of Semantic Annotation for POIs with Spatial and Textual Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, users’ check-ins are hardly obtained in reality, especially for those newly created POIs. In this context, we present a Spatial-Textual POI Annotation (STPA) model for static POIs, which derives POI categories using only the geographic locations and names of POIs. |
Dabin Zhang; Ronghui Xu; Weiming Huang; Kai Zhao; Meng Chen; |
| 272 | Minimally Supervised Contextual Inference from Human Mobility: An Iterative Collaborative Distillation Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study a more practical yet challenging setting—contextual inference using mobility data with minimal supervision (i.e., a few labels per class and massive unlabeled data). |
Jiayun Zhang; Xinyang Zhang; Dezhi Hong; Rajesh K. Gupta; Jingbo Shang; |
| 273 | Towards Hierarchical Policy Learning for Conversational Recommendation with Hypergraph-based Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing methods heavily rely on a unified decision-making module or heuristic rules, while neglecting to distinguish the roles of different decision procedures, as well as the mutual influences between them. To address this, we propose a novel Director-Actor Hierarchical Conversational Recommender (DAHCR), where the director selects the most effective option, followed by the actor accordingly choosing primitive actions that satisfy user preferences. |
Sen Zhao; Wei Wei; Yifan Liu; Ziyang Wang; Wendi Li; Xian-Ling Mao; Shuai Zhu; Minghui Yang; Zujie Wen; |
| 274 | Online Harmonizing Gradient Descent for Imbalanced Data Streams One-Pass Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study alternatively shows that the imbalance of instances can be implied by the imbalance of gradients. |
Han Zhou; Hongpeng Yin; Xuanhong Deng; Yuyu Huang; |
| 275 | Totally Dynamic Hypergraph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a new method, namely, totally hypergraph neural network (TDHNN), to adjust the hyperedge number for optimizing the hypergraph structure. |
Peng Zhou; Zongqian Wu; Xiangxiang Zeng; Guoqiu Wen; Junbo Ma; Xiaofeng Zhu; |
| 276 | Simplification and Improvement of MMS Approximation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We consider the problem of fairly allocating a set of indivisible goods among n agents with additive valuations, using the popular fairness notion of maximin share (MMS). |
Hannaneh Akrami; Jugal Garg; Eklavya Sharma; Setareh Taki; |
| 277 | Fair and Efficient Allocation of Indivisible Chores with Surplus Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a polynomial-time algorithm which gives EF1 and PO allocations with n-1 surplus. |
Hannaneh Akrami; Bhaskar Ray Chaudhury; Jugal Garg; Kurt Mehlhorn; Ruta Mehta; |
| 278 | Non-Obvious Manipulability in Extensive-Form Mechanisms: The Revelation Principle for Single-Parameter Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work we first extend the cycle monotonicity framework for direct-revelation NOM mechanism design to indirect mechanisms. We then apply this to two settings, single-parameter agents and mechanisms for two agents in which one has a two-value domain, and show that under these models the revelation principle holds: direct mechanisms are just as powerful as indirect ones. |
Thomas Archbold; Bart de Keijzer; Carmine Ventre; |
| 279 | Temporal Network Creation Games Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This observation is the driving force of the recent intensive research on game-theoretic network formation models. In this work we bring together these two recent research directions: temporal graphs and game-theoretic network formation. |
Davide Bilò; Sarel Cohen; Tobias Friedrich; Hans Gawendowicz; Nicolas Klodt; Pascal Lenzner; George Skretas; |
| 280 | Schelling Games with Continuous Types Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We focus on segregation caused by non-categorical attributes, such as household income or position in a political left-right spectrum. |
Davide Bilò; Vittorio Bilò; Michelle Döring; Pascal Lenzner; Louise Molitor; Jonas Schmidt; |
| 281 | Delegated Online Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study a natural online variant of delegation, in which the agent searches through the options in an online fashion. |
Pirmin Braun; Niklas Hahn; Martin Hoefer; Conrad Schecker; |
| 282 | Proportionality Guarantees in Elections with Interdependent Issues Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In particular, this has been shown for Proportional Approval Voting (PAV) and for the Method of Equal Shares (MES). In this paper, we go two steps further: we generalize these guarantees for issues with a non-binary domain, and, most importantly, we consider extensions to elections with dependencies among issues, where we identify restrictions that lead to analogous results. |
Markus Brill; Evangelos Markakis; Georgios Papasotiropoulos; Jannik Peters; |
| 283 | Outsourcing Adjudication to Strategic Jurors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study a scenario where an adjudication task (e.g., the resolution of a binary dispute) is outsourced to a set of agents who are appointed as jurors. |
Ioannis Caragiannis; Nikolaj Schwartzbach; |
| 284 | New Fairness Concepts for Allocating Indivisible Items Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose two alternative fairness concepts—called epistemic EFX (EEFX) and minimum EFX value fairness (MXS)—inspired by EFX and MMS. |
Ioannis Caragiannis; Jugal Garg; Nidhi Rathi; Eklavya Sharma; Giovanna Varricchio; |
| 285 | Optimal Seat Arrangement: What Are The Hard and Easy Cases? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study four NP-hard optimal seat arrangement problems which each have as input a set of n agents, where each agent has cardinal preferences over other agents, and an n-vertex undirected graph (called the seat graph). |
Esra Ceylan; Jiehua Chen; Sanjukta Roy; |
| 286 | Rainbow Cycle Number and EFX Allocations: (Almost) Closing The Gap Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce another problem in extremal combinatorics. |
Shayan Chashm Jahan; Masoud Seddighin; Seyed-Mohammad Seyed-Javadi; Mohammad Sharifi; |
| 287 | Exploring Leximin Principle for Fair Core-Selecting Combinatorial Auctions: Payment Rule Design and Implementation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the MRC rule can suffer from severe unfairness since it ignores individuals’ utilities. To address this limitation, we propose to explore the leximin principle to achieve fairness in core-selecting CAs since the leximin principle prefers to maximize the utility of the worst-off; the resulting bidder-leximin-optimal (BLO) payment rule is then theoretically analyzed and an effective algorithm is further provided to compute the BLO outcome. |
Hao Cheng; Shufeng Kong; Yanchen Deng; Caihua Liu; Xiaohu Wu; Bo An; Chongjun Wang; |
| 288 | Deliberation As Evidence Disclosure: A Tale of Two Protocol Types Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study a model inspired by deliberative practice, in which agents selectively disclose evidence about a set of alternatives prior to taking a final decision on them. |
Julian Chingoma; Adrian Haret; |
| 289 | Adversarial Contention Resolution Games Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce the notion of adversarial equilibrium (AE), which incorporates adversarial selection of players. |
Giorgos Chionas; Bogdan S. Chlebus; Dariusz R. Kowalski; Piotr Krysta; |
| 290 | Measuring A Priori Voting Power in Liquid Democracy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce new power indices to measure the a priori voting power of voters in liquid democracy elections where an underlying network restricts delegations. |
Rachael Colley; Théo Delemazure; Hugo Gilbert; |
| 291 | Measuring and Controlling Divisiveness in Rank Aggregation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We focus instead on identifying divisive issues that express disagreements among the preferences of individuals. |
Rachael Colley; Umberto Grandi; César Hidalgo; Mariana Macedo; Carlos Navarrete; |
| 292 | Inferring Private Valuations from Behavioral Data in Bilateral Sequential Bargaining Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most existing approaches that infer agents’ private information from observable data either rely on strong equilibrium assumptions or require a careful design of agents’ behavior models. To overcome these weaknesses, we propose a Bayesian Learning-based Valuation Inference (BLUE) framework. |
Lvye Cui; Haoran Yu; |
| 293 | Differentiable Economics for Randomized Affine Maximizer Auctions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present an architecture that supports multiple bidders and is perfectly strategyproof, but cannot necessarily represent the optimal mechanism. |
Michael Curry; Tuomas Sandholm; John Dickerson; |
| 294 | Complexity of Efficient Outcomes in Binary-Action Polymatrix Games and Implications for Coordination Problems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce an intermediate, new graph-partition problem, termed MWDP, which is of independent interest, and we provide a complexity dichotomy for it. |
Argyrios Deligkas; Eduard Eiben; Gregory Gutin; Philip Neary; Anders Yeo; |
| 295 | Algorithmics of Egalitarian Versus Equitable Sequences of Committees Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the election of sequences of committees, where in each of tau levels (e.g. modeling points in time) a committee consisting of k candidates from a common set of m candidates is selected. |
Eva Michelle Deltl; Till Fluschnik; Robert Bredereck; |
| 296 | Discrete Two Player All-Pay Auction with Complete Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study discrete two player all-pay auction with complete information. |
Marcin Dziubiński; Krzysztof Jahn; |
| 297 | Participatory Budgeting: Data, Tools and Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We provide a library of participatory budgeting data (Pabulib) and open source tools (Pabutools and Pabustats) for analysing this data. |
Piotr Faliszewski; Jarosław Flis; Dominik Peters; Grzegorz Pierczyński; Piotr Skowron; Dariusz Stolicki; Stanisław Szufa; Nimrod Talmon; |
| 298 | An Experimental Comparison of Multiwinner Voting Rules on Approval Elections Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we experimentally compare major approval based multiwinner voting rules. |
Piotr Faliszewski; Martin Lackner; Krzysztof Sornat; Stanisław Szufa; |
| 299 | Diversity, Agreement, and Polarization in Elections Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In particular, we present "maps of preference orders" that highlight relations between the votes in a given election and which help in making arguments about their nature. |
Piotr Faliszewski; Andrzej Kaczmarczyk; Krzysztof Sornat; Stanisław Szufa; Tomasz Wąs; |
| 300 | Revenue Maximization Mechanisms for An Uninformed Mediator with Communication Abilities Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the problem of designing revenue-maximizing mechanisms for the mediator. |
Zhikang Fan; Weiran Shen; |
| 301 | Strategic Resource Selection with Homophilic Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For our general model, we consider the existence and quality of equilibria and the complexity of maximizing the social welfare. |
Jonathan Gadea Harder; Simon Krogmann; Pascal Lenzner; Alexander Skopalik; |
| 302 | New Algorithms for The Fair and Efficient Allocation of Indivisible Chores Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the problem of fairly and efficiently allocating indivisible chores among agents with additive disutility functions. |
Jugal Garg; Aniket Murhekar; John Qin; |
| 303 | First-Choice Maximality Meets Ex-ante and Ex-post Fairness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For the assignment problem where multiple indivisible items are allocated to a group of agents given their ordinal preferences, we design randomized mechanisms that satisfy first-choice maximality (FCM), i.e., maximizing the number of agents assigned their first choices, together with Pareto efficiency (PE). |
Xiaoxi Guo; Sujoy Sikdar; Lirong Xia; Yongzhi Cao; Hanpin Wang; |
| 304 | A Unifying Formal Approach to Importance Values in Boolean Functions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Quantifying the relative impact of variables on the truth value by means of importance values can provide useful insights to steer system design and debugging. In this paper, we introduce a uniform framework for reasoning about such values, relying on a generic notion of importance value functions (IVFs). |
Hans Harder; Simon Jantsch; Christel Baier; Clemens Dubslaff; |
| 305 | Fairly Allocating Goods and (Terrible) Chores Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nonetheless, we propose a natural subclass of lexicographic preferences for which an EFX and Pareto optimal (PO) allocation is guaranteed to exist and can be computed efficiently for any mixed instance. |
Hadi Hosseini; Aghaheybat Mammadov; Tomasz Wąs; |
| 306 | On Lower Bounds for Maximin Share Guarantees Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, they proved that an MMS allocation is not guaranteed to exist for instances with 3 agents and at least 9 items, or n ≥ 4 agents and at least 3n + 3 items. In this work, we shrink the gap between these upper and lower bounds for guaranteed existence of MMS allocations. |
Halvard Hummel; |
| 307 | Fair Division with Two-Sided Preferences Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study a fair division setting in which a number of players are to be fairly distributed among a set of teams. |
Ayumi Igarashi; Yasushi Kawase; Warut Suksompong; Hanna Sumita; |
| 308 | Ties in Multiwinner Approval Voting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We consider a family of Thiele rules, their greedy variants, Phragmen’s sequential rule, and Method of Equal Shares. |
Łukasz Janeczko; Piotr Faliszewski; |
| 309 | Matchings Under One-Sided Preferences with Soft Quotas Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The order in which these objectives are considered, the different possibilities to optimize deviation combined with the well-studied notions of optimality w.r.t. preferences open up a range of optimization problems of practical importance. We present efficient algorithms based on flow-networks to solve these optimization problems. |
Santhini K. A.; Raghu Raman Ravi; Meghana Nasre; |
| 310 | Convergence in Multi-Issue Iterative Voting Under Uncertainty Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study strategic behavior in iterative plurality voting for multiple issues under uncertainty. We introduce a model synthesizing simultaneous multi-issue voting with local dominance theory, in which agents repeatedly update their votes based on sets of vote profiles they deem possible, and determine its convergence properties. |
Joshua Kavner; Reshef Meir; Francesca Rossi; Lirong Xia; |
| 311 | Random Assignment of Indivisible Goods Under Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we consider a more general setting in which each agent can consume a set of items only if the set satisfies her feasibility constraint. |
Yasushi Kawase; Hanna Sumita; Yu Yokoi; |
| 312 | Game Theory with Simulation of Other Players Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we first formally define games in which one player can simulate another at a cost, and derive some basic properties of such games. |
Vojtěch Kovařík; Caspar Oesterheld; Vincent Conitzer; |
| 313 | Truthful Fair Mechanisms for Allocating Mixed Divisible and Indivisible Goods Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the problem of designing truthful and fair mechanisms when allocating a mixture of divisible and indivisible goods. |
Zihao Li; Shengxin Liu; Xinhang Lu; Biaoshuai Tao; |
| 314 | Auto-bidding with Budget and ROI Constrained Buyers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In our study, we focus on second-price auctions where bidders have both private budget and private ROI (return on investment) constraints. We formulate the auto-bidding system design problem as a mathematical program and analyze the auto-bidders’ bidding strategy under such constraints. |
Xiaodong Liu; Weiran Shen; |
| 315 | Approximating Fair Division on D-Claw-Free Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we consider the class of graphs of goods which do not contain a star with d+1 edges (where d > 1) as an induced subgraph. |
Zbigniew Lonc; |
| 316 | Fair Division of A Graph Into Compact Bundles Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an alternative for connectivity in fair division. |
Jayakrishnan Madathil; |
| 317 | Finding Mixed-Strategy Equilibria of Continuous-Action Games Without Gradients Using Randomized Policy Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the problem of computing an approximate Nash equilibrium of continuous-action game without access to gradients. |
Carlos Martin; Tuomas Sandholm; |
| 318 | Deliberation and Voting in Approval-Based Multi-Winner Elections Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we build a deliberation model using established models from the opinion-dynamics literature and study the effect of different deliberation mechanisms on voting outcomes achieved when using well-studied voting rules. |
Kanav Mehra; Nanda Kishore Sreenivas; Kate Larson; |
| 319 | Learning Efficient Truthful Mechanisms for Trading Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose an approach for computing or learning truthful and efficient mechanisms for given networks in a Bayesian setting, where WBB and IR, respectively, are relaxed to ex ante and interim for a given distribution over the private information. |
Takayuki Osogami; Segev Wasserkrug; Elisheva S. Shamash; |
| 320 | Participatory Budgeting with Multiple Degrees of Projects and Ranged Approval Votes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we let each project have a set of permissible costs, each reflecting a possible degree of sophistication of the project. |
Gogulapati Sreedurga; |
| 321 | The Computational Complexity of Single-Player Imperfect-Recall Games Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study single-player extensive-form games with imperfect recall, such as the Sleeping Beauty problem or the Absentminded Driver game. |
Emanuel Tewolde; Caspar Oesterheld; Vincent Conitzer; Paul W. Goldberg; |
| 322 | Error in The Euclidean Preference Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, previous work has shown there are ordinal preference profiles that cannot be represented with this structure if the Euclidean space has two fewer dimensions than there are individuals or alternatives. We extend this result, showing that there are situations in which almost all preference profiles cannot be represented with the Euclidean model, and derive a theoretical lower bound on the expected error when using the Euclidean model to approximate non-Euclidean preference profiles. |
Luke Thorburn; Maria Polukarov; Carmine Ventre; |
| 323 | Maximin-Aware Allocations of Indivisible Chores with Symmetric and Asymmetric Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recently, a new fairness notion, maximin-awareness (MMA) was proposed and it guarantees that every agent is not the worst-off one, no matter how the items that are not allocated to this agent are distributed. We adapt and generalize this notion to the case of indivisible chores and when the agents may have arbitrary weights. |
Tianze Wei; Bo Li; Minming Li; |
| 324 | Ordinal Hedonic Seat Arrangement Under Restricted Preference Domains: Swap Stability and Popularity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study a variant of hedonic games, called hedonic seat arrangements in the literature, where the goal is not to partition the agents into coalitions but to assign them to vertices of a given graph; their satisfaction is then based on the subset of agents in their neighborhood. |
Anaëlle Wilczynski; |
| 325 | Truthful Auctions for Automated Bidding in Online Advertising Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the resulted allocation mapped from these non-decreasing functions generally follows an irregular shape, making it difficult to obtain a closed-form expression for the auction objective. To overcome this design difficulty, we propose a family of truthful automated bidding auction with personalized rank scores, similar to the Generalized Second-Price (GSP) auction. |
Yidan Xing; Zhilin Zhang; Zhenzhe Zheng; Chuan Yu; Jian Xu; Fan Wu; Guihai Chen; |
| 326 | Approximate Envy-Freeness in Graphical Cake Cutting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the problem of fairly allocating a divisible resource in the form of a graph, also known as graphical cake cutting. |
Sheung Man Yuen; Warut Suksompong; |
| 327 | Incentive-Compatible Selection for One or Two Influentials Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we aim to select one or two influentials in terms of progeny (the influential power) and prevent agents from manipulating their edges (incentive compatibility). |
Yuxin Zhao; Yao Zhang; Dengji Zhao; |
| 328 | Can You Improve My Code? Optimizing Programs with Local Search Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces a local search method for improving an existing program with respect to a measurable objective. |
Fatemeh Abdollahi; Saqib Ameen; Matthew E. Taylor; Levi H. S. Lelis; |
| 329 | Sequence Learning Using Equilibrium Propagation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus it is not possible to design a model for sequence classification using EP with an LSTM or GRU like architecture. In this paper, we leverage recent developments in modern hopfield networks to further understand energy based models and develop solutions for complex sequence classification tasks using EP while satisfying its convergence criteria and maintaining its theoretical similarities with recurrent backpropagation. |
Malyaban Bal; Abhronil Sengupta; |
| 330 | Towards Collaborative Plan Acquisition Through Theory of Mind Modeling in Situated Dialogue Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Specifically, we formulate a novel problem for agents to predict the missing task knowledge for themselves and for their partners based on rich perceptual and dialogue history. |
Cristian-Paul Bara; Ziqiao Ma; Yingzhuo Yu; Julie Shah; Joyce Chai; |
| 331 | Sketch Recognition Via Part-based Hierarchical Analogical Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel part-based approach for sketch recognition, based on hierarchical analogical learning, a new method to apply analogical learning to qualitative representations. |
Kezhen Chen; Ken Forbus; Balaji Vasan Srinivasan; Niyati Chhaya; Madeline Usher; |
| 332 | Black-Box Data Poisoning Attacks on Crowdsourcing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a black-box data poisoning attack framework that finds the optimal strategies for instance selection and labeling to attack unknown label aggregation models in crowdsourcing. |
Pengpeng Chen; Yongqiang Yang; Dingqi Yang; Hailong Sun; Zhijun Chen; Peng Lin; |
| 333 | TDG4Crowd:Test Data Generation for Evaluation of Aggregation Algorithms in Crowdsourcing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To our best knowledge, little effort have been made to the fair evaluation of aggregation algorithms. To fill in this gap, we propose a novel method named TDG4Crowd that can automatically generate comprehensive and balanced datasets. |
Yili Fang; Chaojie Shen; Huamao Gu; Tao Han; Xinyi Ding; |
| 334 | Enhancing Efficient Continual Learning with Dynamic Structure Development of Spiking Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Drawing on continual learning mechanisms during child growth and development, we propose Dynamic Structure Development of Spiking Neural Networks (DSD-SNN) for efficient and adaptive continual learning. |
Bing Han; Feifei Zhao; Yi Zeng; Wenxuan Pan; Guobin Shen; |
| 335 | Learnable Surrogate Gradient for Direct Training Spiking Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel perspective to unlock the width limitation of SG, called the learnable surrogate gradient (LSG) method. |
Shuang Lian; Jiangrong Shen; Qianhui Liu; Ziming Wang; Rui Yan; Huajin Tang; |
| 336 | A Hierarchical Approach to Population Training for Human-AI Collaboration Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recent work have shown that training a single agent as the best response to a diverse population of training partners significantly increases an agent’s robustness to novel partners. We further enhance the population-based training approach by introducing a Hierarchical Reinforcement Learning (HRL) based method for Human-AI Collaboration. |
Yi Loo; Chen Gong; Malika Meghjani; |
| 337 | Strategic Adversarial Attacks in AI-assisted Decision Making to Reduce Human Trust and Reliance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, through a human-subject experiment, we first show that in AI-assisted decision making, the timing of the attacks largely influences how much humans decrease their trust in and reliance on AI�the decrease is particularly salient when attacks occur on decision making tasks that humans are highly confident themselves. Based on these insights, we next propose an algorithmic framework to infer the human decision maker�s hidden trust in the AI model and dynamically decide when the attacker should launch an attack to the model. |
Zhuoran Lu; Zhuoyan Li; Chun-Wei Chiang; Ming Yin; |
| 338 | Learning Heuristically-Selected and Neurally-Guided Feature for Age Group Recognition Using Unconstrained Smartphone Interaction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To fully utilize the capability of mobile devices without breaching personal privacy, we build the first corpus for age group recognition on smartphones with more than 1,445,087 unrestricted actions from 2,100 subjects. |
Yingmao Miao; Qiwei Tian; Chenhao Lin; Tianle Song; Yajie Zhou; Junyi Zhao; Shuxin Gao; Minghui Yang; Chao Shen; |
| 339 | Learning When to Advise Human Decision Makers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel design of AI systems in which the algorithm interacts with the human user in a two-sided manner and aims to provide advice only when it is likely to be beneficial for the user in making their decision. |
Gali Noti; Yiling Chen; |
| 340 | A Low Latency Adaptive Coding Spike Framework for Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we use learnable matrix multiplication to encode and decode spikes, improving the flexibility of the coders and thus reducing latency. |
Lang Qin; Rui Yan; Huajin Tang; |
| 341 | Cognitively Inspired Learning of Incremental Drifting Concepts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In contrast, machine learning models perform poorly in a continual learning setting, where input data distribution changes over time. Inspired by the nervous system learning mechanisms, we develop a computational model that enables a deep neural network to learn new concepts and expand its learned knowledge to new domains incrementally in a continual learning setting. |
Mohammad Rostami; Aram Galstyan; |
| 342 | A New ANN-SNN Conversion Method with High Accuracy, Low Latency and Good Robustness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we reanalyzed the relationship between Integrate-and-Fire (IF) neuron model and ReLU activation function, proposed a StepReLU activation function more suitable for SNNs under membrane potential encoding, and used it to train ANNs. |
Bingsen Wang; Jian Cao; Jue Chen; Shuo Feng; Yuan Wang; |
| 343 | The Effects of AI Biases and Explanations on Human Decision Fairness: A Case Study of Bidding in Rental Housing Markets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, using a real-world business decision making scenario—bidding in rental housing markets—as our testbed, we present an experimental study on understanding how the bias level of the AI-based decision aid as well as the provision of AI explanations affect the fairness level of humans’ decisions, both during and after their usage of the decision aid. |
Xinru Wang; Chen Liang; Ming Yin; |
| 344 | Spatial-Temporal Self-Attention for Asynchronous Spiking Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To fully leverage the advantages of both SNNs and attention mechanisms, we propose an SNNs-based spatial-temporal self-attention (STSA) mechanism, which calculates the feature dependence across the time and space domains without destroying the asynchronous transmission properties of SNNs. |
Yuchen Wang; Kexin Shi; Chengzhuo Lu; Yuguo Liu; Malu Zhang; Hong Qu; |
| 345 | Preferences and Constraints in Abstract Argumentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present an extension of Abstract Argumentation Framework (AF) that allows for the representation of preferences over arguments’ truth values (3-valued preferences). |
Gianvincenzo Alfano; Sergio Greco; Francesco Parisi; Irina Trubitsyna; |
| 346 | Leveraging Argumentation for Generating Robust Sample-based Explanations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper solves the problem by introducing explanation functions that generate abductive explanations from a sample of instances. |
Leila Amgoud; Philippe Muller; Henri Trenquier; |
| 347 | Abstraction of Nondeterministic Situation Calculus Action Theories Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We develop a general framework for abstracting the behavior of an agent that operates in a nondeterministic domain, i.e., where the agent does not control the outcome of the nondeterministic actions, based on the nondeterministic situation calculus and the ConGolog programming language. |
Bita Banihashemi; Giuseppe De Giacomo; Yves Lesperance; |
| 348 | Bipolar Abstract Dialectical Frameworks Are Covered By Kleene’s Three-valued Logic Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we formally prove that in case of BADFs we may bypass the computationally intensive procedure via applying Kleene’s three-valued logic K. |
Ringo Baumann; Maximilian Heinrich; |
| 349 | REPLACE: A Logical Framework for Combining Collective Entity Resolution and Repairing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper considers the problem of querying dirty databases, which may contain both erroneous facts and multiple names for the same entity. |
Meghyn Bienvenu; Gianluca Cima; Víctor Gutiérrez-Basulto; |
| 350 | Augmenting Automated Spectrum Based Fault Localization for Multiple Faults Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a new algorithm that "augments” previously proposed SBFL heuristics to produce a ranked list where faulty components ranked low by base SBFL metrics are ranked significantly higher. |
Prantik Chatterjee; Jose Campos; Rui Abreu; Subhajit Roy; |
| 351 | Automatic Verification for Soundness of Bounded QNP Abstractions for Generalized Planning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, based on the previous work, we explore automatic verification of sound abstractions for GP. |
Zhenhe Cui; Weidu Kuang; Yongmei Liu; |
| 352 | On Translations Between ML Models for XAI Purposes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, the succinctness of various ML models is studied. |
Alexis de Colnet; Pierre Marquis; |
| 353 | Description Logics with Pointwise Circumscription Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our main idea is to replace the second-order quantification step of classic circumscription with a series of (pointwise) local checks on all domain elements and their immediate neighbourhood. |
Federica Di Stefano; Magdalena Ortiz; Mantas Šimkus; |
| 354 | Parametrized Gradual Semantics Dealing with Varied Degrees of Compensation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a parameterised family of gradual semantics, which unifies multiple semantics that share some principles but differ in their strategy regarding solving dilemmas. |
Dragan Doder; Leila Amgoud; Srdjan Vesic; |
| 355 | Learning Small Decision Trees with Large Domain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present FPT algorithms for learning a smallest or lowest-depth DT from data, with the only parameters solution size and maximum difference. |
Eduard Eiben; Sebastian Ordyniak; Giacomo Paesani; Stefan Szeider; |
| 356 | Explaining Answer-Set Programs with Abstract Constraint Atoms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we introduce several formal notions of justification in this setting based on the one hand on a semantic characterisation utilising minimal partial models, and on the other hand on a more ruled-guided approach. |
Thomas Eiter; Tobias Geibinger; |
| 357 | Treewidth-Aware Complexity for Evaluating Epistemic Logic Programs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present complexity results for the evaluation of key ELP fragments for treewidth, which are exponentially better than known results for full ELPs. |
Jorge Fandinno; Markus Hecher; |
| 358 | Quantitative Reasoning and Structural Complexity for Claim-Centric Argumentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a novel concept of a justification status for claims, a quantitative measure of extensions supporting a particular claim. |
Johannes K. Fichte; Markus Hecher; Yasir Mahmood; Arne Meier; |
| 359 | An Ensemble Approach for Automated Theorem Proving Based on Efficient Name Invariant Graph Neural Representations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present NIAGRA; an ensemble Name InvAriant Graph RepresentAtion. |
Achille Fokoue; Ibrahim Abdelaziz; Maxwell Crouse; Shajith Ikbal; Akihiro Kishimoto; Guilherme Lima; Ndivhuwo Makondo; Radu Marinescu; |
| 360 | Reverse Engineering of Temporal Queries Mediated By LTL Ontologies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In reverse engineering of database queries, we aim to construct a query from a given set of answers and non-answers; it can then be used to explore the data further or as an explanation of the answers and non-answers. |
Marie Fortin; Boris Konev; Vladislav Ryzhikov; Yury Savateev; Frank Wolter; Michael Zakharyaschev; |
| 361 | Disentanglement of Latent Representations Via Causal Interventions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Instead of directly representing the factors of variation, the problem of disentanglement can be seen as finding the interventions on one image that yield a change to a single factor. Following this assumption, we introduce a new method for disentanglement inspired by causal dynamics that combines causality theory with vector-quantized variational autoencoders. |
Gaël Gendron; Michael Witbrock; Gillian Dobbie; |
| 362 | Safety Verification and Universal Invariants for Relational Action Bases Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To make these systems amenable to verification, the amount of information stored in each state needs to be bounded, or restrictions are imposed on the preconditions and effects of actions. We lift these restrictions by introducing the framework of Relational Action Bases (RABs), which generalizes existing frameworks and in which unbounded relational states are evolved through actions that can (1) quantify both existentially and universally over the data, and (2) use arithmetic constraints. |
Silvio Ghilardi; Alessandro Gianola; Marco Montali; Andrey Rivkin; |
| 363 | Tractable Diversity: Scalable Multiperspective Ontology Management Via Standpoint EL Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we introduce Standpoint EL, a multi-modal extension of EL that allows for the integrated representation of domain knowledge relative to diverse, possibly conflicting standpoints (or contexts), which can be hierarchically organised and put in relation to each other. |
Lucía Gómez Álvarez; Sebastian Rudolph; Hannes Strass; |
| 364 | Ranking-based Argumentation Semantics Applied to Logical Argumentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we make a systematic investigation into the be- haviour of ranking-based semantics applied to existing formalisms for structured argumentation. |
Jesse Heyninck; Badran Raddaoui; Christian Straßer; |
| 365 | Temporal Datalog with Existential Quantification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we consider TGDs in the temporal setting, by introducing and studying DatalogMTLE—an extension of metric temporal Datalog (DatalogMTL) obtained by allowing for existential rules in programs. |
Matthias Lanzinger; Markus Nissl; Emanuel Sallinger; Przemysław A. Wałęga; |
| 366 | A Rule-Based Modal View of Causal Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a novel rule-based semantics for causal reasoning as well as a number of modal languages interpreted over it. |
Emiliano Lorini; |
| 367 | Probabilistic Temporal Logic for Reasoning About Bounded Policies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To build a theory of intention revision for agents operating in stochastic environments, we need a logic in which we can explicitly reason about their decision-making policies and those policies’ uncertain outcomes. Towards this end, we propose PLBP, a novel probabilistic temporal logic for Markov Decision Processes that allows us to reason about policies of bounded size. |
Nima Motamed; Natasha Alechina; Mehdi Dastani; Dragan Doder; Brian Logan; |
| 368 | Shhh! The Logic of Clandestine Operations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The paper proposes a formal semantics of clandestine operations and introduces a sound and complete logical system that describes the interplay between the distributed knowledge modality and a modality capturing coalition power to conduct clandestine operations. |
Pavel Naumov; Oliver Orejola; |
| 369 | The Parameterized Complexity of Finding Concise Local Explanations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We consider the computational problem of finding a smallest local explanation (anchor) for classifying a given feature vector (example) by a black-box model. |
Sebastian Ordyniak; Giacomo Paesani; Stefan Szeider; |
| 370 | Relative Inconsistency Measures for Indefinite Databases with Denial Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we investigate relative inconsistency measures for indefinite databases, which allow for indefinite or partial information which is formally expressed by means of disjunctive tuples. |
Francesco Parisi; John Grant; |
| 371 | A Comparative Study of Ranking Formulas Based on Consistency Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper is concerned with the problem of ranking information of a knowledge base when this latter is possibly inconsistent. |
Badran Raddaoui; Christian Straßer; Said Jabbour; |
| 372 | On Discovering Interesting Combinatorial Integer Sequences Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A key technical contribution of our work is the method for generation of first-order logic sentences which is able to drastically prune the space of sentences by discarding large fraction of sentences which would lead to redundant integer sequences. |
Martin Svatoš; Peter Jung; Jan Tóth; Yuyi Wang; Ondřej Kuželka; |
| 373 | SAT-Based PAC Learning of Description Logic Concepts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose bounded fitting as a scheme for learning description logic concepts in the presence of ontologies. |
Balder ten Cate; Maurice Funk; Jean Christoph Jung; Carsten Lutz; |
| 374 | Efficient Computation of General Modules for ALC Ontologies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a method for extracting general modules for ontologies formulated in the description logic ALC. |
Hui Yang; Patrick Koopmann; Yue Ma; Nicole Bidoit; |
| 375 | On The Paradox of Learning to Reason from Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We make observations that seem to contradict each other: BERT attains near-perfect accuracy on in-distribution test examples while failing to generalize to other data distributions over the exact same problem space. Our study provides an explanation for this paradox: instead of learning to emulate the correct reasoning function, BERT has, in fact, learned statistical features that inherently exist in logical reasoning problems. |
Honghua Zhang; Liunian Harold Li; Tao Meng; Kai-Wei Chang; Guy Van den Broeck; |
| 376 | A Multi-Modal Neural Geometric Solver with Textual Clauses Parsed from Diagram Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we convert diagrams into basic textual clauses to describe diagram features effectively, and propose a new neural solver called PGPSNet to fuse multi-modal information efficiently. |
Ming-Liang Zhang; Fei yin; Cheng-Lin Liu; |
| 377 | Enhancing Datalog Reasoning with Hypertree Decompositions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we provide algorithms that exploit hypertree decompositions for the materialisation and incremental evaluation of Datalog programs. |
Xinyue Zhang; Pan Hu; Yavor Nenov; Ian Horrocks; |
| 378 | Building Concise Logical Patterns By Constraining Tsetlin Machine Clause Size Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel variant of TM learning — Clause Size Constrained TMs (CSC-TMs) — where one can set a soft constraint on the clause size. |
K. Darshana Abeyrathna; Ahmed A. O. Abouzeid; Bimal Bhattarai; Charul Giri; Sondre Glimsdal; Ole-Christoffer Granmo; Lei Jiao; Rupsa Saha; Jivitesh Sharma; Svein A. Tunheim; Xuan Zhang; |
| 379 | GIDnets: Generative Neural Networks for Solving Inverse Design Problems Via Latent Space Exploration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In a number of different fields, including Engeneering, Chemistry and Physics, the design of technological tools and device structures is increasingly supported by deep-learning based methods, which provide suggestions on crucial architectural choices based on the properties that these tools and structures should exhibit. The paper proposes a novel architecture, named GIDnet, to address this inverse design problem, which is based on exploring a suitably defined latent space associated with the possible designs. |
Carlo Adornetto; Gianluigi Greco; |
| 380 | CROP: Towards Distributional-Shift Robust Reinforcement Learning Using Compact Reshaped Observation Processing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To improve data efficiency and generalization capabilities, we propose Compact Reshaped Observation Processing (CROP) to reduce the state information used for policy optimization. |
Philipp Altmann; Fabian Ritz; Leonard Feuchtinger; Jonas Nüßlein; Claudia Linnhoff-Popien; Thomy Phan; |
| 381 | Learning to Learn from Corrupted Data for Few-Shot Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the problem, we propose a unified peer-collaboration learning (PCL) framework to extract valid knowledge from corrupted data for few-shot learning. |
Yuexuan An; Xingyu Zhao; Hui Xue; |
| 382 | Computing Abductive Explanations for Boosted Regression Trees Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present two algorithms for generating (resp. evaluating) abductive explanations for boosted regression trees. |
Gilles Audemard; Steve Bellart; Jean-Marie Lagniez; Pierre Marquis; |
| 383 | HOUDINI: Escaping from Moderately Constrained Saddles Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We give polynomial time algorithms for escaping from high-dimensional saddle points under a moderate number of constraints. |
Dmitrii Avdiukhin; Grigory Yaroslavtsev; |
| 384 | Scaling Goal-based Exploration Via Pruning Proto-goals Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The core challenge in goal discovery is finding the right balance between generality (not hand-crafted) and tractability (useful, not too many). Our approach explicitly seeks the middle ground, enabling the human designer to specify a vast but meaningful proto-goal space, and an autonomous discovery process to refine this to a narrower space of controllable, reachable, novel, and relevant goals. |
Akhil Bagaria; Tom Schaul; |
| 385 | ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel approach, ReLiNet (Recurrent Linear Parameter Varying Network), to ensure safety for multistep prediction of dynamical systems. |
Alexandra Baier; Decky Aspandi; Steffen Staab; |
| 386 | Poisoning The Well: Can We Simultaneously Attack A Group of Learning Agents? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, attacks on collections of learning agents remain largely overlooked. We remedy the situation by developing a constructive training-time attack on a population of learning agents and additionally make the attack agnostic to the population’s size. |
Ridhima Bector; Hang Xu; Abhay Aradhya; Chai Quek; Zinovi Rabinovich; |
| 387 | On Approximating Total Variation Distance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce and study the problem of computing the TV distance of two product distributions over the domain {0,1}^n. |
Arnab Bhattacharyya; Sutanu Gayen; Kuldeep S. Meel; Dimitrios Myrisiotis; A. Pavan; N. V. Vinodchandran; |
| 388 | Lifelong Multi-view Spectral Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Hence it leads to high storage consumption, especially for multi-view datasets. In this paper, we address this limitation by introducing a lifelong multi-view clustering framework. |
Hecheng Cai; Yuze Tan; Shudong Huang; Jiancheng Lv; |
| 389 | A Novel Demand Response Model and Method for Peak Reduction in Smart Grids — PowerTAC Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: When RRs are unknown, we propose a Multi-Armed Bandit (MAB) based online algorithm, namely MJSUCB–ExpResponse, to learn RRs. |
Sanjay Chandlekar; Shweta Jain; Sujit Gujar; |
| 390 | Boosting Few-Shot Open-Set Recognition with Multi-Relation Margin Loss Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing FSOSR methods mainly construct an ambiguous distribution of known classes from scarce known samples without considering the latent distribution information of unknowns, which degrades the performance of open-set recognition. To address this issue, we propose a novel loss function called multi-relation margin (MRM) loss that can plug in few-shot methods to boost the performance of FSOSR. |
Yongjuan Che; Yuexuan An; Hui Xue; |
| 391 | Ensemble Reinforcement Learning in Continuous Spaces — A Hierarchical Multi-Step Approach for Policy Training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new technique to train an ensemble of base learners based on an innovative multi-step integration method. |
Gang Chen; Victoria Huang; |
| 392 | Incremental and Decremental Optimal Margin Distribution Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we take the latest statistical learning framework optimal margin distribution machine (ODM) which involves a quadratic-type loss due to the optimization of margin variance, for example, and equip it with the ability to handle IDL tasks. |
Li-Jun Chen; Teng Zhang; Xuanhua Shi; Hai Jin; |
| 393 | Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper develops a foundation model across regions capable of understanding complex meteorological data and providing weather forecasting. |
Shengchao Chen; Guodong Long; Tao Shen; Jing Jiang; |
| 394 | FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks Through Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, manipulating individual model parameters is not only inefficient in meaningfully reducing the communication overhead when training large-scale FL models, but may also be detrimental to the scaling efforts and model performance as shown by recent research. To address these issues, we propose the Federated Opportunistic Block Dropout (FedOBD) approach. |
Yuanyuan Chen; Zichen Chen; Pengcheng Wu; Han Yu; |
| 395 | LSGNN: Towards General Graph Neural Network in Node Classification By Local Similarity Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: For better fusion, we propose a novel and efficient Initial Residual Difference Connection (IRDC) to extract more informative multi-hop information. |
Yuhan Chen; Yihong Luo; Jing Tang; Liang Yang; Siya Qiu; Chuan Wang; Xiaochun Cao; |
| 396 | Graph Propagation Transformer for Graph Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a novel transformer architecture for graph representation learning. |
Zhe Chen; Hao Tan; Tao Wang; Tianrun Shen; Tong Lu; Qiuying Peng; Cheng Cheng; Yue Qi; |
| 397 | Some General Identification Results for Linear Latent Hierarchical Causal Structure Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While some existing methods are able to recover the latent hierarchical causal structure, they mostly suffer from restricted assumptions, including the tree-structured graph constraint, no “triangle" structure, and non-Gaussian assumptions. In this paper, we relax these restrictions above and consider a more general and challenging scenario where the beyond tree-structured graph, the “triangle" structure, and the arbitrary noise distribution are allowed. |
Zhengming Chen; Feng Xie; Jie Qiao; Zhifeng Hao; Ruichu Cai; |
| 398 | Deep Multi-view Subspace Clustering with Anchor Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing DMVSC methods still have two issues: (1) they mainly focus on using autoencoders to nonlinearly embed the data, while the embedding may be suboptimal for clustering because the clustering objective is rarely considered in autoencoders, and (2) existing methods typically have a quadratic or even cubic complexity, which makes it challenging to deal with large-scale data. To address these issues, in this paper we propose a novel deep multi-view subspace clustering method with anchor graph (DMCAG). |
Chenhang Cui; Yazhou Ren; Jingyu Pu; Xiaorong Pu; Lifang He; |
| 399 | Neuro-Symbolic Learning of Answer Set Programs from Raw Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce Neuro-Symbolic Inductive Learner (NSIL), an approach that trains a general neural network to extract latent concepts from raw data, whilst learning symbolic knowledge that maps latent concepts to target labels. |
Daniel Cunnington; Mark Law; Jorge Lobo; Alessandra Russo; |
| 400 | Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Deep Symboilic Learning (DSL), a NeSy system that learns NeSy-functions, i.e., the composition of a (set of) perception functions which map continuous data to discrete symbols, and a symbolic function over the set of symbols. |
Alessandro Daniele; Tommaso Campari; Sagar Malhotra; Luciano Serafini; |
| 401 | DeepPSL: End-to-End Perception and Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce DeepPSL a variant of probabilistic soft logic (PSL) to produce an end-to-end trainable system that integrates reasoning and perception. |
Sridhar Dasaratha; Sai Akhil Puranam; Karmvir Singh Phogat; Sunil Reddy Tiyyagura; Nigel P. Duffy; |
| 402 | Scalable Coupling of Deep Learning with Logical Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a scalable neural architecture and loss function dedicated to learning the constraints and criteria of NP-hard reasoning problems expressed as discrete Graphical Models. |
Marianne Defresne; Sophie Barbe; Thomas Schiex; |
| 403 | Neuro-Symbolic Class Expression Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Arguably, the reliance on myopic heuristic functions contributes to this limitation. We propose a novel neuro-symbolic class expression learning model, DRILL, to mitigate this limitation. |
Caglar Demir; Axel-Cyrille Ngonga Ngomo; |
| 404 | Bidirectional Dilation Transformer for Multispectral and Hyperspectral Image Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose a bidirectional dilation Transformer (BDT) for multispectral and hyperspectral image fusion (MHIF), which aims to leverage the advantages of both MSA and the latent multiscale information specific to MHIF tasks. |
Shangqi Deng; Liang-Jian Deng; Xiao Wu; Ran Ran; Rui Wen; |
| 405 | Understanding The Generalization Ability of Deep Learning Algorithms: A Kernelized Rényi’s Entropy Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the current generalization error bounds within this framework are still far from optimal, while substantial improvements on these bounds are quite challenging due to the intractability of high-dimensional information quantities. To address this issue, we first propose a novel information theoretical measure: kernelized R�nyi’s entropy, by utilizing operator representation in Hilbert space. |
Yuxin Dong; Tieliang Gong; Hong Chen; Chen Li; |
| 406 | ActUp: Analyzing and Consolidating TSNE and UMAP Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We theoretically and experimentally evaluate the space of parameters in the TSNE and UMAP algorithms and observe that a single one — the normalization — is responsible for switching between them. This, in turn, implies that a majority of the algorithmic differences can be toggled without affecting the embeddings. |
Andrew Draganov; Jakob Jørgensen; Katrine Scheel; Davide Mottin; Ira Assent; Tyrus Berry; Cigdem Aslay; |
| 407 | Automatic Truss Design with Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we develop AutoTruss, a two-stage framework to efficiently generate both lightweight and valid truss layouts. |
Weihua Du; Jinglun Zhao; Chao Yu; Xingcheng Yao; Zimeng Song; Siyang Wu; Ruifeng Luo; Zhiyuan Liu; Xianzhong Zhao; Yi Wu; |
| 408 | A Logic-based Approach to Contrastive Explainability for Neurosymbolic Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a CE framework for VQA that uses a neurosymbolic VQA architecture which disentangles perception from reasoning. |
Thomas Eiter; Tobias Geibinger; Nelson Higuera; Johannes Oetsch; |
| 409 | Cardinality-Minimal Explanations for Monotonic Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate whether tractability can be regained by focusing on neural models implementing a monotonic function. |
Ouns El Harzli; Bernardo Cuenca Grau; Ian Horrocks; |
| 410 | Neural Capacitated Clustering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper we propose a new method for the CCP, Neural Capacited Clustering, that learns a neural network to predict the assignment probabilities of points to cluster centers from a data set of optimal or near optimal past solutions of other problem instances. |
Jonas K. Falkner; Lars Schmidt-Thieme; |
| 411 | A Fast Adaptive Randomized PCA Algorithm Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It is desirable to adaptively determine the number of dimensions (rank) for PCA according to a given tolerance of low-rank approximation error. In this work, we aim to develop a fast algorithm solving this adaptive PCA problem. |
Xu Feng; Wenjian Yu; |
| 412 | FedHGN: A Federated Framework for Heterogeneous Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Conventional FL/FGL methods attempting to define a global HGNN model would violate schema privacy. To address these challenges, we propose FedHGN, a novel and general FGL framework for HGNNs. |
Xinyu Fu; Irwin King; |
| 413 | Autonomous Exploration for Navigating in MDPs Using Blackbox RL Algorithms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a novel meta-algorithm which can use any online reinforcement learning algorithm (with appropriate regret guarantees) as a black-box. |
Pratik Gajane; Peter Auer; Ronald Ortner; |
| 414 | Self-Recover: Forecasting Block Maxima in Time Series from Predictors with Disparate Temporal Coverage Using Self-Supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents Self-Recover, a deep learning framework to predict the block maxima of a time window by employing self-supervised learning to address the varying temporal data coverage problem. |
Asadullah Hill Galib; Andrew McDonald; Pang-Ning Tan; Lifeng Luo; |
| 415 | Unbiased Risk Estimator to Multi-Labeled Complementary Label Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we first propose the unbiased risk estimator for the multi-labeled complementary label learning (MLCLL) problem. We also provide an estimation error bound to ensure the convergence of the empirical risk estimator. |
Yi Gao; Miao Xu; Min-Ling Zhang; |
| 416 | Modeling with Homophily Driven Heterogeneous Data in Gossip Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These characteristics are pervasive across practical applications as people with similar interests (thus producing similar data) tend to create communities. This paper proposes a data-driven neighbor weighting strategy for aggregating the models: this enables faster diffusion of knowledge across the communities in the network and leads to quicker convergence. |
Abhirup Ghosh; Cecilia Mascolo; |
| 417 | Adaptive Estimation Q-learning with Uncertainty and Familiarity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel method, Adaptive Estimation Q-learning (AEQ), which uses uncertainty and familiarity to control the value estimation naturally and can adaptively change for specific state-action pair. |
Xiaoyu Gong; Shuai Lü; Jiayu Yu; Sheng Zhu; Zongze Li; |
| 418 | FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a general privacy-preserving vertical federated deep learning framework called FedPass, which leverages adaptive obfuscation to protect the feature and label simultaneously. |
Hanlin Gu; Jiahuan Luo; Yan Kang; Lixin Fan; Qiang Yang; |
| 419 | Globally Consistent Federated Graph Autoencoder for Non-IID Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a globally consistent federated graph autoencoder (GCFGAE) to overcome the non-IID problem in unsupervised federated graph learning via three innovations. |
Kun Guo; Yutong Fang; Qingqing Huang; Yuting Liang; Ziyao Zhang; Wenyu He; Liu Yang; Kai Chen; Ximeng Liu; Wenzhong Guo; |
| 420 | Generalization Guarantees of Self-Training of Halfspaces Under Label Noise Corruption Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We investigate the generalization properties of a self-training algorithm with halfspaces. |
Lies Hadjadj; Massih-Reza Amini; Sana Louhichi; |
| 421 | Learning Preference Models with Sparse Interactions of Criteria Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an approach to learn a decision model in which the interaction pattern is revealed from preference data and kept as simple as possible. |
Margot Herin; Patrice Perny; Nataliya Sokolovska; |
| 422 | BRExIt: On Opponent Modelling in Expert Iteration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose Best Response Expert Iteration (BRExIt), which accelerates learning in games by incorporating opponent models into the state-of-the-art learning algorithm Expert Iteration (ExIt). |
Daniel Hernandez; Hendrik Baier; Michael Kaisers; |
| 423 | Dynamic Flows on Curved Space Generated By Labeled Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose our gradient flow method to leverage the existing dataset (i.e., source) to generate new samples that are close to the dataset of interest (i.e., target). |
Xinru Hua; Truyen Nguyen; Tam Le; Jose Blanchet; Viet Anh Nguyen; |
| 424 | DiffAR: Adaptive Conditional Diffusion Model for Temporal-augmented Human Activity Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, current CSI-based HAR performance is hampered by incomplete CSI recordings due to fixed window sizes in CSI collection and human/machine errors that incur missing values in CSI. To address these issues, we propose DiffAR, a temporal-augmented HAR approach that improves HAR performance by augmenting CSI. |
Shuokang Huang; Po-Yu Chen; Julie McCann; |
| 425 | Progressive Label Propagation for Semi-Supervised Multi-Dimensional Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To reduce the labeling cost, we attempt to deal with the MDC problem under the semi-supervised learning setting. |
Teng Huang; Bin-Bin Jia; Min-Ling Zhang; |
| 426 | Federated Graph Semantic and Structural Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper firstly reveals that local client distortion is brought by both node-level semantics and graph-level structure. First, for node-level semantic, we find that contrasting nodes from distinct classes is beneficial to provide a well-performing discrimination. |
Wenke Huang; Guancheng Wan; Mang Ye; Bo Du; |
| 427 | Enabling Abductive Learning to Exploit Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing methods can hardly make use of them because the large-scale probabilistic logical inference is usually intractable. To address these problems, we present ABductive Learning with Knowledge Graph (ABL-KG) that can automatically mine logic rules from knowledge graphs during learning, using a knowledge forgetting mechanism for filtering out irrelevant information. |
Yu-Xuan Huang; Zequn Sun; Guangyao Li; Xiaobin Tian; Wang-Zhou Dai; Wei Hu; Yuan Jiang; Zhi-Hua Zhou; |
| 428 | Latent Processes Identification From Multi-View Time Series Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel framework MuLTI that employs the contrastive learning technique to invert the data generative process for enhanced identifiability. |
Zenan Huang; Haobo Wang; Junbo Zhao; Nenggan Zheng; |
| 429 | Multi-Modality Deep Network for JPEG Artifacts Reduction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The main challenge is that the highly compressed image loses too much information, resulting in reconstructing high-quality image difficultly. To address this issue, we propose a multimodal fusion learning method for text-guided JPEG artifacts reduction, in which the corresponding text description not only provides the potential prior information of the highly compressed image, but also serves as supplementary information to assist in image deblocking. |
Xuhao Jiang; Weimin Tan; Qing Lin; Chenxi Ma; Bo Yan; Liquan Shen; |
| 430 | Musical Voice Separation As Link Prediction: Modeling A Musical Perception Task As A Multi-Trajectory Tracking Problem Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper targets the perceptual task of separating the different interacting voices, i.e., monophonic melodic streams, in a polyphonic musical piece. We target symbolic music, where notes are explicitly encoded, and model this task as a Multi-Trajectory Tracking (MTT) problem from discrete observations, i.e., notes in a pitch-time space. |
Emmanouil Karystinaios; Francesco Foscarin; Gerhard Widmer; |
| 431 | A Unification Framework for Euclidean and Hyperbolic Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose the Poincaré disk model as our search space, and apply all approximations on the disk (as if the disk is a tangent space derived from the origin), thus getting rid of all inter-space transformations. |
Mehrdad Khatir; Nurendra Choudhary; Sutanay Choudhury; Khushbu Agarwal; Chandan K. Reddy; |
| 432 | SeRO: Self-Supervised Reinforcement Learning for Recovery from Out-of-Distribution Situations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Therefore, agents should be able to recognize whether they are in OOD states and learn how to return to the learned state distribution rather than continue to take unreliable actions. In this study, we propose a novel method for retraining agents to recover from OOD situations in a self-supervised manner when they fall into OOD states. |
Chan Kim; Jaekyung Cho; Christophe Bobda; Seung-Woo Seo; Seong-Woo Kim; |
| 433 | MultiPar-T: Multiparty-Transformer for Capturing Contingent Behaviors in Group Conversations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recognizing and interpreting multiparty behaviors is challenging, as the system must recognize individual behavioral cues, deal with the complexity of multiple streams of data from multiple people, and recognize the subtle contingent social exchanges that take place amongst group members. To tackle this challenge, we propose the Multiparty-Transformer (Multipar- T), a transformer model for multiparty behavior modeling. |
Dong Won Lee; Yubin Kim; Rosalind W. Picard; Cynthia Breazeal; Hae Won Park; |
| 434 | Stochastic Feature Averaging for Learning with Long-Tailed Noisy Labels Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a distance-based sample selection algorithm called Stochastic Feature Averaging (SFA), which fits a Gaussian using the exponential running average of class centroids to capture uncertainty in representation space due to label noise and data scarcity. |
Hao-Tian Li; Tong Wei; Hao Yang; Kun Hu; Chong Peng; Li-Bo Sun; Xun-Liang Cai; Min-Ling Zhang; |
| 435 | Incomplete Multi-view Clustering Via Prototype-based Imputation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study how to achieve two characteristics highly-expected by incomplete multi-view clustering (IMvC). |
Haobin Li; Yunfan Li; Mouxing Yang; Peng Hu; Dezhong Peng; Xi Peng; |
| 436 | Towards Sharp Analysis for Distributed Learning with Random Features Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, using refined proof techniques, we first extend the optimal rates for distributed learning with random features to the non-attainable case. Then, we reduce the number of required random features via data-dependent generating strategy, and improve the allowed number of partitions with additional unlabeled data. |
Jian Li; Yong Liu; |
| 437 | IID-GAN: An IID Sampling Perspective for Regularizing Mode Collapse Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite its success, generative adversarial networks (GANs) still suffer from mode collapse, i.e., the generator can only map latent variables to a partial set of modes in the target distribution. In this paper, we analyze and seek to regularize this issue with an independent and identically distributed (IID) sampling perspective and emphasize that holding the IID property referring to the target distribution for generation can naturally avoid mode collapse. |
Yang Li; Liangliang Shi; Junchi Yan; |
| 438 | Generative Flow Networks for Precise Reward-Oriented Active Learning on Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we innovatively formulate the graph active learning problem as a generative process, named GFlowGNN, which generates various samples through sequential actions with probabilities precisely proportional to a predefined reward function. |
Yinchuan Li; Zhigang Li; Wenqian Li; Yunfeng Shao; Yan Zheng; Jianye Hao; |
| 439 | Teacher Assistant-Based Knowledge Distillation Extracting Multi-level Features on Single Channel Sleep EEG Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel general knowledge distillation framework for sleep stage classification tasks called SleepKD. |
Heng Liang; Yucheng Liu; Haichao Wang; Ziyu Jia; |
| 440 | HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, in the real world, non-identical and independent data distributions (non-IID) among clients hinder the performance of FL due to three issues, i.e., (1) the class statistics shifting, (2) the insufficient hierarchical information utilization, and (3) the inconsistency in aggregating clients. To address the above issues, we propose HyperFed which contains three main modules, i.e., hyperbolic prototype Tammes initialization (HPTI), hyperbolic prototype learning (HPL), and consistent aggregation (CA). |
Xinting Liao; Weiming Liu; Chaochao Chen; Pengyang Zhou; Huabin Zhu; Yanchao Tan; Jun Wang; Yue Qi; |
| 441 | Contrastive Learning and Reward Smoothing for Deep Portfolio Management Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we used reinforcement learning (RL) models to invest assets in order to earn returns. |
Yun-Hsuan Lien; Yuan-Kui Li; Yu-Shuen Wang; |
| 442 | Learning Survival Distribution with Implicit Survival Function Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose Implicit Survival Function (ISF) based on Implicit Neural Representation for survival distribution estimation without strong assumptions, and employ numerical integration to approximate the cumulative distribution function for prediction and optimization. |
Yu Ling; Weimin Tan; Bo Yan; |
| 443 | FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, due to the limitation of communication cost, it is challenging to use large-scale models in FL, which will affect the prediction accuracy. To address these challenges, we propose a novel framework, Federated Enhanced Transformer (FedET), which simultaneously achieves high accuracy and low communication cost. |
Chenghao Liu; Xiaoyang Qu; Jianzong Wang; Jing Xiao; |
| 444 | FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It not only sheer increases communication traffic but also potentially infringes data privacy. In this paper, we propose a new PFL algorithm called FedDWA (Federated Learning with Dynamic Weight Adjustment) to address the above problem, which leverages the parameter server (PS) to compute personalized aggregation weights based on collected models from clients. |
Jiahao Liu; Jiang Wu; Jinyu Chen; Miao Hu; Yipeng Zhou; Di Wu; |
| 445 | Open-world Semi-supervised Novel Class Discovery Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a new open-world semi-supervised novel class discovery approach named OpenNCD, a progressive bi-level contrastive learning method over multiple prototypes. |
Jiaming Liu; Yangqiming Wang; Tongze Zhang; Yulu Fan; Qinli Yang; Junming Shao; |
| 446 | Bayesian Optimization with Switching Cost: Regret Analysis and Lookahead Variants Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Tackling such problems requires a careful cost-benefit analysis of candidate locations and balancing exploration and exploitation. In this work, we formulate such a problem as a constrained Markov Decision Process (MDP) and solve it by proposing a new distance-adjusted multi-step look-ahead acquisition function, the distUCB, and using rollout approximation. |
Peng Liu; Haowei Wang; Wei Qiyu; |
| 447 | Label Enhancement Via Joint Implicit Representation Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, they suffer from biased instance relations, limited model capabilities, or suboptimal local label correlations. Therefore, in this paper, we propose a deep generative model called JRC to simultaneously learn and cluster the joint implicit representations of both features and labels, which can be used to improve any existing LE algorithm involving the instance relation or local label correlations. |
Yunan Lu; Weiwei Li; Xiuyi Jia; |
| 448 | Recognizable Information Bottleneck Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we establish the connection between the recognizability of representations and the recent functional conditional mutual information (f-CMI) generalization bound, which is significantly easier to estimate. |
Yilin Lyu; Xin Liu; Mingyang Song; Xinyue Wang; Yaxin Peng; Tieyong Zeng; Liping Jing; |
| 449 | Multi-View Robust Graph Representation Learning for Graph Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Empirically, we find that these graph classification models also suffer from semantic bias and confidence collapse issues, which substantially hinder their applicability in real-world scenarios. To address these issues, we present MGRL, a multi-view representation learning model for graph classification tasks that achieves robust results. |
Guanghui Ma; Chunming Hu; Ling Ge; Hong Zhang; |
| 450 | CTW: Confident Time-Warping for Time-Series Label-Noise Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, this paper proposes a method that expands the distribution of Confident instances by Time-Warping (CTW) to learn robust representations of time series. |
Peitian Ma; Zhen Liu; Junhao Zheng; Linghao Wang; Qianli Ma; |
| 451 | Label Specific Multi-Semantics Metric Learning for Multi-Label Classification: Global Consideration Helps Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, the first attempt towards multi-semantics metric learning for multi-label classification is investigated. |
Jun-Xiang Mao; Wei Wang; Min-Ling Zhang; |
| 452 | Doubly Stochastic Graph-based Non-autoregressive Reaction Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a new framework called ReactionSink that combines two doubly stochastic self-attention mappings to obtain electron redistribution predictions that follow both constraints. |
Ziqiao Meng; Peilin Zhao; Yang Yu; Irwin King; |
| 453 | Overlooked Implications of The Reconstruction Loss for VAE Disentanglement Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we highlight the interaction between data and the reconstruction term of the loss as the main contributor to disentanglement in VAEs. |
Nathan Michlo; Richard Klein; Steven James; |
| 454 | Social Motivation for Modelling Other Agents Under Partial Observability in Decentralised Training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Current works focus on centralised training, wherein agents are allowed to know all the information about others and the environmental state during training. In contrast, this work studies decentralised training, wherein agents must learn the model of other agents in order to cooperate with them under partially-observable conditions, even during training, i.e. learning agents are myopic. |
Dung Nguyen; Hung Le; Kien Do; Svetha Venkatesh; Truyen Tran; |
| 455 | Efficient NLP Model Finetuning Via Multistage Data Filtering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our key techniques are two: (1) automatically determine a training loss threshold for skipping backward training passes; (2) run a meta predictor for further skipping forward training passes. We integrate the above techniques in a holistic, three-stage training pro- cess. |
Xu Ouyang; Shahina Mohd Azam Ansari; Felix Xiaozhu Lin; Yangfeng Ji; |
| 456 | Mitigating Disparity While Maximizing Reward: Tight Anytime Guarantee for Improving Bandits Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our main contribution is an anytime algorithm for the IMAB problem that achieves the best possible cumulative reward while ensuring that the arms reach their true potential given sufficient time. |
Vishakha Patil; Vineet Nair; Ganesh Ghalme; Arindam Khan; |
| 457 | An Empirical Study on The Language Modal in Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper attempts to provide new insights into the influence of language modality on VQA performance from an empirical study perspective. |
Daowan Peng; Wei Wei; Xian-Ling Mao; Yuanyuan Fu; Dangyang Chen; |
| 458 | RAIN: RegulArization on Input and Network for Black-Box Domain Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel approach named RAIN (RegulArization on Input and Network) for Black-Box domain adaptation from both input-level and network-level regularization. |
Qucheng Peng; Zhengming Ding; Lingjuan Lyu; Lichao Sun; Chen Chen; |
| 459 | Linear Query Approximation Algorithms for Non-monotone Submodular Maximization Under Knapsack Constraint Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work, for the first time, introduces two constant factor approximation algorithms with linear query complexity for non-monotone submodular maximization over a ground set of size n subject to a knapsack constraint, DLA and RLA. |
Canh V. Pham; Tan D. Tran; Dung T.K. Ha; My T. Thai; |
| 460 | On Conditional and Compositional Language Model Differentiable Prompting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we investigate conditional and compositional differentiable prompting. |
Jonathan Pilault; Can Liu; Mohit Bansal; Markus Dreyer; |
| 461 | NeuPSL: Neural Probabilistic Soft Logic Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce Neural Probabilistic Soft Logic (NeuPSL), a novel neuro-symbolic (NeSy) framework that unites state-of-the-art symbolic reasoning with the low-level perception of deep neural networks. |
Connor Pryor; Charles Dickens; Eriq Augustine; Alon Albalak; William Yang Wang; Lise Getoor; |
| 462 | FedSampling: A Better Sampling Strategy for Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, instead of client uniform sampling, we propose a novel data uniform sampling strategy for federated learning (FedSampling), which can effectively improve the performance of federated learning especially when client data size distribution is highly imbalanced across clients. |
Tao Qi; Fangzhao Wu; Lingjuan Lyu; Yongfeng Huang; Xing Xie; |
| 463 | Efficient Online Decision Tree Learning with Active Feature Acquisition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, in many real world applications, both feature values and the labels are unknown a priori and can only be obtained at a cost. For example, in medical diagnosis, doctors have to choose which tests to perform (i.e., making costly feature queries) on a patient in order to make a diagnosis decision (i.e., predicting labels). We provide a fresh perspective to tackle this practical challenge. |
Arman Rahbar; Ziyu Ye; Yuxin Chen; Morteza Haghir Chehreghani; |
| 464 | Some Might Say All You Need Is Sum Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It is desired that a GNN’s usability will not be limited to graphs of any specific size. Therefore, we explore the realm of unrestricted-size expressivity. We prove that basic functions, which can be computed exactly by Mean or Max GNNs, are inapproximable by any Sum GNN. |
Eran Rosenbluth; Jan Tönshoff; Martin Grohe; |
| 465 | Sample Efficient Model-free Reinforcement Learning from LTL Specifications with Optimality Guarantees Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a model-free Reinforcement Learning (RL) approach that efficiently learns an optimal policy for an unknown stochastic system, modelled using Markov Decision Processes (MDPs). |
Daqian Shao; Marta Kwiatkowska; |
| 466 | Graph-based Semi-supervised Local Clustering with Few Labeled Nodes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As the local structure is usually small in size compared to the entire graph, one can think of it as a compressive sensing problem where the indices of target cluster can be thought as a sparse solution to a linear system. In this paper, we apply this idea based on two pioneering works under the same framework and propose a new semi-supervised local clustering approach using only few labeled nodes. |
Zhaiming Shen; Ming-Jun Lai; Sheng Li; |
| 467 | Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, such co-training models tend to converge early to a consensus, degenerating to the self-training ones, and produce low-confidence pseudo labels from the perturbed inputs during training. To address these issues, we propose an Uncertainty-guided Collaborative Mean-Teacher (UCMT) for semi-supervised semantic segmentation with the high-confidence pseudo labels. |
Zhiqiang Shen; Peng Cao; Hua Yang; Xiaoli Liu; Jinzhu Yang; Osmar R. Zaiane; |
| 468 | Unreliable Partial Label Learning with Recursive Separation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the issue, we propose a two-stage framework named Unreliable Partial Label Learning with Recursive Separation (UPLLRS). |
Yu Shi; Ning Xu; Hua Yuan; Xin Geng; |
| 469 | Guide to Control: Offline Hierarchical Reinforcement Learning Using Subgoal Generation for Long-Horizon and Sparse-Reward Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose an offline hierarchical RL method, Guider (Guide to Control), that can efficiently solve long-horizon and sparse-reward tasks from offline data. |
Wonchul Shin; Yusung Kim; |
| 470 | MA2CL:Masked Attentive Contrastive Learning for Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose an effective framework called Multi-Agent Masked Attentive Contrastive Learning (MA2CL), which encourages learning representation to be both temporal and agent-level predictive by reconstructing the masked agent observation in latent space. |
Haolin Song; Mingxiao Feng; Wengang Zhou; Houqiang Li; |
| 471 | Handling Learnwares Developed from Heterogeneous Feature Spaces Without Auxiliary Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a general framework for accommodating heterogeneous learnwares without requiring additional auxiliary data. |
Peng Tan; Zhi-Hao Tan; Yuan Jiang; Zhi-Hua Zhou; |
| 472 | Improving Heterogeneous Model Reuse By Density Estimation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Upon existing works, we address a challenging problem of heterogeneous model reuse from a decision theory perspective and take advantage of recent advances in density estimation. |
Anke Tang; Yong Luo; Han Hu; Fengxiang He; Kehua Su; Bo Du; Yixin Chen; Dacheng Tao; |
| 473 | Spike Count Maximization for Neuromorphic Vision Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a Spike Count Maximization (SCM) training approach for the SNN-based neuromorphic vision recognition model based on optimizing the output spike counts. |
Jianxiong Tang; Jian-Huang Lai; Xiaohua Xie; Lingxiao Yang; |
| 474 | Competitive-Cooperative Multi-Agent Reinforcement Learning for Auction-based Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, they cannot support a single data owner to join multiple data consumers simultaneously. To bridge these gaps, we propose the Multi-Agent Reinforcement Learning for AFL (MARL-AFL) approach to steer data consumers to bid strategically towards an equilibrium with desirable overall system characteristics. |
Xiaoli Tang; Han Yu; |
| 475 | Calibrating A Deep Neural Network with Its Predecessors Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we study the limitions of early stopping and comprehensively analyze the overfitting problem of a network considering each individual block. |
Linwei Tao; Minjing Dong; Daochang Liu; Changming Sun; Chang Xu; |
| 476 | One Model, Any CSP: Graph Neural Networks As Fast Global Search Heuristics for Constraint Satisfaction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a universal Graph Neural Network architecture which can be trained as an end-2-end search heuristic for any Constraint Satisfaction Problem (CSP). |
Jan Tönshoff; Berke Kisin; Jakob Lindner; Martin Grohe; |
| 477 | DEIR: Efficient and Robust Exploration Through Discriminative-Model-Based Episodic Intrinsic Rewards Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To evaluate exploratory behaviors accurately, we propose DEIR, a novel method in which we theoretically derive an intrinsic reward with a conditional mutual information term that principally scales with the novelty contributed by agent explorations, and then implement the reward with a discriminative forward model. |
Shanchuan Wan; Yujin Tang; Yingtao Tian; Tomoyuki Kaneko; |
| 478 | DeLELSTM: Decomposition-based Linear Explainable LSTM to Capture Instantaneous and Long-term Effects in Time Series Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Decomposition-based Linear Explainable LSTM (DeLELSTM) to improve the interpretability of LSTM. |
Chaoqun Wang; Yijun Li; Xiangqian Sun; Qi Wu; Dongdong Wang; Zhixiang Huang; |
| 479 | Deep Partial Multi-Label Learning with Graph Disambiguation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, we observe that existing graph-based PML methods typically adopt linear multi-label classifiers and thus fail to achieve superior performance. In this work, we attempt to remove several obstacles for extending them to deep models and propose a novel deep Partial multi-Label model with grAph-disambIguatioN (PLAIN). |
Haobo Wang; Shisong Yang; Gengyu Lyu; Weiwei Liu; Tianlei Hu; Ke Chen; Songhe Feng; Gang Chen; |
| 480 | Context-Aware Feature Selection and Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a joint model that performs instance-level feature selection and classification. |
Juanyan Wang; Mustafa Bilgic; |
| 481 | From Association to Generation: Text-only Captioning By Unsupervised Cross-modal Mapping Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose the K-nearest-neighbor Cross-modality Mapping (Knight), a zero-shot method from association to generation. |
Junyang Wang; Ming Yan; Yi Zhang; Jitao Sang; |
| 482 | Multi-objective Optimization-based Selection for Quality-Diversity By Non-surrounded-dominated Sorting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent studies have treated each type of behavior of a solution as an objective, and selected the parent solutions based on Multi-objective Optimization (MO), which is a natural idea, but has not lead to satisfactory performance as expected. This paper gives the reason for the first time, and then proposes a new MO-based selection method by non-surrounded-dominated sorting (NSS), which considers all possible directions of the behaviors, and thus can generate diverse solutions over the whole behavior space. |
Ren-Jian Wang; Ke Xue; Haopu Shang; Chao Qian; Haobo Fu; Qiang Fu; |
| 483 | FedBFPT: An Efficient Federated Learning Framework for Bert Further Pre-training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study proposes FEDBFPT (Federated BERT Further Pre-Training), a Federated Learning (FL) framework for further pre-training the BERT language model in specialized domains while addressing privacy concerns. |
Xin’ao Wang; Huan Li; Ke Chen; Lidan Shou; |
| 484 | Contrastive Label Enhancement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: They typically overlook the fact that both features and logical labels are descriptions of the instance from different views. Therefore, we propose a novel method called Contrastive Label Enhancement (ConLE) which integrates features and logical labels into the unified projection space to generate high-level features by contrastive learning strategy. |
Yifei Wang; Yiyang Zhou; Jihua Zhu; Xinyuan Liu; Wenbiao Yan; Zhiqiang Tian; |
| 485 | Scalable Optimal Margin Distribution Machine Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a scalable ODM, which can achieve nearly ten times speedup compared to the original ODM training method. |
Yilin Wang; Nan Cao; Teng Zhang; Xuanhua Shi; Hai Jin; |
| 486 | C-TPE: Tree-structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Hyperparameter optimization (HPO) is crucial for strong performance of deep learning algorithms and real-world applications often impose some constraints, such as memory usage, or latency on top of the performance requirement. In this work, we propose constrained TPE (c-TPE), an extension of the widely-used versatile Bayesian optimization method, tree-structured Parzen estimator (TPE), to handle these constraints. |
Shuhei Watanabe; Frank Hutter; |
| 487 | Speeding Up Multi-Objective Hyperparameter Optimization By Task Similarity-Based Meta-Learning for The Tree-Structured Parzen Estimator Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we extend TPE’s acquisition function to the meta-learning setting using a task similarity defined by the overlap of top domains between tasks. |
Shuhei Watanabe; Noor Awad; Masaki Onishi; Frank Hutter; |
| 488 | PED-ANOVA: Efficiently Quantifying Hyperparameter Importance in Arbitrary Subspaces Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the original f-ANOVA formulation is inapplicable to the subspaces most relevant to algorithm designers, such as those defined by top performance. To overcome this issue, we derive a novel formulation of f-ANOVA for arbitrary subspaces and propose an algorithm that uses Pearson divergence (PED) to enable a closed-form calculation of HPI. |
Shuhei Watanabe; Archit Bansal; Frank Hutter; |
| 489 | Generalization Bounds for Adversarial Metric Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite rapid progress in validating its effectiveness empirically, theoretical guarantees on adversarial robustness and generalization are far less understood. To fill this gap, this paper focuses on unveiling the generalization properties of adversarial metric learning by developing the uniform convergence analysis techniques. |
Wen Wen; Han Li; Hong Chen; Rui Wu; Lingjuan Wu; Liangxuan Zhu; |
| 490 | More for Less: Safe Policy Improvement with Stronger Performance Guarantees Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: State-of-the-art approaches to SPI require a high number of samples to provide practical probabilistic guarantees on the improved policy’s performance. We present a novel approach to the SPI problem that provides the means to require less data for such guarantees. |
Patrick Wienhöft; Marnix Suilen; Thiago D. Simão; Clemens Dubslaff; Christel Baier; Nils Jansen; |
| 491 | Not Only Pairwise Relationships: Fine-Grained Relational Modeling for Multivariate Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present ReMo, a Relational Modeling-based method, to promote fine-grained relational learning among multivariate time series data. |
Jinming Wu; Qi Qi; Jingyu Wang; Haifeng Sun; Zhikang Wu; Zirui Zhuang; Jianxin Liao; |
| 492 | FedNoRo: Towards Noise-Robust Federated Learning By Addressing Class Imbalance and Label Noise Heterogeneity Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we first formulate a new and more realistic federated label noise problem where global data is class-imbalanced and label noise is heterogeneous, and then propose a two-stage framework named FedNoRo for noise-robust federated learning. |
Nannan Wu; Li Yu; Xuefeng Jiang; Kwang-Ting Cheng; Zengqiang Yan; |
| 493 | Singularformer: Learning to Decompose Self-Attention to Linearize The Complexity of Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Transformer variant (Singularformer) that uses neural networks to learn the singular value decomposition process of the attention matrix to design a linear-complexity and memory-efficient global self-attention mechanism. |
Yifan Wu; Shichao Kan; Min Zeng; Min Li; |
| 494 | ProMix: Combating Label Noise Via Maximizing Clean Sample Utility Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Key to our method, we propose a matched high confidence selection technique that selects those examples with high confidence scores and matched predictions with given labels to dynamically expand a base clean sample set. |
Ruixuan Xiao; Yiwen Dong; Haobo Wang; Lei Feng; Runze Wu; Gang Chen; Junbo Zhao; |
| 495 | Violin: Virtual Overbridge Linking for Enhancing Semi-supervised Learning on Graphs with Limited Labels Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unlabeled data with useful information are usually under-exploited, which limits the representation power of GNNs. To handle these problems, we propose Virtual Overbridge Linking (Violin), a generic framework to enhance the learning capacity of common GNNs. |
Siyue Xie; Da Sun Handason Tam; Wing Cheong Lau; |
| 496 | Distilling Universal and Joint Knowledge for Cross-Domain Model Compression on Time Series Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In particular, we propose to transfer both the universal feature-level knowledge across source and target domains and the joint logit-level knowledge shared by both domains from the teacher to the student model via an adversarial learning scheme. |
Qing Xu; Min Wu; Xiaoli Li; Kezhi Mao; Zhenghua Chen; |
| 497 | Expanding The Hyperbolic Kernels: A Curvature-aware Isometric Embedding View Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a curvature-aware isometric embedding, which establishes an isometry from the Poincar\’e model to a special reproducing kernel Hilbert space (RKHS). |
Meimei Yang; Pengfei Fang; Hui Xue; |
| 498 | BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The challenge of this problem lies in the opaque feedback between reward budget allocation and model utility improvement of FL, making the optimal reward budget allocation complicated. To address this problem, we design an online reward budget allocation algorithm using Bayesian optimization named BARA (Budget Allocation for Reverse Auction). |
Yunchao Yang; Yipeng Zhou; Miao Hu; Di Wu; Quan Z. Sheng; |
| 499 | Generalized Discriminative Deep Non-Negative Matrix Factorization Based on Latent Feature and Basis Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To overcome the limitations, a novel method called Generalized Deep Non-negative Matrix Factorization (GDNMF) is proposed, which generalizes several NMF and deep NMF methods in a unified framework. |
Zijian Yang; Zhiwei Li; Lu Sun; |
| 500 | Multi-Task Learning Via Time-Aware Neural ODE Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Many MTL methods try to mitigate this problem by dynamically weighting task losses or manipulating task gradients. Different from existing studies, in this paper, we propose a Neural Ordinal diffeRential equation based Multi-tAsk Learning (NORMAL) method to alleviate this issue by modeling task-specific feature transformations from the perspective of dynamic flows built on the Neural Ordinary Differential Equation (NODE). |
Feiyang Ye; Xuehao Wang; Yu Zhang; Ivor W. Tsang; |
| 501 | LGI-GT: Graph Transformers with Local and Global Operators Interleaving Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel graph Transformer with local and global operators interleaving (LGI-GT), in which we further design a new method propagating embeddings of the [CLS] token for global information representation. |
Shuo Yin; Guoqiang Zhong; |
| 502 | On The Reuse Bias in Off-Policy Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We further provide a high-probability upper bound of the Reuse Bias and show that controlling one term of the upper bound can control the Reuse Bias by introducing the concept of stability for off-policy algorithms. Based on these analyses, we present a novel yet simple Bias-Regularized Importance Sampling (BIRIS) framework along with practical algorithms, which can alleviate the negative impact of the Reuse Bias, and show that our BIRIS can significantly reduce the Reuse Bias empirically. |
Chengyang Ying; Zhongkai Hao; Xinning Zhou; Hang Su; Dong Yan; Jun Zhu; |
| 503 | Adversarial Amendment Is The Only Force Capable of Transforming An Enemy Into A Friend Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents an opposite perspective: adversarial attacks can be harnessed to improve neural models if amended correctly. |
Chong Yu; Tao Chen; Zhongxue Gan; |
| 504 | CLE-ViT: Contrastive Learning Encoded Transformer for Ultra-Fine-Grained Visual Categorization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, this paper introduces CLE-ViT, a novel contrastive learning encoded transformer, to address the fundamental problem in ultra-FGVC. |
Xiaohan Yu; Jun Wang; Yongsheng Gao; |
| 505 | Explainable Reinforcement Learning Via A Causal World Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we develop a novel framework for explainable RL by learning a causal world model without prior knowledge of the causal structure of the environment. |
Zhongwei Yu; Jingqing Ruan; Dengpeng Xing; |
| 506 | Hierarchical State Abstraction Based on Structural Information Principles Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this article, we propose a novel mathematical Structural Information principles-based State Abstraction framework, namely SISA, from the information-theoretic perspective. |
Xianghua Zeng; Hao Peng; Angsheng Li; Chunyang Liu; Lifang He; Philip S. Yu; |
| 507 | Dual Personalization on Federated Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a novel Personalized Federated Recommendation (PFedRec) framework to learn many user-specific lightweight models to be deployed on smart devices rather than a heavyweight model on a server. |
Chunxu Zhang; Guodong Long; Tianyi Zhou; Peng Yan; Zijian Zhang; Chengqi Zhang; Bo Yang; |
| 508 | Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Unlike recent model poisoning attacks that optimize the amplitude of malicious perturbations along certain prescribed directions to cause DoS, we propose a flexible model poisoning attack (FMPA) that can achieve versatile attack goals. |
Hangtao Zhang; Zeming Yao; Leo Yu Zhang; Shengshan Hu; Chao Chen; Alan Liew; Zhetao Li; |
| 509 | G2Pxy: Generative Open-Set Node Classification on Graphs with Proxy Unknowns Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel generative open-set node classification method, i.e., G2Pxy, which follows a stricter inductive learning setting where no information about unknown classes is available during training and validation. |
Qin Zhang; Zelin Shi; Xiaolin Zhang; Xiaojun Chen; Philippe Fournier-Viger; Shirui Pan; |
| 510 | Learning to Binarize Continuous Features for Neuro-Rule Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose AutoInt, an approach that automatically binarizes continuous features and enables the intervals to be optimized with NRNs in an end-to-end fashion. |
Wei Zhang; Yongxiang Liu; Zhuo Wang; Jianyong Wang; |
| 511 | SSML-QNet: Scale-Separative Metric Learning Quadruplet Network for Multi-modal Image Patch Matching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a Scale-Separative Metric Learning Quadruplet network (SSML-QNet) for multi-modal image patch matching. |
Xiuwei Zhang; Yi Sun; Yamin Han; Yanping Li; Hanlin Yin; Yinghui Xing; Yanning Zhang; |
| 512 | Communication-Efficient Stochastic Gradient Descent Ascent with Momentum Algorithms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the compressed momentum makes it considerably challenging to investigate the convergence rate of our algorithms, especially in the presence of the interaction between the minimization and maximization subproblems. In this paper, we successfully addressed these challenges and established the convergence rate of our algorithms for nonconvex-strongly-concave problems. |
Yihan Zhang; Meikang Qiu; Hongchang Gao; |
| 513 | Multi-level Graph Contrastive Prototypical Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a novel Multi-Level Graph Contrastive Prototypical Clustering (MLG-CPC) framework for end-to-end clustering. |
Yuchao Zhang; Yuan Yuan; Qi Wang; |
| 514 | Adaptive Reward Shifting Based on Behavior Proximity for Offline Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: One of the major challenges of the current offline reinforcement learning research is to deal with the distribution shift problem due to the change in state-action visitations for the new policy. To address this issue, we present a novel reward shifting-based method. |
Zhe Zhang; Xiaoyang Tan; |
| 515 | Unbiased Gradient Boosting Decision Tree with Unbiased Feature Importance Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Based on the analysis, we propose unbiased gain, a new unbiased measurement of gain importance using out-of-bag samples. |
Zheyu Zhang; Tianping Zhang; Jian Li; |
| 516 | DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose the differentially private multi-agent communication (DPMAC) algorithm, which protects the sensitive information of individual agents by equipping each agent with a local message sender with rigorous (epsilon, delta)-differential privacy (DP) guarantee. |
Canzhe Zhao; Yanjie Ze; Jing Dong; Baoxiang Wang; Shuai Li; |
| 517 | LGPConv: Learnable Gaussian Perturbation Convolution for Lightweight Pansharpening Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Current methods use CNNs with standard convolution, but we’ve observed strong correlation among channel dimensions in the kernel, leading to computational burden and redundancy. To address this, we propose Learnable Gaussian Perturbation Convolution (LGPConv), surpassing standard convolution. |
Chen-Yu Zhao; Tian-Jing Zhang; Ran Ran; Zhi-Xuan Chen; Liang-Jian Deng; |
| 518 | Graph Neural Convection-Diffusion with Heterophily Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel GNN that incorporates the principle of heterophily by modeling the flow of information on nodes using the convection-diffusion equation (CDE). |
Kai Zhao; Qiyu Kang; Yang Song; Rui She; Sijie Wang; Wee Peng Tay; |
| 519 | Reducing Communication for Split Learning By Randomized Top-k Sparsification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate multiple communication reduction methods for split learning, including cut layer size reduction, top-k sparsification, quantization, and L1 regularization. |
Fei Zheng; Chaochao Chen; Lingjuan Lyu; Binhui Yao; |
| 520 | MAT: Mixed-Strategy Game of Adversarial Training in Fine-tuning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In order to push the performance boundaries, we propose a novel Mixed-strategy Adversarial Training algorithm (MAT). |
Zhehua Zhong; Tianyi Chen; Zhen Wang; |
| 521 | PTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Due to the fact that the probability distributions cannot be averaged over different models straightforwardly, the current time series model ensemble methods cannot be directly applied to improve the robustness and accuracy of forecasting. To address this issue, we propose pTSE, a multi-model distribution ensemble method for probabilistic forecasting based on Hidden Markov Model (HMM). |
Yunyi Zhou; Zhixuan Chu; Yijia Ruan; Ge Jin; Yuchen Huang; Sheng Li; |
| 522 | Towards Long-delayed Sparsity: Learning A Better Transformer Through Reward Redistribution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This delay causes an unwanted bias cumulating in autoregressive learning global signals. In this paper, we focused its virtual example on episodic reinforcement learning with trajectory feedback. |
Tianchen Zhu; Yue Qiu; Haoyi Zhou; Jianxin Li; |
| 523 | Hierarchical Transformer for Scalable Graph Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Additionally, conventional sampling-based methods fail to capture necessary high-level contextual information, resulting in a significant loss of performance. In this paper, we introduce the Hierarchical Scalable Graph Transformer (HSGT) as a solution to these challenges. |
Wenhao Zhu; Tianyu Wen; Guojie Song; Xiaojun Ma; Liang Wang; |
| 524 | Causal Deep Reinforcement Learning Using Observational Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, observational data may mislead the learning agent to undesirable outcomes if the behavior policy that generates the data depends on unobserved random variables (i.e., confounders). In this paper, we propose two deconfounding methods in DRL to address this problem. |
Wenxuan Zhu; Chao Yu; Qiang Zhang; |
| 525 | Prediction with Incomplete Data Under Agnostic Mask Distribution Shift Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we consider prediction with incomplete data in the presence of distribution shift. |
Yichen Zhu; Jian Yuan; Bo Jiang; Tao Lin; Haiming Jin; Xinbing Wang; Chenghu Zhou; |
| 526 | Graph Sampling-based Meta-Learning for Molecular Property Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To effectively utilize many-to-many correlations of molecules and properties, we propose a Graph Sampling-based Meta-learning (GS-Meta) framework for few-shot molecular property prediction. |
Xiang Zhuang; Qiang Zhang; Bin Wu; Keyan Ding; Yin Fang; Huajun Chen; |
| 527 | A Noisy-Label-Learning Formulation for Immune Repertoire Classification and Disease-Associated Immune Receptor Sequence Identification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a noisy-label-learning formulation to solve the immune repertoire classification task. |
Mingcai Chen; Yu Zhao; Zhonghuang Wang; Bing He; Jianhua Yao; |
| 528 | Specifying and Testing K-Safety Properties for Machine-Learning Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we take inspiration from specifications used in formal methods, expressing functional-correctness properties by reasoning about k different executions—so-called k-safety properties. |
Maria Christakis; Hasan Ferit Eniser; Jörg Hoffmann; Adish Singla; Valentin Wüstholz; |
| 529 | A Generalized Deep Markov Random Fields Framework for Fake News Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Current methods are often fully supervised and typically employ deep neural networks (DNN) to learn implicit relevance from labeled data, ignoring explicitly shared properties (e.g., inflammatory expressions) across fake news. To address this limitation, we propose a graph-theoretic framework, called Generalized Deep Markov Random Fields Framework (GDMRFF), that inherits the capability of deep learning while at the same time exploiting the correlations among the news articles (including labeled and unlabeled data). |
Yiqi Dong; Dongxiao He; Xiaobao Wang; Yawen Li; Xiaowen Su; Di Jin; |
| 530 | StockFormer: Learning Hybrid Trading Machines with Predictive Coding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present StockFormer, a hybrid trading machine that integrates the forward modeling capabilities of predictive coding with the advantages of RL agents in policy flexibility. |
Siyu Gao; Yunbo Wang; Xiaokang Yang; |
| 531 | Pseudo-Labeling Enhanced By Privileged Information and Its Application to In Situ Sequencing Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we frame a crucial problem in spatial transcriptomics – decoding barcodes from In-Situ-Sequencing (ISS) images – as a semi-supervised object detection (SSOD) problem. |
Marzieh Haghighi; Mario C. Cruz; Erin Weisbart; Beth A. Cimini; Avtar Singh; Julia Bauman; Maria E. Lozada; Sanam L. Kavari; James T. Neal; Paul C. Blainey; Anne E. Carpenter; Shantanu Singh; |
| 532 | Relation-enhanced DETR for Component Detection in Graphic Design Reverse Engineering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Built on the current state-of-the-art DETR (DEtection TRansformer), we introduce a learnable relation matrix to model class correlations. |
Xixuan Hao; Danqing Huang; Jieru Lin; Chin-Yew Lin; |
| 533 | Sequential Attention Source Identification Based on Feature Representation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: So these methods have a decreased accuracy in heterogeneous interaction scenarios. To solve this critical issue, this paper proposes a sequence-to-sequence based localization framework called Temporal-sequence based Graph Attention Source Identification (TGASI) based on an inductive learning idea. |
Dongpeng Hou; Zhen Wang; Chao Gao; Xuelong Li; |
| 534 | Differentially Private Partial Set Cover with Applications to Facility Location Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Under differential privacy, it has been proved that the Set Cover problem has strong impossibility results and no explicit forms of the output can be released to the public. In this work, we observe that these hardness results dissolve when we turn to the Partial Set Cover problem, where we only need to cover a ρ ∈ (0,1) fraction of the elements. |
George Z. Li; Dung Nguyen; Anil Vullikanti; |
| 535 | Voice Guard: Protecting Voice Privacy with Strong and Imperceptible Adversarial Perturbation in The Time Domain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the Voice Guard defense method, which uses a novel method to advance the adversarial perturbation to the time domain to avoid the loss caused by cross-domain conversion. |
Jingyang Li; Dengpan Ye; Long Tang; Chuanxi Chen; Shengshan Hu; |
| 536 | GLPocket: A Multi-Scale Representation Learning Approach for Protein Binding Site Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, this scheme only performs feature extraction for single-scale samples, which may bring the loss of global or local information, resulting in incomplete, artifacted or even missed predictions. To tackle this issue, we propose a network called GLPocket, which is based on the Lmser (Least mean square error reconstruction) network and utilizes multi-scale representation to predict binding sites. |
Peiying Li; Yongchang Liu; Shikui Tu; Lei Xu; |
| 537 | Multi-view Contrastive Learning Hypergraph Neural Network for Drug-Microbe-Disease Association Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To mitigate the limitation, we propose a Multi-view Contrastive Learning Hypergraph Neural Network, named MCHNN, for DMD association prediction. |
Luotao Liu; Feng Huang; Xuan Liu; Zhankun Xiong; Menglu Li; Congzhi Song; Wen Zhang; |
| 538 | Robust Steganography Without Embedding Based on Secure Container Synthesis and Iterative Message Recovery Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing methods suffer from weak message recovery robustness, synthesis fidelity, and the risk of message leakage. To address these problems, we propose a novel robust steganography without embedding method in this paper. |
Ziping Ma; Yuesheng Zhu; Guibo Luo; Xiyao Liu; Gerald Schaefer; Hui Fang; |
| 539 | Choosing Well Your Opponents: How to Guide The Synthesis of Programmatic Strategies Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces Local Learner (2L), an algorithm for providing a set of reference strategies to guide the search for programmatic strategies in two-player zero-sum games. |
Rubens O. Moraes; David S. Aleixo; Lucas N. Ferreira; Levi H. S. Lelis; |
| 540 | Toward Convex Manifolds: A Geometric Perspective for Deep Graph Clustering of Single-cell RNA-seq Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this context, existing methods perform euclidean-based embedding clustering without ensuring the flatness and convexity of the latent manifolds. To address this problem, we incorporate two mechanisms. |
Nairouz Mrabah; Mohamed Mahmoud Amar; Mohamed Bouguessa; Abdoulaye Banire Diallo; |
| 541 | Unveiling Concepts Learned By A World-Class Chess-Playing Agent Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Using a world-class superhuman-strength chess-playing engine as our testbed, we show how recent model probing interpretability techniques can shed light on concepts learned by the engine’s NN. |
Aðalsteinn Pálsson; Yngvi Björnsson; |
| 542 | Revisiting The Evaluation of Deep Learning-Based Compiler Testing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study revisits the evaluation of DLGs, and proposes a new, fair, simple yet strong baseline named Kitten for evaluating DLGs. |
Yongqiang Tian; Zhenyang Xu; Yiwen Dong; Chengnian Sun; Shing-Chi Cheung; |
| 543 | Transferable Curricula Through Difficulty Conditioned Generators Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a method named Parameterized Environment Response Model (PERM) that shows promising results in training RL agents in parameterized environments. |
Sidney Tio; Pradeep Varakantham; |
| 544 | JEPOO: Highly Accurate Joint Estimation of Pitch, Onset and Offset for Music Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a highly accurate method for joint estimation of pitch, onset and offset, named JEPOO. |
Haojie Wei; Jun Yuan; Rui Zhang; Yueguo Chen; Gang Wang; |
| 545 | HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and Regime-Switch VAE Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, it is still an open question to build a factor model that can conduct stock prediction in an online and adaptive setting, where the model can adapt itself to match the current market regime identified based on only point-in-time market information. To tackle this problem, we propose the first deep learning based online and adaptive factor model, HireVAE, at the core of which is a hierarchical latent space that embeds the underlying relationship between the market situation and stock-wise latent factors, so that HireVAE can effectively estimate useful latent factors given only historical market information and subsequently predict accurate stock returns. |
Zikai Wei; Anyi Rao; Bo Dai; Dahua Lin; |
| 546 | A Diffusion Model with Contrastive Learning for ICU False Arrhythmia Alarm Reduction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a deep generative approach based on the conditional denoising diffusion model to detect false arrhythmia alarms in the ICUs. |
Feng Wu; Guoshuai Zhao; Xueming Qian; Li-wei H. Lehman; |
| 547 | VecoCare: Visit Sequences-Clinical Notes Joint Learning for Diagnosis Prediction in Healthcare Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, synthesizing rich information from both of them still needs to be explored. Firstly, the heterogeneous semantic biases across them heavily hinder the synthesis of representation spaces, which is critical for diagnosis prediction. Secondly, the intermingled quality of partial clinical notes leads to inadequate representations of to-be-predicted patients. Thirdly, typical attention mechanisms mainly focus on aggregating information from similar patients, ignoring important auxiliary information from others. To tackle these challenges, we propose a novel visit sequences-clinical notes joint learning approach, dubbed VecoCare. |
Yongxin Xu; Kai Yang; Chaohe Zhang; Peinie Zou; Zhiyuan Wang; Hongxin Ding; Junfeng Zhao; Yasha Wang; Bing Xie; |
| 548 | Spotlight News Driven Quantitative Trading Based on Trajectory Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel NQT framework SpotlightTrader based on decision trajectory optimization, which can effectively stitch together a continuous and flexible sequence of trading decisions to maximize profits. |
Mengyuan Yang; Mengying Zhu; Qianqiao Liang; Xiaolin Zheng; MengHan Wang; |
| 549 | GPMO: Gradient Perturbation-Based Contrastive Learning for Molecule Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Hence, in this work, we propose a molecule optimization method called GPMO, which leverages a gradient perturbation-based contrastive learning method to prevent the “exposure bias” problem in translation-based molecule optimization. |
Xixi Yang; Li Fu; Yafeng Deng; Yuansheng Liu; Dongsheng Cao; Xiangxiang Zeng; |
| 550 | InitLight: Initial Model Generation for Traffic Signal Control Using Adversarial Inverse Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Due to repetitive trial-and-error style interactions between agents and a fixed traffic environment during the policy learning, existing Reinforcement Learning (RL)-based Traffic Signal Control (TSC) methods greatly suffer from long RL training time and poor adaptability of RL agents to other complex traffic environments. To address these problems, we propose a novel Adversarial Inverse Reinforcement Learning (AIRL)-based pre-training method named InitLight, which enables effective initial model generation for TSC agents. |
Yutong Ye; Yingbo Zhou; Jiepin Ding; Ting Wang; Mingsong Chen; Xiang Lian; |
| 551 | Don’t Ignore Alienation and Marginalization: Correlating Fraud Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on the finding, we propose COFRAUD, a correlation-aware fraud detection model, which innovatively incorporates synergistic camouflage into fraud detection. |
Yilong Zang; Ruimin Hu; Zheng Wang; Danni Xu; Jia Wu; Dengshi Li; Junhang Wu; Lingfei Ren; |
| 552 | Realistic Cell Type Annotation and Discovery for Single-cell RNA-seq Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this task, cells from seen cell types are given class labels, while cells from novel cell types are given cluster labels. To tackle this problem, we propose an end-to-end algorithm framework called scPOT from the perspective of optimal transport (OT). |
Yuyao Zhai; Liang Chen; Minghua Deng; |
| 553 | Towards Generalizable Reinforcement Learning for Trade Execution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we provide an extensive study on the overfitting problem in optimized trade execution. |
Chuheng Zhang; Yitong Duan; Xiaoyu Chen; Jianyu Chen; Jian Li; Li Zhao; |
| 554 | SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein–Protein Interaction Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a self-ensembling multi-graph neural network (SemiGNN-PPI) that can effectively predict PPIs while being both efficient and generalizable. |
Ziyuan Zhao; Peisheng Qian; Xulei Yang; Zeng Zeng; Cuntai Guan; Wai Leong Tam; Xiaoli Li; |
| 555 | Deep Hashing-based Dynamic Stock Correlation Estimation Via Normalizing Flow Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel hash-based dynamic correlation forecasting model (HDCF) to estimate dynamic stock correlations. |
Xiaolin Zheng; Mengpu Liu; Mengying Zhu; |
| 556 | MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The primary challenge to hierarchical generation is the non-differentiable issue caused by the generation of intermediate discrete coarsened graph structures. To sidestep this issue, we cast the tricky hierarchical generation problem over discrete spaces as the reverse process of hierarchical representation learning and propose MolHF, a new hierarchical flow-based model that generates molecular graphs in a coarse-to-fine manner. |
Yiheng Zhu; Zhenqiu Ouyang; Ben Liao; Jialu Wu; Yixuan Wu; Chang-Yu Hsieh; Tingjun Hou; Jian Wu; |
| 557 | Keep Skills in Mind: Understanding and Implementing Skills in Commonsense Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a new approach named Dynamic Skill-aware Commonsense Question Answering (DSCQA), which transcends the limitations of traditional methods by informing the model about the need for each skill in questions and utilizes skills as a critical driver in CQA process. |
Meikai Bao; Qi Liu; Kai Zhang; Ye Liu; Linan Yue; Longfei Li; Jun Zhou; |
| 558 | An Effective and Efficient Time-aware Entity Alignment Framework Via Two-aspect Three-view Label Propagation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an effective and efficient non-neural EA framework between TKGs, namely LightTEA, which consists of four essential components: (1) Two-aspect Three-view Label Propagation, (2) Sparse Similarity with Temporal Constraints, (3) Sinkhorn Operator, and (4) Temporal Iterative Learning. |
Li Cai; Xin Mao; Youshao Xiao; Changxu Wu; Man Lan; |
| 559 | One Model for All Domains: Collaborative Domain-Prefix Tuning for Cross-Domain NER Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, current models only focus on leveraging knowledge in one general source domain while failing to successfully transfer knowledge from multiple sources to the target. To address these issues, we introduce Collaborative Domain-Prefix Tuning for cross-domain NER (CP-NER) based on text-to-text generative PLMs. |
Xiang Chen; Lei Li; Shuofei Qiao; Ningyu Zhang; Chuanqi Tan; Yong Jiang; Fei Huang; Huajun Chen; |
| 560 | Less Learn Shortcut: Analyzing and Mitigating Learning of Spurious Feature-Label Correlation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we focus on the spurious correlation between word features and labels that models learn from the biased data distribution of training data. |
Yanrui Du; Jing Yan; Yan Chen; Jing Liu; Sendong Zhao; Qiaoqiao She; Hua Wu; Haifeng Wang; Bing Qin; |
| 561 | KEST: Kernel Distance Based Efficient Self-Training for Improving Controllable Text Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Nevertheless, in facilitating semi-supervised controllable language generation, ST faces two key challenges. First, augmented by self-generated pseudo text, generation models tend to over-exploit the previously learned text distribution, suffering from mode collapse and poor generation diversity. Second, generating pseudo text in each iteration is time-consuming, severely decelerating the training process. In this work, we propose KEST, a novel and efficient self-training framework to handle these problems. |
Yuxi Feng; Xiaoyuan Yi; Laks V.S. Lakshmanan; Xing Xie; |
| 562 | Regularisation for Efficient Softmax Parameter Generation in Low-Resource Text Classifiers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we improve on recent advances in meta-learning for natural language models that allow training on a diverse set of training tasks for few-shot, low-resource target tasks. |
Daniel Grießhaber; Johannes Maucher; Ngoc Thang Vu; |
| 563 | SmartBERT: A Promotion of Dynamic Early Exiting Mechanism for Accelerating BERT Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel dynamic early exiting combined with layer skipping for BERT inference named SmartBERT, which adds a skipping gate and an exiting operator into each layer of BERT. |
Boren Hu; Yun Zhu; Jiacheng Li; Siliang Tang; |
| 564 | Cross-Modal Global Interaction and Local Alignment for Audio-Visual Speech Recognition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a cross-modal global interaction and local alignment (GILA) approach for AVSR, which captures the deep audio-visual (A-V) correlations from both global and local perspectives. |
Yuchen Hu; Ruizhe Li; Chen Chen; Heqing Zou; Qiushi Zhu; Eng Siong Chng; |
| 565 | Explainable Text Classification Via Attentive and Targeted Mixing Data Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing methods do not control the quality of augmented data and have low model explainability. To tackle these issues, this paper proposes an explainable text classification solution based on attentive and targeted mixing data augmentation, ATMIX. |
Songhao Jiang; Yan Chu; Zhengkui Wang; Tianxing Ma; Hanlin Wang; Wenxuan Lu; Tianning Zang; Bo Wang; |
| 566 | ScriptWorld: Text Based Environment for Learning Procedural Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce ScriptWorld: a text-based environment for teaching agents about real-world daily chores and hence imparting commonsense knowledge. |
Abhinav Joshi; Areeb Ahmad; Umang Pandey; Ashutosh Modi; |
| 567 | Towards Incremental NER Data Augmentation Via Syntactic-aware Insertion Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Such uncorrelation also brings low diversity and inconsistent labeling of synthetic samples. To fill this gap, we present SAINT (Syntactic-Aware InsertioN Transformer), a hard-constraint controlled text generation model that incorporates syntactic information. |
Wenjun Ke; Zongkai Tian; Qi Liu; Peng Wang; Jinhua Gao; Rui Qi; |
| 568 | Towards Lossless Head Pruning Through Automatic Peer Distillation for Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on attention head pruning as head attention is a key component of the transformer-based language models and provides interpretable knowledge meaning. |
Bingbing Li; Zigeng Wang; Shaoyi Huang; Mikhail Bragin; Ji Li; Caiwen Ding; |
| 569 | Annealing Genetic-based Preposition Substitution for Text Rubbish Example Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the produced rubbish examples usually cause a reduction in model confidence and sometimes deliver human-readable text. To address these problems, we propose an Annealing Genetic based Preposition Substitution (AGPS) algorithm for text rubbish sample generation with two major merits. |
Chen Li; Xinghao Yang; Baodi Liu; Weifeng Liu; Honglong Chen; |
| 570 | IRe2f: Rethinking Effective Refinement in Language Structure Prediction Via Efficient Iterative Retrospecting and Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To make the refiner more practical for real-world applications, this paper proposes a lightweight but effective iterative refinement framework, iRe^2f, based on iterative retrospecting and reasoning without involving the re-encoding process on the graph. |
Zuchao Li; Xingyi Guo; Letian Peng; Lefei Zhang; Hai Zhao; |
| 571 | Local and Global: Temporal Question Answering Via Information Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the fruitful efforts of previous models in temporal KGQA, they still have several limitations. (I) They neither emphasize the graph structural information between entities in KGs nor explicitly utilize a multi-hop relation path through graph neural networks to enhance answer prediction. (II) They adopt pre-trained language models (LMs) to obtain question representations, focusing merely on the global information related to the question while not highlighting the local information of the entities in KGs. To address these limitations, we introduce a novel model that simultaneously explores both Local information and Global information for the task of temporal KGQA (LGQA). |
Yonghao Liu; Di Liang; Mengyu Li; Fausto Giunchiglia; Ximing Li; Sirui Wang; Wei Wu; Lan Huang; Xiaoyue Feng; Renchu Guan; |
| 572 | PPAT: Progressive Graph Pairwise Attention Network for Event Causality Identification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, they indiscriminately reason all event causality in the same way, ignoring that most inter-sentence event causality depends on intra-sentence event causality to infer. In this paper, we propose a progressive graph pairwise attention network (PPAT) to consider the above dependence. |
Zhenyu Liu; Baotian Hu; Zhenran Xu; Min Zhang; |
| 573 | Meta-Tsallis-Entropy Minimization: A New Self-Training Approach for Domain Adaptation on Text Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Meta-Tsallis Entropy minimization (MTEM). |
Menglong Lu; Zhen Huang; Zhiliang Tian; Yunxiang Zhao; Xuanyu Fei; Dongsheng Li; |
| 574 | ODEE: A One-Stage Object Detection Framework for Overlapping and Nested Event Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present a new approach to Event Extraction (EE) by reformulating it as an object detection task on a table of token pairs. |
Jinzhong Ning; Zhihao Yang; Zhizheng Wang; Yuanyuan Sun; Hongfei Lin; |
| 575 | Learning to Speak from Text: Zero-Shot Multilingual Text-to-Speech with Unsupervised Text Pretraining Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a method for zero-shot multilingual TTS using text-only data for the target language. |
Takaaki Saeki; Soumi Maiti; Xinjian Li; Shinji Watanabe; Shinnosuke Takamichi; Hiroshi Saruwatari; |
| 576 | Case-Based Reasoning with Language Models for Classification of Logical Fallacies Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a Case-Based Reasoning method that classifies new cases of logical fallacy by language-modeling-driven retrieval and adaptation of historical cases. |
Zhivar Sourati; Filip Ilievski; Hông-Ân Sandlin; Alain Mermoud; |
| 577 | Fine-tuned Vs. Prompt-tuned Supervised Representations: Which Better Account for Brain Language Representations? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: If so, what kind of NLU task leads a pre-trained model to better decode the information represented in the human brain? We investigate these questions by comparing prompt-tuned and fine-tuned representations in neural decoding, that is predicting the linguistic stimulus from the brain activities evoked by the stimulus. |
Jingyuan Sun; Marie-Francine Moens; |
| 578 | SQuAD-SRC: A Dataset for Multi-Accent Spoken Reading Comprehension Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we construct a large-scale multi-accent human spoken dataset SQuAD-SRC, in order to study the problem of multi-accent spoken reading comprehension. |
Yixuan Tang; Anthony K.H: Tung; |
| 579 | PasCore: A Chinese Overlapping Relation Extraction Model Based on Global Pointer Annotation Strategy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Especially, overlapping entities are common and indispensable in Chinese. To address this issue, this paper proposes PasCore, which utilizes a global pointer annotation strategy for overlapping relation extraction in Chinese. |
Peng Wang; Jiafeng Xie; Xiye Chen; Guozheng Li; Wei Li; |
| 580 | Privacy-Preserving End-to-End Spoken Language Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, the SLU system needs to ensure that a potential malicious attacker cannot deduce the sensitive attributes of the users, while it should avoid greatly compromising the SLU accuracy. To address the above challenge, this paper proposes a novel SLU multi-task privacy-preserving model to prevent both the speech recognition (ASR) and identity recognition (IR) attacks. |
Yinggui Wang; Wei Huang; Le Yang; |
| 581 | Beyond Pure Text: Summarizing Financial Reports Based on Both Textual and Tabular Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, real-world documents, e.g., financial reports, generally contain rich data such as charts and tabular data which invalidates most existing text summarization approaches. This paper is thus motivated to propose this novel approach to simultaneously summarize both textual and tabular data. |
Ziao Wang; Zelin Jiang; Xiaofeng Zhang; Jaehyeon Soon; Jialu Zhang; Wang Xiaoyao; Hongwei Du; |
| 582 | Learning Summary-Worthy Visual Representation for Abstractive Summarization in Video Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel approach to learning the summary-worthy visual representation that facilitates abstractive summarization. |
Zenan Xu; Xiaojun Meng; Yasheng Wang; Qinliang Su; Zexuan Qiu; Xin Jiang; Qun Liu; |
| 583 | TITAN : Task-oriented Dialogues with Mixed-Initiative Interactions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we construct a multi-domain task-oriented dialogue dataset with mixed-initiative strategies named TITAN from the large-scale dialogue corpus MultiWOZ 2.1. |
Sitong Yan; Shengli Song; Jingyang Li; Shiqi Meng; Guangneng Hu; |
| 584 | Efficient Sign Language Translation with A Curriculum-based Non-autoregressive Decoder Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the main disadvantage of the AR-DM is high inference latency. To address this problem, we introduce Non-AutoRegressive Decoding Mechanism (NAR-DM) into SLT, which generates the whole sentence at once. |
Pei Yu; Liang Zhang; Biao Fu; Yidong Chen; |
| 585 | Fast-StrucTexT: An Efficient Hourglass Transformer with Modality-guided Dynamic Token Merge for Document Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle the concerns, we propose Fast-StrucTexT, an efficient multi-modal framework based on the StrucTexT algorithm with an hourglass transformer architecture, for visual document understanding. |
Mingliang Zhai; Yulin Li; Xiameng Qin; Chen Yi; Qunyi Xie; Chengquan Zhang; Kun Yao; Yuwei Wu; Yunde Jia; |
| 586 | Exploring Effective Inter-Encoder Semantic Interaction for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a document-level RE model with a Graph-Transformer Network (GTN). |
Liang Zhang; Zijun Min; Jinsong Su; Pei Yu; Ante Wang; Yidong Chen; |
| 587 | NerCo: A Contrastive Learning Based Two-Stage Chinese NER Method Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As a result, different tokens in the same entity may be learned with representations that are isolated and unrelated in target representation space, which could finally negatively affect the subsequent performance of token classification. In this paper, we point out and define this problem as Entity Representation Segmentation in Label-semantics. |
Zai Zhang; Bin Shi; Haokun Zhang; Huang Xu; Yaodong Zhang; Yuefei Wu; Bo Dong; Qinghua Zheng; |
| 588 | Genetic Prompt Search Via Exploiting Language Model Probabilities Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, there are usually preconditions to apply existing DFO-based prompt tuning methods, e.g. the backbone PLM needs to provide extra APIs so that hidden states (and/or embedding vectors) can be injected into it as continuous prompts, or carefully designed (discrete) manual prompts need to be available beforehand, serving as the initial states of the tuning algorithm. To waive such preconditions and make DFO-based prompt tuning ready for general use, this paper introduces a novel genetic algorithm (GA) that evolves from empty prompts, and uses the predictive probabilities derived from the backbone PLM(s) on the basis of a (few-shot) training set to guide the token selection process during prompt mutations. |
Jiangjiang Zhao; Zhuoran Wang; Fangchun Yang; |
| 589 | Learning Few-shot Sample-set Operations for Noisy Multi-label Aspect Category Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, MACD is a high-noise task, and existing methods fail to address it with only two or three training samples per class, which limits the application in practice. To solve above issues, we propose a group of Few-shot Sample-set Operations (FSO) to solve noisy MACD in fewer sample scenarios by identifying the semantic contents of samples. |
Shiman Zhao; Wei Chen; Tengjiao Wang; |
| 590 | COOL, A Context Outlooker, and Its Application to Question Answering and Other Natural Language Processing Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present an outlook attention mechanism, COOL, for natural language processing. |
Fangyi Zhu; See-Kiong Ng; Stéphane Bressan; |
| 591 | DiSProD: Differentiable Symbolic Propagation of Distributions for Planning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The paper introduces DiSProD, an online planner developed for environments with probabilistic transitions in continuous state and action spaces. |
Palash Chatterjee; Ashutosh Chapagain; Weizhe Chen; Roni Khardon; |
| 592 | Minimizing Reachability Times on Temporal Graphs Via Shifting Labels Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study how we can accelerate the spreading of information in temporal graphs via shifting operations; a problem that captures real-world applications varying from information flows to distribution schedules. |
Argyrios Deligkas; Eduard Eiben; George Skretas; |
| 593 | On The Compilability of Bounded Numeric Planning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper extends Nebel’s framework to the setting of bounded numeric planning. |
Nicola Gigante; Enrico Scala; |
| 594 | On The Study of Curriculum Learning for Inferring Dispatching Policies on The Job Shop Scheduling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a size-agnostic model that enables us to demonstrate that current curriculum strategies have a major impact on the quality of the solution inferred. |
Zangir Iklassov; Dmitrii Medvedev; Ruben Solozabal Ochoa de Retana; Martin Takac; |
| 595 | Simulation-Assisted Optimization for Large-Scale Evacuation Planning with Congestion-Dependent Delays Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To approach it, we present MIP-LNS, a scalable optimization method that utilizes heuristic search with mathematical optimization and can optimize a variety of objective functions. |
Kazi Ashik Islam; Da Qi Chen; Madhav Marathe; Henning Mortveit; Samarth Swarup; Anil Vullikanti; |
| 596 | K∗ Search Over Orbit Space for Top-k Planning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we take a similar approach to top-k planning. |
Michael Katz; Junkyu Lee; |
| 597 | Helpful Information Sharing for Partially Informed Planning Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We offer a novel compilation of HIS to a classical planning problem, which can be solved efficiently by any off-the-shelf planner. We provide guarantees of optimality for our approach and describe its extensions to maximize robustness and support settings in which the agent needs to decide which sensors to deploy in the environment. |
Sarah Keren; David Wies; Sara Bernardini; |
| 598 | Mean Payoff Optimization for Systems of Periodic Service and Maintenance Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose randomized finite-memory (RFM) schedules as a compact description of the agent’s strategies and design an efficient algorithm for constructing RFM schedules. |
David Klaška; Antonín Kučera; Vít Musil; Vojtěch Řehák; |
| 599 | Action Space Reduction for Planning Domains Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In order to apply such methods, the label sets are often manually reduced. In this work, we propose automating this manual process. |
Harsha Kokel; Junkyu Lee; Michael Katz; Kavitha Srinivas; Shirin Sohrabi; |
| 600 | Recursive Small-Step Multi-Agent A* for Dec-POMDPs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present recursive small-step multi-agent A* (RS-MAA*), an exact algorithm that optimizes the expected reward in decentralized partially observable Markov decision processes (Dec-POMDPs). |
Wietze Koops; Nils Jansen; Sebastian Junges; Thiago D. Simão; |
| 601 | Generalization Through Diversity: Improving Unsupervised Environment Design Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To that end, we provide a principled approach to adaptively identify diverse environments based on a novel distance measure relevant to environment design. |
Wenjun Li; Pradeep Varakantham; Dexun Li; |
| 602 | Can I Really Do That? Verification of Meta-Operators Via Stackelberg Planning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce meta-operators, which allow using different sequences of actions in each state. |
Florian Pham; Alvaro Torralba; |
| 603 | Topological Planning with Post-unique and Unary Actions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We are interested in realistic planning problems to model the behavior of Non-Playable Characters (NPCs) in video games. |
Guillaume Prévost; Stéphane Cardon; Tristan Cazenave; Christophe Guettier; Éric Jacopin; |
| 604 | Model Predictive Control with Reach-avoid Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we investigate the optimal controller synthesis problem, so that the system under the controller can reach a specified target set while satisfying given constraints. |
Dejin Ren; Wanli Lu; Jidong Lv; Lijun Zhang; Bai Xue; |
| 605 | Formal Explanations of Neural Network Policies for Planning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we formulate the problem of finding explanations for the sequence of decisions recommended by a learnt policy in a given state. |
Renee Selvey; Alban Grastien; Sylvie Thiébaux; |
| 606 | Optimal Decision Tree Policies for Markov Decision Processes Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we study the optimization of size-limited decision trees for Markov Decision Processes (MPDs) and propose OMDTs: Optimal MDP Decision Trees. |
Daniël Vos; Sicco Verwer; |
| 607 | Online Task Assignment with Controllable Processing Time Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose the Online Machine and Level Assignment (OMLA) Algorithm to simultaneously assign an offline machine and a processing level to each online task. |
Ruoyu Wu; Wei Bao; Liming Ge; |
| 608 | A Rigorous Risk-aware Linear Approach to Extended Markov Ratio Decision Processes with Embedded Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a theoretical framework, Extended Markov Ratio Decision Processes (EMRDP), that incorporates risk into MDPs and embeds environment learning into this framework. |
Alexander Zadorojniy; Takayuki Osogami; Orit Davidovich; |
| 609 | Learning to Act for Perceiving in Partially Unknown Environments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: When the proper situations to perceive the desired properties are unknown, an agent needs to learn them and plan to get in such situations. In this paper, we devise a general method to solve this problem by evaluating the confidence of a neural network online and by using symbolic planning. |
Leonardo Lamanna; Mohamadreza Faridghasemnia; Alfonso Gerevini; Alessandro Saetti; Alessandro Saffiotti; Luciano Serafini; Paolo Traverso; |
| 610 | Learning to Self-Reconfigure for Freeform Modular Robots Via Altruism Proximal Policy Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we design a parallel distributed self-reconfiguration algorithm for freeform modular robots based on multi-agent reinforcement learning to realize the automatic design of conflict-free reconfiguration controllers in continuous action spaces. |
Lei Wu; Bin Guo; Qiuyun Zhang; Zhuo Sun; Jieyi Zhang; Zhiwen Yu; |
| 611 | Multi-Robot Coordination and Layout Design for Automated Warehousing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We show that, even with state-of-the-art MAPF algorithms, commonly used human-designed layouts can lead to congestion for warehouses with large numbers of robots and thus have limited scalability. |
Yulun Zhang; Matthew C. Fontaine; Varun Bhatt; Stefanos Nikolaidis; Jiaoyang Li; |
| 612 | Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: That is, the next-generation population is formed by selecting the first population-size ranked solutions (based on some selection criteria, e.g., non-dominated sorting, crowdedness and indicators) from the collections of the current population and newly-generated solutions. In this paper, we question this practice. |
Chao Bian; Yawen Zhou; Miqing Li; Chao Qian; |
| 613 | The First Proven Performance Guarantees for The Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on A Combinatorial Optimization Problem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we give the first proven performance guarantees for a classic optimization problem, the NP-complete bi-objective minimum spanning tree problem. |
Sacha Cerf; Benjamin Doerr; Benjamin Hebras; Yakob Kahane; Simon Wietheger; |
| 614 | Complex Contagion Influence Maximization: A Reinforcement Learning Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose the first reinforcement learning (RL) approach to CCIM. |
Haipeng Chen; Bryan Wilder; Wei Qiu; Bo An; Eric Rice; Milind Tambe; |
| 615 | On Optimal Strategies for Wordle and General Guessing Games Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our main contribution are several theorems that build towards a general theory to prove optimality of a strategy for a guessing game. |
Michael Cunanan; Michael Thielscher; |
| 616 | Runtime Analyses of Multi-Objective Evolutionary Algorithms in The Presence of Noise Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we conduct the first mathematical runtime analysis of a simple multi-objective evolutionary algorithm (MOEA) on a classic benchmark in the presence of noise in the objective function. |
Matthieu Dinot; Benjamin Doerr; Ulysse Hennebelle; Sebastian Will; |
| 617 | Diverse Approximations for Monotone Submodular Maximization Problems with A Matroid Constraint Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we consider the most basic variants of submodular optimization, and propose two simple greedy algorithms, which are known to be effective at maximizing monotone submodular functions. |
Anh Viet Do; Mingyu Guo; Aneta Neumann; Frank Neumann; |
| 618 | Efficient Object Search in Game Maps Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a simple lightweight index, called Grid Tree, to store objects and their associated textual data. |
Jinchun Du; Bojie Shen; Shizhe Zhao; Muhammad Aamir Cheema; Adel Nadjaran Toosi; |
| 619 | Sorting and Hypergraph Orientation Under Uncertainty with Predictions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study learning-augmented algorithms for sorting and hypergraph orientation under uncertainty, assuming access to untrusted predictions for the uncertain values. |
Thomas Erlebach; Murilo de Lima; Nicole Megow; Jens Schlöter; |
| 620 | Parameterized Local Search for Max C-Cut Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The case with c=2 is the famous Max Cut problem. To deal with the NP-hardness of this problem, we study parameterized local search algorithms. |
Jaroslav Garvardt; Niels Grüttemeier; Christian Komusiewicz; Nils Morawietz; |
| 621 | Exploring Structural Similarity in Fitness Landscapes Via Graph Data Mining: A Case Study on Number Partitioning Problems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, due to the black-box nature of combinatorial optimization, it is far from trivial to infer such similarity in real-world scenarios. To bridge this gap, by using local optima network as a proxy of fitness landscapes, this paper proposed to leverage graph data mining techniques to conduct qualitative and quantitative analyses to explore the latent topological structural information embedded in those landscapes. |
Mingyu Huang; Ke Li; |
| 622 | An Exact Algorithm for The Minimum Dominating Set Problem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel lower bound and an exact algorithm for MDS. |
Hua Jiang; Zhifei Zheng; |
| 623 | A Refined Upper Bound and Inprocessing for The Maximum K-plex Problem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a refined upper bound, which can derive a tighter upper bound than existing methods, and an inprocessing strategy, which performs graph reduction incrementally. |
Hua Jiang; Fusheng Xu; Zhifei Zheng; Bowen Wang; Wei Zhou; |
| 624 | Levin Tree Search with Context Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work we show that the neural network can be substituted with parameterized context models originating from the online compression literature (LTS+CM). |
Laurent Orseau; Marcus Hutter; Levi H. S. Lelis; |
| 625 | Front-to-End Bidirectional Heuristic Search with Consistent Heuristics: Enumerating and Evaluating Algorithms and Bounds Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent research on bidirectional heuristic search (BiHS) is based on the must-expand pairs theory (MEP theory), which describes which pairs of nodes must be expanded during the search to guarantee the optimality of solutions. A separate line of research in BiHS has proposed algorithms that use lower bounds that are derived from consistent heuristics during search. This paper links these two directions, providing a comprehensive unifying view and showing that both existing and novel algorithms can be derived from the MEP theory. |
Lior Siag; Shahaf Shperberg; Ariel Felner; Nathan Sturtevant; |
| 626 | PathLAD+: An Improved Exact Algorithm for Subgraph Isomorphism Problem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose three main heuristics and develop an improved exact algorithm for SIP. |
Yiyuan Wang; Chenghou Jin; Shaowei Cai; Qingwei Lin; |
| 627 | A Fast Maximum K-Plex Algorithm Parameterized By The Degeneracy Gap Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: So far, there is a number of empirical algorithms without sufficient theoretical explanations on the efficiency. We try to bridge this gap by defining a novel parameter of the input instance, g_k(G), the gap between the degeneracy bound and the size of maximum k-plex in the given graph, and presenting an exact algorithm parameterized by g_k(G). |
Zhengren Wang; Yi Zhou; Chunyu Luo; Mingyu Xiao; |
| 628 | A Mathematical Runtime Analysis of The Non-dominated Sorting Genetic Algorithm III (NSGA-III) Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we provide the first mathematical runtime analysis of the NSGA-III, a refinement of the NSGA-II aimed at better handling more than two objectives. |
Simon Wietheger; Benjamin Doerr; |
| 629 | Probabilistic Rule Induction from Event Sequences with Logical Summary Markov Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce logical summary Markov models, which are a family of models for event sequences that enable interpretable predictions through logical rules that relate historical predicates to the probability of observing an event type at any arbitrary position in the sequence. |
Debarun Bhattacharjya; Oktie Hassanzadeh; Ronny Luss; Keerthiram Murugesan; |
| 630 | On The Complexity of Counterfactual Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the computational complexity of counterfactual reasoning in relation to the complexity of associational and interventional reasoning on structural causal models (SCMs). |
Yunqiu Han; Yizuo Chen; Adnan Darwiche; |
| 631 | Stability and Generalization of Lp-Regularized Stochastic Learning for GCN Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper aims to quantify the trade-off of GCN between smoothness and sparsity, with the help of a general lp-regularized (1 |
Shiyu Liu; Linsen Wei; Shaogao Lv; Ming Li; |
| 632 | Approximate Inference in Logical Credal Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present ARIEL — a novel iterative message-passing scheme for approximate inference in LCNs. |
Radu Marinescu; Haifeng Qian; Alexander Gray; Debarun Bhattacharjya; Francisco Barahona; Tian Gao; Ryan Riegel; |
| 633 | Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequences Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose Structure Hawkes Processes (SHPs) that leverage the instantaneous effect for learning the causal structure among events type in discrete-time event sequence. |
Jie Qiao; Ruichu Cai; Siyu Wu; Yu Xiang; Keli Zhang; Zhifeng Hao; |
| 634 | Distributional Multi-Objective Decision Making Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel algorithm to learn the distributional undominated set and further contribute pruning operators to reduce the set to the convex distributional undominated set. |
Willem Röpke; Conor F. Hayes; Patrick Mannion; Enda Howley; Ann Nowé; Diederik M. Roijers; |
| 635 | Finding An Ε-Close Minimal Variation of Parameters in Bayesian Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on the state-of-the-art “region verification” techniques for parametric Markov chains, we propose an algorithm whose capabilities go beyond any existing techniques. |
Bahare Salmani; Joost-Pieter Katoen; |
| 636 | The Hardness of Reasoning About Probabilities and Causality Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We focus on satisfiability problems whose instance formulas allow expressing many tasks in probabilistic and causal inference. The main contribution of this work is establishing the exact computational complexity of these satisfiability problems. |
Benito van der Zander; Markus Bläser; Maciej Liśkiewicz; |
| 637 | Safe Reinforcement Learning Via Probabilistic Logic Shields Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we introduce Probabilistic Logic Policy Gradient (PLPG). |
Wen-Chi Yang; Giuseppe Marra; Gavin Rens; Luc De Raedt; |
| 638 | Quantifying Consistency and Information Loss for Causal Abstraction Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, switching between different levels of abstraction requires evaluating a trade-off between the consistency and the information loss among different models. In this paper we introduce a family of interventional measures that an agent may use to evaluate such a trade-off. |
Fabio Massimo Zennaro; Paolo Turrini; Theodoros Damoulas; |
| 639 | Max Markov Chain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce Max Markov Chain (MMC), a novel model for sequential data with sparse correlations among the state variables. |
Yu Zhang; Mitchell Bucklew; |
| 640 | Evaluating Human-AI Interaction Via Usability, User Experience and Acceptance Measures for MMM-C: A Creative AI System for Music Composition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper reports on a thorough evaluation of the user adoption of the Multi-Track Music Machine (MMM) as a minimal co-creative AI tool for music composers. |
Renaud Bougueng Tchemeube; Jeffrey Ens; Cale Plut; Philippe Pasquier; Maryam Safi; Yvan Grabit; Jean-Baptiste Rolland; |
| 641 | The ACCompanion: Combining Reactivity, Robustness, and Musical Expressivity in An Automatic Piano Accompanist Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces the ACCompanion, an expressive accompaniment system. |
Carlos Cancino-Chacón; Silvan Peter; Patricia Hu; Emmanouil Karystinaios; Florian Henkel; Francesco Foscarin; Gerhard Widmer; |
| 642 | TeSTNeRF: Text-Driven 3D Style Transfer Via Cross-Modal Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Simply combining image/video style transfer methods and novel view synthesis methods results in flickering when changing viewpoints, while existing 3D style transfer methods learn styles from images instead of texts. To address this problem, we for the first time design an efficient text-driven model for 3D style transfer, named TeSTNeRF, to stylize the scene using texts via cross-modal learning: we leverage an advanced text encoder to embed the texts in order to control 3D style transfer and align the input text and output stylized images in latent space. |
Jiafu Chen; Boyan Ji; Zhanjie Zhang; Tianyi Chu; Zhiwen Zuo; Lei Zhao; Wei Xing; Dongming Lu; |
| 643 | Graph-based Polyphonic Multitrack Music Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Nonetheless, there is a lack of works that consider graph representations in the context of deep learning systems for music generation. This paper bridges this gap by introducing a novel graph representation for music and a deep Variational Autoencoder that generates the structure and the content of musical graphs separately, one after the other, with a hierarchical architecture that matches the structural priors of music. |
Emanuele Cosenza; Andrea Valenti; Davide Bacciu; |
| 644 | Towards Symbiotic Creativity: A Methodological Approach to Compare Human and AI Robotic Dance Creations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work proposes a methodological approach for the artistic evaluation of both AI and human artistic creations in the field of robotic dance. |
Allegra De Filippo; Luca Giuliani; Eleonora Mancini; Andrea Borghesi; Paola Mello; Michela Milano; |
| 645 | Automating Rigid Origami Design Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we build upon the recently developed principle of three units method to formulate rigid origami design as a discrete optimization problem, the rigid origami game. |
Jeremia Geiger; Karolis Martinkus; Oliver Richter; Roger Wattenhofer; |
| 646 | Collaborative Neural Rendering Using Anime Character Sheets Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present the Collaborative Neural Rendering (CoNR) method, which creates new images for specified poses from a few reference images (AKA Character Sheets). |
Zuzeng Lin; Ailin Huang; Zhewei Huang; |
| 647 | IberianVoxel: Automatic Completion of Iberian Ceramics for Cultural Heritage Studies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To overcome the challenges of manual reconstruction, reduce the materials’ exposure and deterioration, and improve the quality of reconstructed samples, we present IberianVoxel, a novel 3D Autoencoder Generative Adversarial Network (3D AE-GAN) framework tested on an extensive database with complete and fragmented references. |
Pablo Navarro; Celia Cintas; Manuel Lucena; José Manuel Fuertes; Antonio Rueda; Rafael Segura; Carlos Ogayar-Anguita; Rolando González-José; Claudio Delrieux; |
| 648 | Discrete Diffusion Probabilistic Models for Symbolic Music Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work presents the direct generation of Polyphonic Symbolic Music using D3PMs. |
Matthias Plasser; Silvan Peter; Gerhard Widmer; |
| 649 | Learn and Sample Together: Collaborative Generation for Graphic Design Layout Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a two-stage generation framework: a spatial graph generator and a subsequent layout decoder which is conditioned on the previous output graph. |
Haohan Weng; Danqing Huang; Tong Zhang; Chin-Yew Lin; |
| 650 | DiffuseStyleGesture: Stylized Audio-Driven Co-Speech Gesture Generation with Diffusion Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: It is a challenging task due to the diversity of gestures and the difficulty of matching the rhythm and semantics of the gesture to the corresponding speech. To address these problems, we present DiffuseStyleGesture, a diffusion model based speech-driven gesture generation approach. |
Sicheng Yang; Zhiyong Wu; Minglei Li; Zhensong Zhang; Lei Hao; Weihong Bao; Ming Cheng; Long Xiao; |
| 651 | NAS-FM: Neural Architecture Search for Tunable and Interpretable Sound Synthesis Based on Frequency Modulation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose “NAS-FM”, which adopts neural architecture search (NAS) to build a differentiable frequency modulation (FM) synthesizer. |
Zhen Ye; Wei Xue; Xu Tan; Qifeng Liu; Yike Guo; |
| 652 | Q&A: Query-Based Representation Learning for Multi-Track Symbolic Music Re-Arrangement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we tackle rearrangement problems via self-supervised learning, in which the mapping styles can be regarded as conditions and controlled in a flexible way. |
Jingwei Zhao; Gus Xia; Ye Wang; |
| 653 | Fairness and Representation in Satellite-Based Poverty Maps: Evidence of Urban-Rural Disparities and Their Impacts on Downstream Policy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Such poverty maps are typically constructed by training machine learning algorithms on a relatively modest amount of “ground truth" data from surveys, and then predicting poverty levels in areas where imagery exists but surveys do not. Using survey and satellite data from ten countries, this paper investigates disparities in representation, systematic biases in prediction errors, and fairness concerns in satellite-based poverty mapping across urban and rural lines, and shows how these phenomena affect the validity of policies based on predicted maps. |
Emily Aiken; Esther Rolf; Joshua Blumenstock; |
| 654 | Forecasting Soil Moisture Using Domain Inspired Temporal Graph Convolution Neural Networks To Guide Sustainable Crop Management Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Drawing inspiration from traditional hydrological modeling, a novel temporal graph convolution neural network has been constructed. |
Muneeza Azmat; Malvern Madondo; Arun Bawa; Kelsey Dipietro; Raya Horesh; Michael Jacobs; Raghavan Srinivasan; Fearghal O’Donncha; |
| 655 | Toward Job Recommendation for All Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a job recommendation algorithm designed and validated in the context of the French Public Employment Service. |
Guillaume Bied; Solal Nathan; Elia Perennes; Morgane Hoffmann; Philippe Caillou; Bruno Crépon; Christophe Gaillac; Michèle Sebag; |
| 656 | Fast and Differentially Private Fair Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study presents the first differentially private and fair clustering method, built on the recently proposed density-based fair clustering approach. |
Junyoung Byun; Jaewook Lee; |
| 657 | Supporting Sustainable Agroecological Initiatives for Small Farmers Through Constraint Programming Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a flexible model based on Constraint Programming (CP) to address the crop allocation problem. |
Margot Challand; Philippe Vismara; Dimitri Justeau-Allaire; Stéphane de Tourdonnet; |
| 658 | Towards Gender Fairness for Mental Health Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we focus on gender bias in mental health and make the following contributions. First, we examine whether bias exists in existing mental health datasets and algorithms. |
Jiaee Cheong; Selim Kuzucu; Sinan Kalkan; Hatice Gunes; |
| 659 | Addressing Weak Decision Boundaries in Image Classification By Leveraging Web Search and Generative Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose an approach that leverages the power of web search and generative models to alleviate some of the shortcomings of discriminative models. |
Preetam Prabhu Srikar Dammu; Yunhe Feng; Chirag Shah; |
| 660 | Limited Resource Allocation in A Non-Markovian World: The Case of Maternal and Child Healthcare Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle the generalised non-Markovian RMAB setting we (i) model each participant’s trajectory as a time-series, (ii) leverage the power of time-series forecasting models to learn complex patterns and dynamics to predict future states, and (iii) propose the Time-series Arm Ranking Index (TARI) policy, a novel algorithm that selects the RMAB arms that will benefit the most from an intervention, given our future state predictions. |
Panayiotis Danassis; Shresth Verma; Jackson A. Killian; Aparna Taneja; Milind Tambe; |
| 661 | Disentangling Societal Inequality from Model Biases: Gender Inequality in Divorce Court Proceedings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, on the methodological side, we demonstrate that existing NLP resources required several non-trivial modifications to quantify societal inequalities. |
Sujan Dutta; Parth Srivastava; Vaishnavi Solunke; Swaprava Nath; Ashiqur R. KhudaBukhsh; |
| 662 | Sign Language-to-Text Dictionary with Lightweight Transformer Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The need for a sign language-to-text dictionary was raised by a bilingual deaf school in Belgium and linguist experts in sign languages (SL) in order to improve the autonomy of students. To meet that need, an efficient SLR system was built based on a specific transformer model. |
Jérôme Fink; Pierre Poitier; Maxime André; Loup Meurice; Benoît Frénay; Anthony Cleve; Bruno Dumas; Laurence Meurant; |
| 663 | Find Rhinos Without Finding Rhinos: Active Learning with Multimodal Imagery of South African Rhino Habitats Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Monitoring rhinos’ movement is crucial to their protection but has unfortunately proven difficult because rhinos are elusive. Therefore, instead of tracking rhinos, we propose the novel approach of mapping communal defecation sites, called middens, which give information about rhinos’ spatial behavior valuable to anti-poaching, management, and reintroduction efforts. |
Lucia Gordon; Nikhil Behari; Samuel Collier; Elizabeth Bondi-Kelly; Jackson A. Killian; Catherine Ressijac; Peter Boucher; Andrew Davies; Milind Tambe; |
| 664 | CGS: Coupled Growth and Survival Model with Cohort Fairness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a Bayesian network method for modeling fish survival and growth over multiple connected rivers. |
Erhu He; Yue Wan; Benjamin H. Letcher; Jennifer H. Fair; Yiqun Xie; Xiaowei Jia; |
| 665 | Decoding The Underlying Meaning of Multimodal Hateful Memes Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: A major reason for the lack of explainable hateful meme methods is the absence of a hateful meme dataset that contains ground truth explanations for benchmarking or training. Intuitively, having such explanations can educate and assist content moderators in interpreting and removing flagged hateful memes. This paper address this research gap by introducing Hateful meme with Reasons Dataset (HatReD), which is a new multimodal hateful meme dataset annotated with the underlying hateful contextual reasons. |
Ming Shan Hee; Wen-Haw Chong; Roy Ka-Wei Lee; |
| 666 | Computationally Assisted Quality Control for Public Health Data Streams Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing outlier detection frameworks perform poorly on this task because they do not account for the data volume or for the statistical properties of public health streams. Accordingly, we developed FlaSH (Flagging Streams in public Health), a practical outlier detection framework for public health data users that uses simple, scalable models to capture these statistical properties explicitly. |
Ananya Joshi; Kathryn Mazaitis; Roni Rosenfeld; Bryan Wilder; |
| 667 | For Women, Life, Freedom: A Participatory AI-Based Social Web Analysis of A Watershed Moment in Iran’s Gender Struggles Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a computational analysis of the Persian language Twitter discourse with the aim to estimate the shift in stance toward gender equality following the death of Mahsa Amini in police custody. |
Adel Khorramrouz; Sujan Dutta; Ashiqur R. KhudaBukhsh; |
| 668 | Building A Personalized Messaging System for Health Intervention in Underprivileged Regions Using Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose to apply reinforcement learning (RL) to optimize our message selection policy over time, tailoring our messages to align with each individual participant’s needs and preferences. |
Sarah Kinsey; Jack Wolf; Nalini Saligram; Varun Ramesan; Meeta Walavalkar; Nidhi Jaswal; Sandhya Ramalingam; Arunesh Sinha; Thanh Nguyen; |
| 669 | Unified Model for Crystalline Material Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recently, the problem of generating crystal materials has received increasing attention, however, it remains unclear to what extent, or in what way, we can develop generative models that consider both the periodicity and equivalence geometric of crystal structures. To alleviate this issue, we propose two unified models that act at the same time on crystal lattice and atomic positions using periodic equivariant architectures. |
Astrid Klipfel; Yaël Frégier; Adlane Sayede; Zied Bouraoui; |
| 670 | Machine Learning Driven Aid Classification for Sustainable Development Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the challenges of current labor-intensive practices of assigning the code and the related human inefficiencies, we propose a machine learning solution that uses ELECTRA to suggest relevant five-digit purpose codes in CRS for aid activities, achieving an accuracy of 0.9575 for the top-3 recommendations. |
Junho Lee; Hyeonho Song; Dongjoon Lee; Sundong Kim; Jisoo Sim; Meeyoung Cha; Kyung-Ryul Park; |
| 671 | Confidence-based Self-Corrective Learning: An Application in Height Estimation Using Satellite LiDAR and Imagery Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a spatial self-corrective learning framework, which explicitly uses confidence-based pseudo-interpolation, recurrent self-refinement, and truth-based correction with a regression layer to address the challenges. |
Zhili Li; Yiqun Xie; Xiaowei Jia; |
| 672 | DenseLight: Efficient Control for Large-scale Traffic Signals with Dense Feedback Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Therefore, the TSC agent is required to leverage both the local observation and the non-local traffic conditions to predict the long-horizontal traffic conditions of each intersection comprehensively. To address these challenges, we propose DenseLight, a novel RL-based TSC method that employs an unbiased reward function to provide dense feedback on policy effectiveness and a non-local enhanced TSC agent to better predict future traffic conditions for more precise traffic control. |
Junfan Lin; Yuying Zhu; Lingbo Liu; Yang Liu; Guanbin Li; Liang Lin; |
| 673 | SUSTAINABLESIGNALS: An AI Approach for Inferring Consumer Product Sustainability Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We contribute an analysis of this online environment, exploring how the dynamic between sellers and consumers surfaces claims and concerns regarding sustainable consumption. In order to better provide information to consumers, we propose a machine learning method that can discover signals of sustainability from these interactions. |
Tong Lin; Tianliang Xu; Amit Zac; Sabina Tomkins; |
| 674 | Interpret ESG Rating’s Impact on The Industrial Chain Using Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We conduct a quantitative analysis of the development of the industry chain from the environmental, social, and governance (ESG) perspective, which is an overall measure of sustainability. |
Bin Liu; Jiujun He; Ziyuan Li; Xiaoyang Huang; Xiang Zhang; Guosheng Yin; |
| 675 | Preventing Attacks in Interbank Credit Rating with Selective-aware Graph Neural Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we propose a novel selective-aware graph neural network model (SA-GNN) for defense the Interbank credit rating attacks. |
Junyi Liu; Dawei Cheng; Changjun Jiang; |
| 676 | Customized Positional Encoding to Combine Static and Time-varying Data in Robust Representation Learning for Crop Yield Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel transformer based architecture for accurate and robust crop yield prediction, by introducing a Customized Positional Encoding (CPE) that encodes a sequence adaptively according to static information associated with the sequence. |
Qinqing Liu; Fei Dou; Meijian Yang; Ezana Amdework; Guiling Wang; Jinbo Bi; |
| 677 | GreenFlow: A Computation Allocation Framework for Building Environmentally Sound Recommendation System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes GreenFlow, a practical computation allocation framework for RS, that considers both accuracy and carbon emission during inference. |
Xingyu Lu; Zhining Liu; Yanchu Guan; Hongxuan Zhang; Chenyi Zhuang; Wenqi Ma; Yize Tan; Jinjie Gu; Guannan Zhang; |
| 678 | Coupled Point Process-based Sequence Modeling for Privacy-preserving Network Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we develop a coupled neural point process-based (CPP) sequence modeling strategy, which provides a solution to privacy-preserving network alignment based on the event sequences. |
Dixin Luo; Haoran Cheng; Qingbin Li; Hongteng Xu; |
| 679 | Group Sparse Optimal Transport for Sparse Process Flexibility Design Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As a fundamental problem in Operations Research, sparse process flexibility design (SPFD) aims to design a manufacturing network across industries that achieves a trade-off between the efficiency and robustness of supply chains. In this study, we propose a novel solution to this problem with the help of computational optimal transport techniques. |
Dixin Luo; Tingting Yu; Hongteng Xu; |
| 680 | A Prediction-and-Scheduling Framework for Efficient Order Transfer in Logistics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explore fine-grained delivery station features, i.e., downstream couriers’ remaining working times in last-mile delivery trips and the transferred order distribution to design a Prediction-and-Scheduling framework for efficient Order Transfer called PSOT, including two components: i) a Courier’s Remaining Working Time Prediction component to predict each courier’s working time for conducting heterogeneous tasks, i.e., order pickups and deliveries, with a context-aware location embedding and an attention-based neural network; ii) a Vehicle Scheduling component to generate the vehicle’s route to served delivery stations with an order-transfer-time-aware heuristic algorithm. |
Wenjun Lyu; Haotian Wang; Yiwei Song; Yunhuai Liu; Tian He; Desheng Zhang; |
| 681 | Fighting Against Organized Fraudsters Using Risk Diffusion-based Parallel Graph Neural Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we proposed RDPGL, a novel Risk Diffusion-based Parallel Graph Learning approach, to fighting against medical insurance criminal gangs. |
Jiacheng Ma; Fan Li; Rui Zhang; Zhikang Xu; Dawei Cheng; Yi Ouyang; Ruihui Zhao; Jianguang Zheng; Yefeng Zheng; Changjun Jiang; |
| 682 | Planning Multiple Epidemic Interventions with Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Policy-makers, however, would greatly benefit from tools that can efficiently search for plans that minimize disease and economic costs especially when considering multiple possible interventions over a continuous and complex action space given a continuous and equally complex state space. We formulate this problem as a Markov decision process. |
Anh Mai; Nikunj Gupta; Azza Abouzied; Dennis Shasha; |
| 683 | Temporally Aligning Long Audio Interviews with Questions: A Case Study in Multimodal Data Integration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work is a collaboration with a non-governmental organization called CARE India that collects long audio health surveys from young mothers residing in rural parts of Bihar, India. Given a question drawn from a questionnaire that is used to guide these surveys, we aim to locate where the question is asked within a long audio recording. |
Piyush Singh Pasi; Karthikeya Battepati; Preethi Jyothi; Ganesh Ramakrishnan; Tanmay Mahapatra; Manoj Singh; |
| 684 | Time Series of Satellite Imagery Improve Deep Learning Estimates of Neighborhood-Level Poverty in Africa Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we develop an EO-ML method for inferring neighborhood-level material-asset wealth using multi-temporal imagery and recurrent convolutional neural networks. |
Markus B. Pettersson; Mohammad Kakooei; Julia Ortheden; Fredrik D. Johansson; Adel Daoud; |
| 685 | Balancing Social Impact, Opportunities, and Ethical Constraints of Using AI in The Documentation and Vitalization of Indigenous Languages Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we discuss how AI can contribute to support the documentation and vitalization of Indigenous languages and how that involves a delicate balancing of ensuring social impact, exploring technical opportunities, and dealing with ethical constraints. |
Claudio S. Pinhanez; Paulo Cavalin; Marisa Vasconcelos; Julio Nogima; |
| 686 | PARTNER: A Persuasive Mental Health and Legal Counselling Dialogue System for Women and Children Crime Victims Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose PARTNER, a Politeness and empAthy strategies-adaptive peRsuasive dialogue sysTem for meNtal health and LEgal counselling of cRime victims. |
Priyanshu Priya; Kshitij Mishra; Palak Totala; Asif Ekbal; |
| 687 | AudioQR: Deep Neural Audio Watermarks For QR Code Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: With the goal of “AI for good", this paper proposes the AudioQR, a barrier-free QR coding mechanism for the visually impaired population via deep neural audio watermarks. |
Xinghua Qu; Xiang Yin; Pengfei Wei; Lu Lu; Zejun Ma; |
| 688 | Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The Multi-Agent Reinforcement Learning (MARL) controller trained with Proximal Policy Optimization (PPO) algorithm can handle these complexities. In this paper, we explore different function approximations for the policy and critic networks in modeling the sequential nature of the system dynamics and find that they are key to better performance. |
Soumyendu Sarkar; Vineet Gundecha; Sahand Ghorbanpour; Alexander Shmakov; Ashwin Ramesh Babu; Avisek Naug; Alexandre Pichard; Mathieu Cocho; |
| 689 | Promoting Gender Equality Through Gender-biased Language Analysis in Social Media Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study introduces a novel problem of Gender-biased Language Identification and Extraction (GLIdE) from social media interactions and develops a multi-task deep framework that detects gender-biased content and identifies connected causal phrases from the text using emotional information that is present in the input. |
Gopendra Singh; Soumitra Ghosh; Asif Ekbal; |
| 690 | Leveraging Domain Knowledge for Inclusive and Bias-aware Humanitarian Response Entry Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we aim to provide an effective and ethically-aware system for humanitarian data analysis. |
Nicolò Tamagnone; Selim Fekih; Ximena Contla; Nayid Orozco; Navid Rekabsaz; |
| 691 | Optimizing Crop Management with Reinforcement Learning and Imitation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present an intelligent crop management system that optimizes nitrogen fertilization and irrigation simultaneously via RL, imitation learning (IL), and crop simulations using the Decision Support System for Agrotechnology Transfer (DSSAT). |
Ran Tao; Pan Zhao; Jing Wu; Nicolas Martin; Matthew T. Harrison; Carla Ferreira; Zahra Kalantari; Naira Hovakimyan; |
| 692 | Keeping People Active and Healthy at Home Using A Reinforcement Learning-based Fitness Recommendation Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work we propose a reinforcement learning (RL) based framework for recommending sequences of body-weight exercises to home users over a mobile application interface. |
Elias Tragos; Diarmuid O’Reilly-Morgan; James Geraci; Bichen Shi; Barry Smyth; Cailbhe Doherty; Aonghus Lawlor; Neil Hurley; |
| 693 | Intensity-Valued Emotions Help Stance Detection of Climate Change Twitter Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In our work, we propose a framework that helps identify different attitudes in tweets about climate change (deny, believe, ambiguous). |
Apoorva Upadhyaya; Marco Fisichella; Wolfgang Nejdl; |
| 694 | Evaluating GPT-3 Generated Explanations for Hateful Content Moderation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: For instance, an LLM-generated explanation might inaccurately convince a content moderator that a benign piece of content is hateful. In light of this, we propose an analytical framework for examining hate speech explanations and conducted an extensive survey on evaluating such explanations. |
Han Wang; Ming Shan Hee; Md Rabiul Awal; Kenny Tsu Wei Choo; Roy Ka-Wei Lee; |
| 695 | Full Scaling Automation for Sustainable Development of Green Data Centers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our proposed Full Scaling Automation (FSA) mechanism is an effective method of dynamically adapting resources to accommodate changing workloads in large-scale cloud computing clusters, enabling the clusters in data centers to maintain their desired CPU utilization target and thus improve energy efficiency. |
Shiyu Wang; Yinbo Sun; Xiaoming Shi; Zhu Shiyi; Lin-Tao Ma; James Zhang; YangFei Zheng; Liu Jian; |
| 696 | A Quantitative Game-theoretical Study on Externalities of Long-lasting Humanitarian Relief Operations in Conflict Areas Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we quantitatively analyze the potential externalities associated with long-lasting humanitarian relief operations based on game-theoretical modeling and online planning approaches. |
Kaiming Xiao; Haiwen Chen; Hongbin Huang; Lihua Liu; Jibing Wu; |
| 697 | Quality-agnostic Image Captioning to Safely Assist People with Vision Impairment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a quality-agnostic framework to improve the performance and robustness of image captioning models for visually impaired people. |
Lu Yu; Malvina Nikandrou; Jiali Jin; Verena Rieser; |
| 698 | GreenPLM: Cross-Lingual Transfer of Monolingual Pre-Trained Language Models at Almost No Cost Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Large pre-trained models have revolutionized natural language processing (NLP) research and applications, but high training costs and limited data resources have prevented their benefits from being shared equally amongst speakers of all the world’s languages. To address issues of cross-linguistic access to such models and reduce energy consumption for sustainability during large-scale model training, this study proposes an effective and energy-efficient framework called GreenPLM that uses bilingual lexicons to directly “translate” pre-trained language models of one language into another at almost no additional cost. |
Qingcheng Zeng; Lucas Garay; Peilin Zhou; Dading Chong; Yining Hua; Jiageng Wu; Yikang Pan; Han Zhou; Rob Voigt; Jie Yang; |
| 699 | Mimicking The Thinking Process for Emotion Recognition in Conversation with Prompts and Paraphrasing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: It is a challenging task since the recognition of the emotion in one utterance involves many complex factors, such as the conversational context, the speaker’s background, and the subtle difference between emotion labels. In this paper, we propose a novel framework which mimics the thinking process when modeling these factors. |
Ting Zhang; Zhuang Chen; Ming Zhong; Tieyun Qian; |
| 700 | Long-term Wind Power Forecasting with Hierarchical Spatial-Temporal Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel end-to-end wind power forecasting model named Hierarchical Spatial-Temporal Transformer Network (HSTTN) to address the long-term WPF problems. |
Yang Zhang; Lingbo Liu; Xinyu Xiong; Guanbin Li; Guoli Wang; Liang Lin; |
| 701 | On Optimizing Model Generality in AI-based Disaster Damage Assessment: A Subjective Logic-driven Crowd-AI Hybrid Learning Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper focuses on the AI-based damage assessment (ADA) applications that leverage state-of-the-art AI techniques to automatically assess the disaster damage severity using online social media imagery data, which aligns well with the ”disaster risk reduction” target under United Nations’ Sustainable Development Goals (UN SDGs). |
Yang Zhang; Ruohan Zong; Lanyu Shang; Huimin Zeng; Zhenrui Yue; Na Wei; Dong Wang; |
| 702 | User-Centric Democratization Towards Social Value Aligned Medical AI Services Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate Democratic AI, define it mathematically, and propose a user-centric evolutionary democratic AI (u-DemAI) framework. |
Zhaonian Zhang; Richard Jiang; |
| 703 | Optimization-driven Demand Prediction Framework for Suburban Dynamic Demand-Responsive Transport Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We thus investigate in this work the potential of a Demand Prediction Framework used specifically to build more flexible routes within a Dynamic Dial-a-Ride Problem (DaRP) solver. |
Louis Zigrand; Roberto Wolfler Calvo; Emiliano Traversi; Pegah Alizadeh; |
| 704 | Long-term Monitoring of Bird Flocks in The Wild Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We foresee that the objectives of this project would lead to datasets and benchmarking tools as well as novel algorithms that would be instrumental in developing automated video analysis tools that could in turn help understand individual and social behavior of birds. |
Kshitiz; Sonu Shreshtha; Ramy Mounir; Mayank Vatsa; Richa Singh; Saket Anand; Sudeep Sarkar; Sevaram Mali Parihar; |
| 705 | On AI-Assisted Pneumoconiosis Detection from Chest X-rays Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The reduced availability of expert medical care in rural areas, where these diseases are more prevalent, further adds to the delayed screening and unfavourable outcomes of the disease. This paper aims to highlight the urgent need for early screening and detection of Pneumoconiosis, given its significant impact on affected individuals, their families, and societies as a whole. |
Yasmeena Akhter; Rishabh Ranjan; Richa Singh; Mayank Vatsa; Santanu Chaudhury; |
| 706 | AI-Assisted Tool for Early Diagnosis and Prevention of Colorectal Cancer in Africa Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Hence, why we propose to curate a colonoscopy video dataset focused on African patients, provide expert annotations for video polyp segmentation and provide an AI-assisted tool to record colonoscopy videos using smart phones. |
Bushra Ibnauf; Mohammed Aboul Ezz; Ayman Abdel Aziz; Khalid Elgazzar; Mennatullah Siam; |
| 707 | AI and Decision Support for Sustainable Socio-Ecosystems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, there is still an important gap between biodiversity research and the management of natural areas. This research project aims to reduce this gap by proposing spatial planning methods that robustly and accurately integrate socio-ecological issues. |
Dimitri Justeau-Allaire; |
| 708 | NutriAI: AI-Powered Child Malnutrition Assessment in Low-Resource Environments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this research, we are developing NutriAI, a low-cost solution that leverages small sample size classification approach to detect malnutrition by analyzing 2D images of the subjects in multiple poses. |
Misaal Khan; Shivang Agarwal; Mayank Vatsa; Richa Singh; Kuldeep Singh; |
| 709 | Learning and Reasoning Multifaceted and Longitudinal Data for Poverty Estimates and Livelihood Capabilities of Lagged Regions in Rural India Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The proposed project aims to examine the poverty situation of rural India for the period of 1990-2022 based on the quality of life and livelihood indicators. |
Atharva Kulkarni; Raya Das; Ravi S. Srivastava; Tanmoy Chakraborty; |
| 710 | AI-Driven Sign Language Interpretation for Nigerian Children at Home Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: With the recent successes of AI in natural language understanding, the goal of automated sign language understanding is becoming more realistic using neural deep learning technologies. To this effect, the proposed project aims at co-designing and developing an ongoing AI-driven two-way sign language interpretation tool that can be deployed in homes, to improve language accessibility and communication between the DHH students and other family members. |
Ifeoma Nwogu; Roshan Peiris; Karthik Dantu; Ruchi Gamta; Emma Asonye; |
| 711 | Interactive Machine Learning Solutions for Acoustic Monitoring of Animal Wildlife in Biosphere Reserves Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As an interdisciplinary team of machine learning scientists and ecologists experienced with PAM and working at biosphere reserves in marine and terrestrial ecosystems on three different continents, we report on the co-development of interactive machine learning tools for semi-automated assessment of animal wildlife. |
Thiago S. Gouvêa; Hannes Kath; Ilira Troshani; Bengt Lüers; Patricia P. Serafini; Ivan B. Campos; André S. Afonso; Sergio M. F. M. Leandro; Lourens Swanepoel; Nicholas Theron; Anthony M. Swemmer; Daniel Sonntag; |
| 712 | Even If Explanations: Prior Work, Desiderata & Benchmarks for Semi-Factual XAI Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper surveys semi-factual explanation, summarising historical and recent work. |
Saugat Aryal; Mark T. Keane; |
| 713 | Good Explanations in Explainable Artificial Intelligence (XAI): Evidence from Human Explanatory Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Insights from cognitive science about how people understand explanations can be instructive for the development of robust, user-centred explanations in eXplainable Artificial Intelligence (XAI). I survey key tendencies that people exhibit when they construct explanations and make inferences from them, of relevance to the provision of automated explanations for decisions by AI systems. |
Ruth M.J. Byrne; |
| 714 | Temporal Knowledge Graph Completion: A Survey Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, for the first time, we comprehensively summarize the recent advances in TKGC research. |
Borui Cai; Yong Xiang; Longxiang Gao; He Zhang; Yunfeng Li; Jianxin Li; |
| 715 | Assessing and Enforcing Fairness in The AI Lifecycle Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This survey systematises the state-of-the-art about distinct notions of fairness and relative techniques for bias mitigation according to the AI lifecycle. |
Roberta Calegari; Gabriel G. Castañé; Michela Milano; Barry O’Sullivan; |
| 716 | Anti-unification and Generalization: A Survey Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We provide the first survey of AU research and its applications and a general framework for categorizing existing and future developments. |
David M. Cerna; Temur Kutsia; |
| 717 | Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address this issue, much effort has been devoted to various fields of KGs. In this paper, we use a set of general terminologies to unify these methods and refer to them collectively as Knowledge Extrapolation. |
Mingyang Chen; Wen Zhang; Yuxia Geng; Zezhong Xu; Jeff Z. Pan; Huajun Chen; |
| 718 | A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this survey, we provide a comprehensive overview of the prominent problems and advanced designs for conversational agent’s proactivity in different types of dialogues. |
Yang Deng; Wenqiang Lei; Wai Lam; Tat-Seng Chua; |
| 719 | Machine Learning for Cutting Planes in Integer Programming: A Survey Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents an overview of the topic, highlighting recent advances in the literature, common approaches to data collection, evaluation, and ML model architectures. |
Arnaud Deza; Elias B. Khalil; |
| 720 | Game-theoretic Mechanisms for Eliciting Accurate Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This survey categorizes the different techniques and their properties, and shows their limits and tradeoffs. |
Boi Faltings; |
| 721 | A Survey on Dataset Distillation: Approaches, Applications and Future Directions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite recent advances, we lack a holistic understanding of the approaches and applications. Our survey aims to bridge this gap by first proposing a taxonomy of dataset distillation, characterizing existing approaches, and then systematically reviewing the data modalities, and related applications. |
Jiahui Geng; Zongxiong Chen; Yuandou Wang; Herbert Woisetschlaeger; Sonja Schimmler; Ruben Mayer; Zhiming Zhao; Chunming Rong; |
| 722 | A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this survey, we review the state-of-the-art in intersectional fairness. |
Usman Gohar; Lu Cheng; |
| 723 | Survey on Online Streaming Continual Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This survey explores the intersection of those two online learning paradigms to find synergies. |
Nuwan Gunasekara; Bernhard Pfahringer; Heitor Murilo Gomes; Albert Bifet; |
| 724 | Graph-based Molecular Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recently, MRL has achieved considerable progress, especially in methods based on deep molecular graph learning. In this survey, we systematically review these graph-based molecular representation techniques, especially the methods incorporating chemical domain knowledge. |
Zhichun Guo; Kehan Guo; Bozhao Nan; Yijun Tian; Roshni G. Iyer; Yihong Ma; Olaf Wiest; Xiangliang Zhang; Wei Wang; Chuxu Zhang; Nitesh V. Chawla; |
| 725 | Towards Utilitarian Online Learning — A Review of Online Algorithms in Open Feature Space Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper reviews recent breakthroughs that strived to enable OL in open feature spaces, referred to as Utilitarian Online Learning (UOL). |
Yi He; Christian Schreckenberger; Heiner Stuckenschmidt; Xindong Wu; |
| 726 | A Survey on User Behavior Modeling in Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we attempt to provide a thorough survey of this research topic. |
Zhicheng He; Weiwen Liu; Wei Guo; Jiarui Qin; Yingxue Zhang; Yaochen Hu; Ruiming Tang; |
| 727 | Benchmarking EXplainable AI – A Survey on Available Toolkits and Open Challenges Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we take an additional perspective and analyse which toolkits and data sets are available. |
Phuong Quynh Le; Meike Nauta; Van Bach Nguyen; Shreyasi Pathak; Jörg Schlötterer; Christin Seifert; |
| 728 | Curriculum Graph Machine Learning: A Survey Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle this critical problem, curriculum graph machine learning (Graph CL), which integrates the strength of graph machine learning and curriculum learning, arises and attracts an increasing amount of attention from the research community. Therefore, in this paper, we comprehensively overview approaches on Graph CL and present a detailed survey of recent advances in this direction. |
Haoyang Li; Xin Wang; Wenwu Zhu; |
| 729 | A Survey on Out-of-Distribution Evaluation of Neural NLP Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This survey will 1) compare the three lines of research under a unifying definition; 2) summarize their data-generating processes and evaluation protocols for each line of research; and 3) emphasize the challenges and opportunities for future work. |
Xinzhe Li; Ming Liu; Shang Gao; Wray Buntine; |
| 730 | Diffusion Models for Non-autoregressive Text Generation: A Survey Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this survey, we review the recent progress in diffusion models for NAR text generation. |
Yifan Li; Kun Zhou; Wayne Xin Zhao; Ji-Rong Wen; |
| 731 | Generative Diffusion Models on Graphs: Methods and Applications Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we first provide a comprehensive overview of generative diffusion models on graphs, |
Chengyi Liu; Wenqi Fan; Yunqing Liu; Jiatong Li; Hang Li; Hui Liu; Jiliang Tang; Qing Li; |
| 732 | Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Although a great variety of methods have been proposed in this promising and fast-developing research field, to the best of our knowledge, little effort has been made to systematically summarize these works. To set the stage for the development of future works, in this paper, we attempt to fill this gap by providing a broad review of recent methods for graph pooling. |
Chuang Liu; Yibing Zhan; Jia Wu; Chang Li; Bo Du; Wenbin Hu; Tongliang Liu; Dacheng Tao; |
| 733 | A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this survey, we conduct a comprehensive investigation on advanced deep learning-based reaction and retrosynthesis prediction models. |
Ziqiao Meng; Peilin Zhao; Yang Yu; Irwin King; |
| 734 | Complexity Results and Exact Algorithms for Fair Division of Indivisible Items: A Survey Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This motivates the study of algorithms that—even though they only run in exponential time—are as fast as possible and exactly solve such problems. We present known complexity results for them and give a survey of important techniques for designing such algorithms, mainly focusing on four common fairness notions: max-min fairness, maximin share, maximizing Nash social welfare, and envy-freeness. |
Trung Thanh Nguyen; Jörg Rothe; |
| 735 | What Lies Beyond The Pareto Front? A Survey on Decision-Support Methods for Multi-Objective Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a review that unifies decision-support methods for exploring the solutions produced by multi-objective optimization (MOO) algorithms. |
Zuzanna Osika; Jazmin Zatarain Salazar; Diederik M. Roijers; Frans A. Oliehoek; Pradeep K. Murukannaiah; |
| 736 | Uncovering The Deceptions: An Analysis on Audio Spoofing Detection and Future Prospects Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper reviews the current state-of-the-art techniques for detecting audio spoofing and discusses the current challenges along with open research problems. |
Rishabh Ranjan; Mayank Vatsa; Richa Singh; |
| 737 | Heuristic-Search Approaches for The Multi-Objective Shortest-Path Problem: Progress and Research Opportunities Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Consequently, in this paper we review the fundamental problems and techniques common to most algorithms and provide a general overview of the field. |
Oren Salzman; Ariel Felner; Carlos Hernández; Han Zhang; Shao-Hung Chan; Sven Koenig; |
| 738 | A Survey of Federated Evaluation in Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we provide the first comprehensive survey of existing federated evaluation methods. |
Behnaz Soltani; Yipeng Zhou; Venus Haghighi; John C. S. Lui; |
| 739 | Transformers in Time Series: A Survey Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we systematically review Transformer schemes for time series modeling by highlighting their strengths as well as limitations. |
Qingsong Wen; Tian Zhou; Chaoli Zhang; Weiqi Chen; Ziqing Ma; Junchi Yan; Liang Sun; |
| 740 | A Systematic Survey of Chemical Pre-trained Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present the first survey that summarizes the current progress of CPMs. |
Jun Xia; Yanqiao Zhu; Yuanqi Du; Stan Z. Li; |
| 741 | Recent Advances in Direct Speech-to-text Translation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a comprehensive survey on direct speech translation aiming to summarize the current state-of-the-art techniques. |
Chen Xu; Rong Ye; Qianqian Dong; Chengqi Zhao; Tom Ko; Mingxuan Wang; Tong Xiao; Jingbo Zhu; |
| 742 | A Survey on Masked Autoencoder for Visual Self-supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work conducts a survey on masked autoencoders for visual SSL. |
Chaoning Zhang; Chenshuang Zhang; Junha Song; John Seon Keun Yi; In So Kweon; |
| 743 | State-wise Safe Reinforcement Learning: A Survey Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper provides a comprehensive review of existing approaches that address state-wise constraints in RL. |
Weiye Zhao; Tairan He; Rui Chen; Tianhao Wei; Changliu Liu; |
| 744 | A Survey on Efficient Training of Transformers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This survey provides the first systematic overview of the efficient training of Transformers, covering the recent progress in acceleration arithmetic and hardware, with a focus on the former. |
Bohan Zhuang; Jing Liu; Zizheng Pan; Haoyu He; Yuetian Weng; Chunhua Shen; |
| 745 | Conjure: Automatic Generation of Constraint Models from Problem Specifications (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We describe a method to automatically produce constraint models from a problem specification written in the abstract constraint specification language Essence. |
Özgür Akgün; Alan M. Frisch; Ian P. Gent; Christopher Jefferson; Ian Miguel; Peter Nightingale; |
| 746 | Survey and Evaluation of Causal Discovery Methods for Time Series (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce in this survey the major concepts, models, and algorithms proposed so far to infer causal relations from observational time series, a task usually referred to as causal discovery in time series. |
Charles K. Assaad; Emilie Devijver; Eric Gaussier; |
| 747 | Adversarial Framework with Certified Robustness for Time-Series Domain Via Statistical Features (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel adversarial framework referred to as Time-Series Attacks via STATistical Features (TSA-STAT). |
Taha Belkhouja; Janardhan Rao Doppa; |
| 748 | Constraint Solving Approaches to The Business-to-Business Meeting Scheduling Problem (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Therefore, it constitutes a challenging combinatorial problem in many real-world B2B events. This work presents a comparative study of several approaches to solve this problem. |
Miquel Bofill; Jordi Coll; Marc Garcia; Jesús Giráldez-Cru; Gilles Pesant; Josep Suy; Mateu Villaret; |
| 749 | SAT Encodings for Pseudo-Boolean Constraints Together With At-Most-One Constraints (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we present new encodings for PB(AMO) constraints. |
Miquel Bofill; Jordi Coll; Peter Nightingale; Josep Suy; Felix Ulrich-Oltean; Mateu Villaret; |
| 750 | A False Sense of Security (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper analyzes the vulnerabilities of some recent knowledge protection methods to increase the awareness about their actual effectiveness and their mutual differences. |
Piero A. Bonatti; |
| 751 | Optimizing The Computation of Overriding in DLN (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The family of nonmonotonic logics DLN is no exception to this behavior. We address this issue by introducing two provably correct and complete optimizations for reasoning in DLN. |
Piero A. Bonatti; Iliana Petrova; Luigi Sauro; |
| 752 | SAMBA: A Generic Framework for Secure Federated Multi-Armed Bandits (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We rely on cryptographic schemes and propose SAMBA, a generic framework for Secure federAted Multi-armed BAndits. |
Radu Ciucanu; Pascal Lafourcade; Gael Marcadet; Marta Soare; |
| 753 | Data-Driven Revision of Conditional Norms in Multi-Agent Systems (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate the use of system execution data to automatically synthesise and revise conditional prohibitions with deadlines, a type of norms aimed at preventing agents from exhibiting certain patterns of behaviors. |
Davide Dell’Anna; Natasha Alechina; Fabiano Dalpiaz; Mehdi Dastani; Brian Logan; |
| 754 | Interpretable Local Concept-based Explanation with Human Feedback to Predict All-cause Mortality (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose Concept-based Local Explanations with Feedback (CLEF), a novel local model agnostic explanation framework for learning a set of high-level transparent concept definitions in high-dimensional tabular data that uses clinician-labeled concepts rather than raw features. |
Radwa El Shawi; Mouaz Al-Mallah; |
| 755 | Core Challenges in Embodied Vision-Language Planning (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this survey paper, we discuss Embodied Vision-Language Planning (EVLP) tasks, a family of prominent embodied navigation and manipulation problems that jointly leverage computer vision and natural language for interaction in physical environments. |
Jonathan Francis; Nariaki Kitamura; Felix Labelle; Xiaopeng Lu; Ingrid Navarro; Jean Oh; |
| 756 | Multi-Agent Advisor Q-Learning (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We describe the problem of ADvising Multiple Intelligent Reinforcement Agents (ADMIRAL) in nonrestrictive general-sum stochastic game environments and present two novel Q-learning-based algorithms: ADMIRAL – Decision Making (ADMIRAL-DM) and ADMIRAL – Advisor Evaluation (ADMIRAL-AE), which allow us to improve learning by appropriately incorporating advice from an advisor (ADMIRAL-DM), and evaluate the effectiveness of an advisor (ADMIRAL-AE). |
Sriram Ganapathi Subramanian; Matthew E. Taylor; Kate Larson; Mark Crowley; |
| 757 | Creative Problem Solving in Artificially Intelligent Agents: A Survey and Framework (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite many advancements in planning and learning in AI, resolving novel problems or adapting existing knowledge to a new context, especially in cases where the environment may change in unpredictable ways, remains a challenge. To stimulate further research in CPS, we contribute a definition and a framework of CPS, which we use to categorize existing AI methods in this field. |
Evana Gizzi; Lakshmi Nair; Sonia Chernova; Jivko Sinapov; |
| 758 | Ordinal Maximin Share Approximation for Goods (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We prove the existence of l-out-of-floor((l+1/2)n) MMS allocations of goods for any integer l greater than or equal to 1, and present a polynomial-time algorithm that finds a 1-out-of-ceiling(3n/2) MMS allocation when l = 1. |
Hadi Hosseini; Andrew Searns; Erel Segal-Halevi; |
| 759 | On Tackling Explanation Redundancy in Decision Trees (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper overviews recent theoretical and practical results which demonstrate that for most decision trees, tree paths exhibit so-called explanation redundancy, |
Yacine Izza; Alexey Ignatiev; Joao Marques-Silva; |
| 760 | Get Out of The BAG! Silos in AI Ethics Education: Unsupervised Topic Modeling Analysis of Global AI Curricula (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Building on the uncovered patterns, we distil a model of current pedagogical practice, the BAG model (Build, Assess, and Govern), that combines cognitive levels, course content, and disciplines. |
Rana Tallal Javed; Osama Nasir; Melania Borit; Loïs Vanhée; Elias Zea; Shivam Gupta; Ricardo Vinuesa; Junaid Qadir; |
| 761 | Data-Informed Knowledge and Strategies (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The article proposes a new approach to reasoning about knowledge and strategies in multiagent systems. |
Junli Jiang; Pavel Naumov; |
| 762 | Gradient-Based Mixed Planning with Symbolic and Numeric Action Parameters (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel algorithm framework to solve numeric planning problems mixed with logical relations and numeric changes based on gradient descent. |
Kebing Jin; Hankz Hankui Zhuo; Zhanhao Xiao; Hai Wan; Subbarao Kambhampati; |
| 763 | Rethinking Formal Models of Partially Observable Multiagent Decision Making (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: One issue with the current situation is that while most practical problems can be modelled in both formalisms, the relationship of the two models is unclear, which hinders the transfer of ideas between the two communities. A second issue is that while EFGs have recently seen significant algorithmic progress, their classical formalization is unsuitable for efficient presentation of the underlying ideas, such as those around decomposition. To solve the first issue, we introduce factored-observation stochastic games (FOSGs), a minor modification of the POSG formalism which distinguishes between private and public observation and thereby greatly simplifies decomposition. To remedy the second issue, we show that FOSGs and POSGs are naturally connected to EFGs: by unrolling a FOSG into its tree form, we obtain an EFG. Conversely, any perfect-recall timeable EFG corresponds to some underlying FOSG in this manner. |
Vojtěch Kovařík; Martin Schmid; Neil Burch; Michael Bowling; Viliam Lisý; |
| 764 | Mean-Semivariance Policy Optimization Via Risk-Averse Reinforcement Learning (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper aims at optimizing the mean-semivariance (MSV) criterion in reinforcement learning w.r.t. steady reward distribution. |
Xiaoteng Ma; Shuai Ma; Li Xia; Qianchuan Zhao; |
| 765 | Learning to Design Fair and Private Voting Rules (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Voting is used widely to aggregate preferences to make a collective decision. In this paper, we focus on evaluating and designing voting rules that support both the privacy of the voting agents and a notion of fairness over such agents. |
Farhad Mohsin; Ao Liu; Pin-Yu Chen; Francesca Rossi; Lirong Xia; |
| 766 | A Computational Model of Ostrom’s Institutional Analysis and Development Framework (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Ostrom’s Institutional Analysis and Development (IAD) framework represents a comprehensive theoretical effort to identify and outline the variables that determine the outcome in any social interaction. Taking inspiration from it, we define the Action Situation Language (ASL), a machine-readable logical language to express the components of a multiagent interaction, with a special focus on the rules adopted by the community. |
Nieves Montes; Nardine Osman; Carles Sierra; |
| 767 | Proofs and Certificates for Max-SAT (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a tool, called MS-Builder, which generates certificates for the Max-SAT problem in the particular form of a sequence of equivalence-preserving transformations. |
Matthieu Py; Mohamed Sami Cherif; Djamal Habet; |
| 768 | Automatic Recognition of The General-Purpose Communicative Functions Defined By The ISO 24617-2 Standard for Dialog Act Annotation (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The ISO 24617-2 standard for dialog act annotation defines a hierarchically organized set of general-purpose communicative functions that correspond to different intentions that are relevant in the context of a dialog. In this paper, we explore the automatic recognition of these functions. |
Eugénio Ribeiro; Ricardo Ribeiro; David Martins de Matos; |
| 769 | Memory-Limited Model-Based Diagnosis (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As a remedy, we propose RBF-HS, a diagnostic search based on Korf’s seminal RBFS algorithm which can enumerate an arbitrary fixed number of fault explanations in best-first order within linear space bounds, without sacrificing other desirable properties. |
Patrick Rodler; |
| 770 | Q-Learning-Based Model Predictive Variable Impedance Control for Physical Human-Robot Collaboration (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The robot-control strategies with such attributes are particularly demanded in the industrial field. Indeed, with this aim, this work proposes a Q-Learning-based Model Predictive Variable Impedance Control (Q-LMPVIC) to assist the operators in physical human-robot collaboration (pHRC) tasks. |
Loris Roveda; Andrea Testa; Asad Ali Shahid; Francesco Braghin; Dario Piga; |
| 771 | A Survey of Methods for Automated Algorithm Configuration (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we introduce taxonomies to describe the AC problem and features of configuration methods, respectively. |
Elias Schede; Jasmin Brandt; Alexander Tornede; Marcel Wever; Viktor Bengs; Eyke Hüllermeier; Kevin Tierney; |
| 772 | Motion Planning Under Uncertainty with Complex Agents and Environments Via Hybrid Search (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A variety of 2D and 3D test cases are presented in the full paper including a linear case, a Dubins car model, and an underwater autonomous vehicle. |
Daniel Strawser; Brian Williams; |
| 773 | Incremental Event Calculus for Run-Time Reasoning (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a system for online, incremental composite event recognition. |
Efthimis Tsilionis; Alexander Artikis; Georgios Paliouras; |
| 774 | Ethical By Designer – How to Grow Ethical Designers of Artificial Intelligence (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Training designers for ethical behaviour, understood as habitual application of ethical principles in any situation, can make a significant difference in the practice of research, development, and application of AI systems. Building on interdisciplinary knowledge and practical experience from computer science, moral psychology, and pedagogy, we propose a functional way to provide this training. |
Loïs Vanhée; Melania Borit; |
| 775 | A Logic-based Explanation Generation Framework for Classical and Hybrid Planning Problems (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we approach the model reconciliation problem from a different perspective, that of knowledge representation and reasoning, and demonstrate that our approach can be applied not only to classical planning problems but also hybrid systems planning problems with durative actions and events/processes. |
Stylianos Loukas Vasileiou; William Yeoh; Son Tran; Ashwin Kumar; Michael Cashmore; Daniele Magazzeni; |
| 776 | Reinforcement Learning from Optimization Proxy for Ride-Hailing Vehicle Relocation (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper designs a hybrid approach that leverages the strengths of the two while overcoming their drawbacks. |
Enpeng Yuan; Wenbo Chen; Pascal Van Hentenryck; |
| 777 | Unsupervised and Few-Shot Parsing from Pretrained Language Models (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes two Unsupervised constituent Parsing models (UPOA and UPIO) that calculate inside and outside association scores solely based on the self-attention weight matrix learned in a pretrained language model. |
Zhiyuan Zeng; Deyi Xiong; |
| 778 | Simplified Risk-aware Decision Making with Belief-dependent Rewards in Partially Observable Domains (Extended Abstract) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we extend the decision-making mechanism to the whole by removing standard approximations and considering all previously suppressed stochastic sources of variability. |
Andrey Zhitnikov; Vadim Indelman; |
| 779 | SemFORMS: Automatic Generation of Semantic Transforms By Mining Data Science Code Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We demonstrate a system called SemFORMS (Semantic Transforms), which attempts to mine useful expressions for a dataset from access to a repository of code that may target the same dataset/similar dataset. |
Ibrahim Abdelaziz; Julian Dolby; Udayan Khurana; Horst Samulowitz; Kavitha Srinivas; |
| 780 | Bias On Demand: Investigating Bias with A Synthetic Data Generator Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We demonstrate Bias on Demand, a framework to generate synthetic datasets with different types of bias, which is available as an open-source toolkit and as a pip package. |
Joachim Baumann; Alessandro Castelnovo; Andrea Cosentini; Riccardo Crupi; Nicole Inverardi; Daniele Regoli; |
| 781 | SiWare: Contextual Understanding of Industrial Data for Situational Awareness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: SiWare links and fuses heterogeneous data sources with an industry semantic model leveraging multiple AI capabilities to provide system-wide visibility into operational characteristics. As part of this demo paper, we describe the requirements for such a system, and deployment aspects, and demonstrate the benefits in two industrial scenarios. |
Anuradha Bhamidipaty; Elham Khabiri; Bhavna Agrawal; Yingjie Li; |
| 782 | Automated Planning for Generating and Simulating Traffic Signal Strategies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present a framework that relies on automated planning to generate and simulate traffic signal strategies in a urban region. |
Saumya Bhatnagar; Rongge Guo; Keith McCabe; Thomas McCluskey; Francesco Percassi; Mauro Vallati; |
| 783 | NeoMaPy: A Framework for Computing MAP Inference on Temporal Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Markov Logic Networks (MLN) are used for reasoning on uncertain and inconsistent temporal data. We proposed the TMLN (Temporal Markov Logic Network) which extends them with sorts/types, weights on rules and facts, and various temporal consistencies. |
Victor David; Raphael Fournier-S’niehotta; Nicolas Travers; |
| 784 | Latent Inspector: An Interactive Tool for Probing Neural Network Behaviors Through Arbitrary Latent Activation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work presents an active software instrument allowing deep learning architects to interactively inspect neural network models’ output behavior from user-manipulated values in any latent layer. |
Daniel Geißler; Bo Zhou; Paul Lukowicz; |
| 785 | Understanding The Night-Sky? Developing AI-Enabled System for Exploring Night-Light Usage Patterns Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a demonstration of nighttime light pattern (NTL) analysis system. |
Jakob Hederich; Shreya Ghosh; Zeyu He; Prasenjit Mitra; |
| 786 | Practical Model Reductions for Verification of Multi-Agent Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Formal verification of intelligent agents is often computationally infeasible due to state-space explosion. We present a tool for reducing the impact of the explosion by means of state abstraction that is (a) easy to use and understand by non-experts, and (b) agent-based in the sense that it operates on a modular representation of the system, rather than on its huge explicit state model. |
Wojciech Jamroga; Yan Kim; |
| 787 | A Human-in-the-Loop Tool for Annotating Passive Acoustic Monitoring Datasets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present an interactive machine learning tool for annotating passive acoustic monitoring datasets created for wildlife monitoring, which are time-consuming and costly to annotate manually. |
Hannes Kath; Thiago S. Gouvêa; Daniel Sonntag; |
| 788 | Optimized Crystallographic Graph Generation for Material Science Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: With the aim of training graph-based generative models of new material discovery, we propose an efficient tool to generate cutoff graphs and k-nearest-neighbours graphs of periodic structures within GPU optimization. |
Astrid Klipfel; Yaël Frégier; Adlane Sayede; Zied Bouraoui; |
| 789 | SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a novel recommendation system designed to provide real-time treatment strategies to therapists during psychotherapy sessions. |
Baihan Lin; Guillermo Cecchi; Djallel Bouneffouf; |
| 790 | Fedstellar: A Platform for Training Models in A Privacy-preserving and Decentralized Fashion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents Fedstellar, a platform for training decentralized Federated Learning (FL) models in heterogeneous topologies in terms of the number of federation participants and their connections. |
Enrique Tomás Martínez Beltrán; Pedro Miguel Sánchez Sánchez; Sergio López Bernal; Gérôme Bovet; Manuel Gil Pérez; Gregorio Martínez Pérez; Alberto Huertas Celdrán; |
| 791 | Plansformer Tool: Demonstrating Generation of Symbolic Plans Using Transformers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Plansformer is a novel tool that utilizes a fine-tuned language model based on transformer architecture to generate symbolic plans. |
Vishal Pallagani; Bharath Muppasani; Biplav Srivastava; Francesca Rossi; Lior Horesh; Keerthiram Murugesan; Andrea Loreggia; Francesco Fabiano; Rony Joseph; Yathin Kethepalli; |
| 792 | Humming2Music: Being A Composer As Long As You Can Humming Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present an automatic music generation system to lower the threshold of creating music. |
Yao Qiu; Jinchao Zhang; Huiying Ren; Yong Shan; Jie Zhou; |
| 793 | Modeling The Impact of Policy Interventions for Sustainable Development Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This could be due to interventions towards a given target, affecting other unrelated variables, and/or interventions leading to acute disparities in nearby geographic areas. In order to address such issues, we propose a novel concept called Stress Modeling that analyzes the holistic impact of a policy intervention by taking into account the interactions within a system, after the intervention. |
Sowmith Nandan Rachuri; Arpitha Malavalli; Niharika Sri Parasa; Pooja Bassin; Srinath Srinivasa; |
| 794 | LingGe: An Automatic Ancient Chinese Poem-to-Song Generation System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a novel system, named LingGe ("伶歌" in Chinese), to generate songs for ancient Chinese poems automatically. |
Yong Shan; Jinchao Zhang; Huiying Ren; Yao Qiu; Jie Zhou; |
| 795 | AutoML for Outlier Detection with Optimal Transport Distances Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose `"LOTUS", a novel framework to automate outlier detection based on meta-learning. |
Prabhant Singh; Joaquin Vanschoren; |
| 796 | VideoMaster: A Multimodal Micro Game Video Recreator Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To free human from laborious video production, this paper proposes the building of VideoMaster, a multimodal system equipped with four capabilities: highlight extraction, video describing, video dubbing and video editing. |
Yipeng Yu; Xiao Chen; Hui Zhan; |
| 797 | Matting Moments: A Unified Data-Driven Matting Engine for Mobile AIGC in Photo Gallery Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the development of mobile storage and computing power, achieving diverse mobile Artificial Intelligence Generated Content (AIGC) applications remains a great challenge. To address this issue, we present an innovative demonstration of an automatic system called "Matting Moments" that enables automatic image editing based on matting models in different scenarios. |
Yanhao Zhang; Fanyi Wang; Weixuan Sun; Jingwen Su; Peng Liu; Yaqian Li; Xinjie Feng; Zhengxia Zou; |
| 798 | IMPsys: An Intelligent Mold Processing System for Smart Factory Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Machining mold components is a crucial step in the mold production process for many industries, which creates (e.g., cutting, drilling, and shaping a metal) the individual parts (e.g., core pins, ejector pins, cavities, slides, and lifters) that make up a mold used in manufacturing. We present IMPsys, an AI-based system that automatically explores machining jobs, infers their processing time and schedules them on machines, given numerous 3D modelling files of mold components. |
Xueyi Zhou; Yohan Na; Minju Bang; Dong-Kyu Chae; |
| 799 | Rubik’s Optical Neural Networks: Multi-task Learning with Physics-aware Rotation Architecture Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work presents a novel ONNs architecture, namely, RubikONNs, which utilizes the physical properties of optical systems to encode multiple feed-forward functions by physically rotating the hardware similarly to rotating a Rubik’s Cube. |
Yingjie Li; Weilu Gao; Cunxi Yu; |
| 800 | Imbalanced Node Classification Beyond Homophilic Assumption Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, they uniformly aggregate features from both homophilic and heterophilic neighbors and rely on feature similarity to generate synthetic edges, which cannot be applied to imbalanced graphs in high heterophily. To address this problem, we propose a novel GraphSANN for imbalanced node classification on both homophilic and heterophilic graphs. |
Jie Liu; Mengting He; Guangtao Wang; Quoc Viet Hung Nguyen; Xuequn Shang; Hongzhi Yin; |
| 801 | Dual Prompt Learning for Continual Rain Removal from Single Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate the continual learning issue for rain removal and develop a novel efficient continual learned deraining transformer. |
Minghao Liu; Wenhan Yang; Yuzhang Hu; Jiaying Liu; |
| 802 | Multi-objective Search Via Lazy and Efficient Dominance Checks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we first propose Linear-Time Multi-Objective A* (LTMOA*), an multi-objective search algorithm that implements a more efficient dominance checking than EMOA* using simple data structures like arrays. We then propose an even lazier approach towards dominance checking, and the resulting algorithm, LazyLTMOA*, distinguishes from EMOA* and LTMOA* by removing the dominance checking during node generation. |
Carlos Hernández; William Yeoh; Jorge A. Baier; Ariel Felner; Oren Salzman; Han Zhang; Shao-Hung Chan; Sven Koenig; |
| 803 | Bayesian Federated Learning: A Survey Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Bayesian federated learning (BFL) has emerged as a promising approach to address these issues. This survey presents a critical overview of BFL, including its basic concepts, its relations to Bayesian learning in the context of FL, and a taxonomy of BFL from both Bayesian and federated perspectives. |
Longbing Cao; Hui Chen; Xuhui Fan; Joao Gama; Yew-Soon Ong; Vipin Kumar; |