Most Influential IJCAI Papers (2024-09)
International Joint Conference on Artificial Intelligence (IJCAI) is one of the top artificial intelligence conferences in the world. Paper Digest Team analyzes all papers published on IJCAI in the past years, and presents the 15 most influential papers for each year. This ranking list is automatically constructed based upon citations from both research papers and granted patents, and will be frequently updated to reflect the most recent changes. To find the latest version of this list or the most influential papers from other conferences/journals, please visit Best Paper Digest page. Note: the most influential papers may or may not include the papers that won the best paper awards. (Version: 2024-09)
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TABLE 1: Most Influential IJCAI Papers (2024-09)
| Year | Rank | Paper | Author(s) |
|---|---|---|---|
| 2024 | 1 | Large Language Model Based Multi-agents: A Survey of Progress and Challenges IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recently, LLM-based agent systems have rapidly evolved from single-agent planning or decision-making to operating as multi-agent systems, enhancing their ability in complex problem-solving and world simulation. To offer an overview of this dynamic field, we present this survey to offer an in-depth discussion on the essential aspects and challenges of LLM-based multi-agent (LLM-MA) systems. |
TAICHENG GUO et. al. |
| 2024 | 2 | AutoAgents: A Framework for Automatic Agent Generation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most existing LLM-based multi-agent approaches rely on predefined agents to handle simple tasks, limiting the adaptability of multi-agent collaboration to different scenarios. Therefore, we introduce AutoAgents, an innovative framework that adaptively generates and coordinates multiple specialized agents to build an AI team according to different tasks. |
GUANGYAO CHEN et. al. |
| 2024 | 3 | ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image Recognition IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the problem, this paper proposes prototypical part Transformer (ProtoPFormer) for interpretable image recognition. |
MENGQI XUE et. al. |
| 2024 | 4 | ChatSpot: Bootstrapping Multimodal LLMs Via Precise Referring Instruction Tuning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we present precise referring instructions that utilize diverse reference representations such as points and boxes as referring prompts to refer to the special region. |
LIANG ZHAO et. al. |
| 2024 | 5 | A Survey of Graph Meets Large Language Model: Progress and Future Directions IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Graph plays a significant role in representing and analyzing complex relationships in real-world applications such as citation networks, social networks, and biological data. … |
YUHAN LI et. al. |
| 2024 | 6 | Large Language Models for Time Series: A Survey IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We address the inherent challenge of bridging the gap between LLMs’ original text data training and the numerical nature of time series data, and explore strategies for transferring and distilling knowledge from LLMs to numerical time series analysis. |
Xiyuan Zhang; Ranak Roy Chowdhury; Rajesh K. Gupta; Jingbo Shang; |
| 2024 | 7 | Continual Learning with Pre-Trained Models: A Survey IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a comprehensive survey of the latest advancements in PTM-based CL. |
Da-Wei Zhou; Hai-Long Sun; Jingyi Ning; Han-Jia Ye; De-Chuan Zhan; |
| 2024 | 8 | A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this survey, we aim to provide a comprehensive understanding of graph reduction methods, including graph sparsification, graph coarsening, and graph condensation. |
MOHAMMAD HASHEMI et. al. |
| 2024 | 9 | ScreenAI: A Vision-Language Model for UI and Infographics Understanding IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Screen user interfaces (UIs) and infographics, sharing similar visual language and design principles, play important roles in human communication and human-machine interaction.We … |
GILLES BAECHLER et. al. |
| 2024 | 10 | Empowering Time Series Analysis with Large Language Models: A Survey IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this survey, we provide a systematic overview of existing methods that leverage LLMs for time series analysis. |
YUSHAN JIANG et. al. |
| 2024 | 11 | Reinforcement Learning from Diverse Human Preferences IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing methods for preference-based RL are limited by the need for accurate oracle preference labels. This paper addresses this limitation by developing a method for learning from diverse human preferences. |
Wanqi Xue; Bo An; Shuicheng Yan; Zhongwen Xu; |
| 2024 | 12 | D3ETR: Decoder Distillation for Detection Transformer IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose MixMatcher that aligns the de- coder outputs of DETR-based teacher and student, by mixing two teacher-student matching strategies for combined advantages. |
Xiaokang Chen; Jiahui Chen; Yan Liu; Jiaxiang Tang; Gang Zeng; |
| 2024 | 13 | Towards Generalizable Neural Solvers for Vehicle Routing Problems Via Ensemble with Transferrable Local Policy IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To make neural VRP solvers more practical, we design an auxiliary policy that learns from the local transferable topological features, named local policy, and integrate it with a typical construction policy (which learns from the global information of VRP instances) to form an ensemble policy. |
Chengrui Gao; Haopu Shang; Ke Xue; Dong Li; Chao Qian; |
| 2024 | 14 | Beyond The Limits: A Survey of Techniques to Extend The Context Length in Large Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The diverse methodologies investigated in this study can be leveraged across different phases of LLMs, i.e., training, fine-tuning and inference. |
XINDI WANG et. al. |
| 2024 | 15 | Probabilistic Contrastive Learning for Domain Adaptation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We find that this is mainly because the class weights (weights of the final fully connected layer) are ignored in the domain adaptation optimization process, which makes it difficult for features to cluster around the corresponding class weights. To solve this problem, we propose the simple but powerful Probabilistic Contrastive Learning (PCL), which moves beyond the standard paradigm by removing l2 normalization and replacing the features with probabilities. |
JUNJIE LI et. al. |
| 2023 | 1 | Transformers in Time Series: A Survey IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we systematically review Transformer schemes for time series modeling by highlighting their strengths as well as limitations. |
QINGSONG WEN et. al. |
| 2023 | 2 | On The Paradox of Learning to Reason from Data IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts 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; |
| 2023 | 3 | Communication-Efficient Stochastic Gradient Descent Ascent with Momentum Algorithms IF:3 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; |
| 2023 | 4 | Temporal Knowledge Graph Completion: A Survey IF:3 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 et. al. |
| 2023 | 5 | Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts 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 et. al. |
| 2023 | 6 | On Efficient Transformer-Based Image Pre-training for Low-Level Vision IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts 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; |
| 2023 | 7 | MM-PCQA: Multi-Modal Learning for No-reference Point Cloud Quality Assessment IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts 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 et. al. |
| 2023 | 8 | Graph-based Molecular Representation Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts 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 et. al. |
| 2023 | 9 | Pyramid Diffusion Models for Low-light Image Enhancement IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts 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; |
| 2023 | 10 | DiffuseStyleGesture: Stylized Audio-Driven Co-Speech Gesture Generation with Diffusion Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts 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 et. al. |
| 2023 | 11 | Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-training IF:3 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; |
| 2023 | 12 | Generative Diffusion Models on Graphs: Methods and Applications IF:3 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 et. al. |
| 2023 | 13 | State-wise Safe Reinforcement Learning: A Survey IF:3 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; |
| 2023 | 14 | A Systematic Survey of Chemical Pre-trained Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts 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; |
| 2023 | 15 | ProMix: Combating Label Noise Via Maximizing Clean Sample Utility IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts 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 et. al. |
| 2022 | 1 | The Shapley Value in Machine Learning IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We examine the most crucial limitations of the Shapley value and point out directions for future research. |
BENEDEK ROZEMBERCZKI et. al. |
| 2022 | 2 | A Survey of Vision-Language Pre-Trained Models IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we review the recent progress in Vision-Language Pre-Trained Models (VL-PTMs). |
Yifan Du; Zikang Liu; Junyi Li; Wayne Xin Zhao; |
| 2022 | 3 | FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes FastDiff, a fast conditional diffusion model for high-quality speech synthesis. |
RONGJIE HUANG et. al. |
| 2022 | 4 | Unsupervised Misaligned Infrared and Visible Image Fusion Via Cross-Modality Image Generation and Registration IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To overcome the obstacles, in this paper, we present a robust cross-modality generation-registration paradigm for unsupervised misaligned infrared and visible image fusion (IVIF). |
Di Wang; Jinyuan Liu; Xin Fan; Risheng Liu; |
| 2022 | 5 | SVTR: Scene Text Recognition with A Single Visual Model IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose a Single Visual model for Scene Text recognition within the patch-wise image tokenization framework, which dispenses with the sequential modeling entirely. |
YONGKUN DU et. al. |
| 2022 | 6 | Boundary-Guided Camouflaged Object Detection IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing deep-learning methods often fall into the difficulty of accurately identifying the camouflaged object with complete and fine object structure. To this end, in this paper, we propose a novel boundary-guided network (BGNet) for camouflaged object detection. |
Yujia Sun; Shuo Wang; Chenglizhao Chen; Tian-Zhu Xiang; |
| 2022 | 7 | CAT: Customized Adversarial Training for Improved Robustness IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, we show it would lead worse training and generalizaiton error and forcing the prediction to match one-hot label. In this paper, therefore, we propose a new algorithm, named Customized Adversarial Training (CAT), which adaptively customizes the perturbation level and the corresponding label for each training sample in adversarial training. |
Minhao Cheng; Qi Lei; Pin-Yu Chen; Inderjit Dhillon; Cho-Jui Hsieh; |
| 2022 | 8 | FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most existing quantization methods have been developed mainly on Convolutional Neural Networks (CNNs), and suffer severe degradation when applied to fully quantized vision transformers. In this work, we demonstrate that many of these difficulties arise because of serious inter-channel variation in LayerNorm inputs, and present, Power-of-Two Factor (PTF), a systematic method to reduce the performance degradation and inference complexity of fully quantized vision transformers. |
Yang Lin; Tianyu Zhang; Peiqin Sun; Zheng Li; Shuchang Zhou; |
| 2022 | 9 | Learning Multi-dimensional Edge Feature-based AU Relation Graph for Facial Action Unit Recognition IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes an AU relationship modelling approach that deep learns a unique graph to explicitly describe the relationship between each pair of AUs of the target facial display. |
Cheng Luo; Siyang Song; Weicheng Xie; Linlin Shen; Hatice Gunes; |
| 2022 | 10 | Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel ensemble knowledge transfer method named Fed-ET in which small models (different in architecture) are trained on clients, and used to train a larger model at the server. |
Yae Jee Cho; Andre Manoel; Gauri Joshi; Robert Sim; Dimitrios Dimitriadis; |
| 2022 | 11 | AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose AutoAlign, an automatic feature fusion strategy for 3D object detection. |
ZEHUI CHEN et. al. |
| 2022 | 12 | CERT: Continual Pre-training on Sketches for Library-oriented Code Generation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate how to leverage an unlabelled code corpus to train a model for library-oriented code generation. |
DAOGUANG ZAN et. al. |
| 2022 | 13 | A Survey on Dialogue Summarization: Recent Advances and New Frontiers IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, there still remains a lack of a comprehensive survey for this task. To this end, we take the first step and present a thorough review of this research field carefully and widely. |
Xiachong Feng; Xiaocheng Feng; Bing Qin; |
| 2022 | 14 | SparseTT: Visual Tracking with Sparse Transformers IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, self-attention lacks focusing on the most relevant information in the search regions, making it easy to be distracted by background. In this paper, we relieve this issue with a sparse attention mechanism by focusing the most relevant information in the search regions, which enables a much accurate tracking. |
Zhihong Fu; Zehua Fu; Qingjie Liu; Wenrui Cai; Yunhong Wang; |
| 2022 | 15 | Goal-Conditioned Reinforcement Learning: Problems and Solutions IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this survey, we provide a comprehensive overview of the challenges and algorithms for GCRL. |
Minghuan Liu; Menghui Zhu; Weinan Zhang; |
| 2021 | 1 | Generalizing to Unseen Domains: A Survey on Domain Generalization IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents the first review for recent advances in domain generalization. Third, we introduce the commonly used datasets and applications. |
Jindong Wang; Cuiling Lan; Chang Liu; Yidong Ouyang; Tao Qin; |
| 2021 | 2 | Time Series Data Augmentation for Deep Learning: A Survey IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we systematically review different data augmentation methods for time series. |
QINGSONG WEN et. al. |
| 2021 | 3 | Recent Advances in Adversarial Training for Adversarial Robustness IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For the first time in this survey, we systematically review the recent progress on adversarial training for adversarial robustness with a novel taxonomy. |
Tao Bai; Jinqi Luo; Jun Zhao; Bihan Wen; Qian Wang; |
| 2021 | 4 | Time-Series Representation Learning Via Temporal and Contextual Contrasting IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an unsupervised Time-Series representation learning framework via Temporal and Contextual Contrasting (TS-TCC), to learn time-series representation from unlabeled data. |
EMADELDEEN ELDELE et. al. |
| 2021 | 5 | Combinatorial Optimization and Reasoning with Graph Neural Networks IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a conceptual review of recent key advancements in this emerging field, aiming at researchers in both optimization and machine learning. |
QUENTIN CAPPART et. al. |
| 2021 | 6 | The Surprising Power of Graph Neural Networks with Random Node Initialization IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we analyze the expressive power of GNNs with RNI, and prove that these models are universal, a first such result for GNNs not relying on computationally demanding higher-order properties. |
Ralph Abboud; İsmail İlkan Ceylan; Martin Grohe; Thomas Lukasiewicz; |
| 2021 | 7 | Automated Fact-Checking for Assisting Human Fact-Checkers IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we survey the available intelligent technologies that can support the human expert in the different steps of her fact-checking endeavor. |
PRESLAV NAKOV et. al. |
| 2021 | 8 | Towards Understanding The Spectral Bias of Deep Learning IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we give a comprehensive and rigorous explanation for spectral bias and relate it with the neural tangent kernel function proposed in recent work. |
Yuan Cao; Zhiying Fang; Yue Wu; Ding-Xuan Zhou; Quanquan Gu; |
| 2021 | 9 | Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we theoretically show that the KL divergence loss focuses on the logit matching when ? increases and the label matching when ? goes to 0 and empirically show that the logit matching is positively correlated to performance improvement in general. |
Taehyeon Kim; Jaehoon Oh; Nak Yil Kim; Sangwook Cho; Se-Young Yun; |
| 2021 | 10 | Graph Learning Based Recommender Systems: A Review IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we provide a systematic review of GLRS, by discussing how they extract knowledge from graphs to improve the accuracy, reliability and explainability of the recommendations. |
SHOUJIN WANG et. al. |
| 2021 | 11 | Cross-Domain Recommendation: Challenges, Progress, and Prospects IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To fill this gap, in this paper, we provide a comprehensive review of existing CDR approaches, including challenges, research progress, and prospects. We then present the definitions and challenges of these CDR approaches. |
FENG ZHU et. al. |
| 2021 | 12 | End-to-End Constrained Optimization Learning: A Survey IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a conceptual review of the recent advancements in this emerging area. |
James Kotary; Ferdinando Fioretto; Pascal Van Hentenryck; Bryan Wilder; |
| 2021 | 13 | Context-aware Cross-level Fusion Network for Camouflaged Object Detection IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Context-aware Cross level Fusion Network (C2F-Net) to address the challenging COD task. |
Yujia Sun; Geng Chen; Tao Zhou; Yi Zhang; Nian Liu; |
| 2021 | 14 | LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we proposed a novel design of local differential privacy mechanism for federated learning to address the abovementioned issues. |
Lichao Sun; Jianwei Qian; Xun Chen; |
| 2021 | 15 | Source-free Domain Adaptation Via Avatar Prototype Generation and Adaptation IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a Contrastive Prototype Generation and Adaptation (CPGA) method. |
ZHEN QIU et. al. |
| 2020 | 1 | Transformers As Soft Reasoners Over Language IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper investigates a modern approach to this problem where the facts and rules are provided as natural language sentences, thus bypassing a formal representation. |
Peter Clark; Oyvind Tafjord; Kyle Richardson; |
| 2020 | 2 | LogiQA: A Challenge Dataset For Machine Reading Comprehension With Logical Reasoning IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We build a comprehensive dataset, named LogiQA, which is sourced from expert-written questions for testing human Logical reasoning. |
JIAN LIU et. al. |
| 2020 | 3 | Deep Learning For Community Detection: Progress, Challenges And Opportunities IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Structured into three broad research streams in this domain – deep neural networks, deep graph embedding, and graph neural networks, this article summarizes the contributions of the various frameworks, models, and algorithms in each stream along with the current challenges that remain unsolved and the future research opportunities yet to be explored. |
FANZHEN LIU et. al. |
| 2020 | 4 | Smart Contract Vulnerability Detection Using Graph Neural Network IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explore using graph neural networks (GNNs) for smart contract vulnerability detection. |
YUAN ZHUANG et. al. |
| 2020 | 5 | Channel Pruning Via Automatic Structure Search IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new channel pruning method based on artificial bee colony algorithm (ABC), dubbed as ABCPruner, which aims to efficiently find optimal pruned structure, i.e., channel number in each layer, rather than selecting "important" channels as previous works did. |
MINGBAO LIN et. al. |
| 2020 | 6 | FakeSpotter: A Simple Yet Robust Baseline For Spotting AI-Synthesized Fake Faces IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel approach, named FakeSpotter, based on monitoring neuron behaviors to spot AI-synthesized fake faces. |
RUN WANG et. al. |
| 2020 | 7 | LSGCN: Long Short-Term Traffic Prediction With Graph Convolutional Networks IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In our framework, we propose a new graph attention network called cosAtt, and integrate both cosAtt and graph convolution networks (GCN) into a spatial gated block. |
Rongzhou Huang; Chuyin Huang; Yubao Liu; Genan Dai; Weiyang Kong; |
| 2020 | 8 | Closing The Generalization Gap Of Adaptive Gradient Methods In Training Deep Neural Networks IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we show that adaptive gradient methods such as Adam, Amsgrad, are sometimes "over adapted". |
JINGHUI CHEN et. al. |
| 2020 | 9 | Evaluating And Aggregating Feature-based Model Explanations IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes quantitative evaluation criteria for feature-based explanations: low sensitivity, high faithfulness, and low complexity. |
Umang Bhatt; Adrian Weller; José M. F. Moura; |
| 2020 | 10 | A Survey On Computational Propaganda Detection IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this survey, we review the state of the art on computational propaganda detection from the perspective of Natural Language Processing and Network Analysis, arguing about the need for combined efforts between these communities. |
GIOVANNI DA SAN MARTINO et. al. |
| 2020 | 11 | Graph Neural Architecture Search IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a graph neural architecture search method (GraphNAS) that enables automatic design of the best graph neural architecture based on reinforcement learning. |
Yang Gao; Hong Yang; Peng Zhang; Chuan Zhou; Yue Hu; |
| 2020 | 12 | DIDFuse: Deep Image Decomposition For Infrared And Visible Image Fusion IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a novel auto-encoder (AE) based fusion network. |
ZIXIANG ZHAO et. al. |
| 2020 | 13 | GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks For Sleep Stage Classification IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel deep graph neural network, named GraphSleepNet, for automatic sleep stage classification. |
ZIYU JIA et. al. |
| 2020 | 14 | Learning To Complement Humans IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We demonstrate how an end-to-end learning strategy can be harnessed to optimize the combined performance of human-machine teams by considering the distinct abilities of people and machines. |
Bryan Wilder; Eric Horvitz; Ece Kamar; |
| 2020 | 15 | Automatic Curriculum Learning For Deep RL: A Short Survey IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The ambition of this work is dual: 1) to present a compact and accessible introduction to the Automatic Curriculum Learning literature and 2) to draw a bigger picture of the current state of the art in ACL to encourage the cross-breeding of existing concepts and the emergence of new ideas. |
Rémy Portelas; Cédric Colas; Lilian Weng; Katja Hofmann; Pierre-Yves Oudeyer; |
| 2019 | 1 | Graph WaveNet For Deep Spatial-Temporal Graph Modeling IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To overcome these limitations, we propose in this paper a novel graph neural network architecture, {Graph WaveNet}, for spatial-temporal graph modeling. |
Zonghan Wu; Shirui Pan; Guodong Long; Jing Jiang; Chengqi Zhang; |
| 2019 | 2 | Interpolation Consistency Training For Semi-supervised Learning IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce Interpolation Consistency Training (ICT), a simple and computation efficient algorithm for training Deep Neural Networks in the semi-supervised learning paradigm. |
Vikas Verma; Alex Lamb; Juho Kannala; Yoshua Bengio; David Lopez-Paz; |
| 2019 | 3 | Graph Contextualized Self-Attention Network For Session-based Recommendation IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a graph contextualized self-attention model (GC-SAN), which utilizes both graph neural network and self-attention mechanism, for session-based recommendation. |
CHENGFENG XU et. al. |
| 2019 | 4 | Attributed Graph Clustering: A Deep Attentional Embedding Approach IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a goal-directed deep learning approach, Deep Attentional Embedded Graph Clustering (DAEGC for short). |
CHUN WANG et. al. |
| 2019 | 5 | LogAnomaly: Unsupervised Detection Of Sequential And Quantitative Anomalies In Unstructured Logs IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose LogAnomaly, a framework to model unstructured a log stream as a natural language sequence. |
WEIBIN MENG et. al. |
| 2019 | 6 | Topology Attack And Defense For Graph Neural Networks: An Optimization Perspective IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, leveraging our gradient-based attack, we propose the first optimization-based adversarial training for GNNs. |
KAIDI XU et. al. |
| 2019 | 7 | Adversarial Examples For Graph Data: Deep Insights Into Attack And Defense IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose techniques for both an adversarial attack and a defense against adversarial attacks. |
HUIJUN WU et. al. |
| 2019 | 8 | DeepInspect: A Black-box Trojan Detection And Mitigation Framework For Deep Neural Networks IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our goal in this paper is to address the security concern on unknown DNN to NT attacks and ensure safe model deployment. |
Huili Chen; Cheng Fu; Jishen Zhao; Farinaz Koushanfar; |
| 2019 | 9 | Relation-Aware Entity Alignment For Heterogeneous Knowledge Graphs IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Relation-aware Dual-Graph Convolutional Network (RDGCN) to incorporate relation information via attentive interactions between the knowledge graph and its dual relation counterpart, and further capture neighboring structures to learn better entity representations. |
YUTING WU et. al. |
| 2019 | 10 | Dynamic Hypergraph Neural Networks IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle this issue, we propose a dynamic hypergraph neural networks framework (DHGNN), which is composed of the stacked layers of two modules: dynamic hypergraph construction (DHG) and hypergrpah convolution (HGC). |
Jianwen Jiang; Yuxuan Wei; Yifan Feng; Jingxuan Cao; Yue Gao; |
| 2019 | 11 | Deep Session Interest Network For Click-Through Rate Prediction IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on this observation, we propose a novel CTR model named Deep Session Interest Network (DSIN) that leverages users’ multiple historical sessions in their behavior sequences. |
YUFEI FENG et. al. |
| 2019 | 12 | Feature-level Deeper Self-Attention Network For Sequential Recommendation IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel method named Feature-level Deeper Self-Attention Network (FDSA) for sequential recommendation. |
TINGTING ZHANG et. al. |
| 2019 | 13 | Attributed Graph Clustering Via Adaptive Graph Convolution IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an adaptive graph convolution method for attributed graph clustering that exploits high-order graph convolution to capture global cluster structure and adaptively selects the appropriate order for different graphs. |
Xiaotong Zhang; Han Liu; Qimai Li; Xiao-Ming Wu; |
| 2019 | 14 | Pre-training Of Graph Augmented Transformers For Medication Recommendation IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address these challenges, we propose G-BERT, a new model to combine the power of Graph Neural Networks (GNNs) and BERT (Bidirectional Encoder Representations from Transformers) for medical code representation and medication recommendation. |
Junyuan Shang; Tengfei Ma; Cao Xiao; Jimeng Sun; |
| 2019 | 15 | Neural News Recommendation With Attentive Multi-View Learning IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we propose a neural news recommendation approach which can learn informative representations of users and news by exploiting different kinds of news information. |
CHUHAN WU et. al. |
| 2018 | 1 | Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework For Traffic Forecasting IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain. |
Bing Yu; Haoteng Yin; Zhanxing Zhu; |
| 2018 | 2 | Enhanced-alignment Measure For Binary Foreground Map Evaluation IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we take a detailed look at current binary FM evaluation measures and propose a novel and effective E-measure (Enhanced-alignment measure). |
DENG-PING FAN et. al. |
| 2018 | 3 | Soft Filter Pruning For Accelerating Deep Convolutional Neural Networks IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposed a Soft Filter Pruning (SFP) method to accelerate the inference procedure of deep Convolutional Neural Networks (CNNs). |
Yang He; Guoliang Kang; Xuanyi Dong; Yanwei Fu; Yi Yang; |
| 2018 | 4 | Generating Adversarial Examples With Adversarial Networks IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose AdvGAN to generate adversarial exam- ples with generative adversarial networks (GANs), which can learn and approximate the distribution of original instances. |
CHAOWEI XIAO et. al. |
| 2018 | 5 | Behavioral Cloning From Observation IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a two-phase, autonomous imitation learning technique called behavioral cloning from observation (BCO), that aims to provide improved performance with respect to both of these aspects. |
Faraz Torabi; Garrett Warnell; Peter Stone; |
| 2018 | 6 | A Genetic Programming Approach To Designing Convolutional Neural Network Architectures IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a method for designing convolutional neural network (CNN) architectures based on Cartesian genetic programming (CGP). |
Masanori Suganuma; Shinichi Shirakawa; Tomoharu Nagao; |
| 2018 | 7 | Adversarially Regularized Graph Autoencoder For Graph Embedding IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel adversarial graph embedding framework for graph data. |
SHIRUI PAN et. al. |
| 2018 | 8 | Unbiased Learning-to-Rank With Biased Feedback IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To overcome this bias problem, we present a counterfactual inference framework that provides the theoretical basis for unbiased LTR via Empirical Risk Minimization despite biased data. |
Thorsten Joachims; Adith Swaminathan; Tobias Schnabel; |
| 2018 | 9 | Co-occurrence Feature Learning From Skeleton Data For Action Recognition And Detection With Hierarchical Aggregation IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we propose an end-to-end convolutional co-occurrence feature learning framework. |
Chao Li; Qiaoyong Zhong; Di Xie; Shiliang Pu; |
| 2018 | 10 | Commonsense Knowledge Aware Conversation Generation With Graph Attention IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a novel open-domain conversation generation model to demonstrate how large-scale commonsense knowledge can facilitate language understanding and generation. |
HAO ZHOU et. al. |
| 2018 | 11 | Bootstrapping Entity Alignment With Knowledge Graph Embedding IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a bootstrapping approach to embedding-based entity alignment. |
Zequn Sun; Wei Hu; Qingheng Zhang; Yuzhong Qu; |
| 2018 | 12 | R³Net: Recurrent Residual Refinement Network For Saliency Detection IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel recurrent residual refinement network (R^3Net) equipped with residual refinement blocks (RRBs) to more accurately detect salient regions of an input image. |
ZIJUN DENG et. al. |
| 2018 | 13 | GeoMAN: Multi-level Attention Networks For Geo-sensory Time Series Prediction IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we predict the readings of a geo-sensor over several future hours by using a multi-level attention-based recurrent neural network that considers multiple sensors’ readings, meteorological data, and spatial data. |
Yuxuan Liang; Songyu Ke; Junbo Zhang; Xiuwen Yi; Yu Zheng; |
| 2018 | 14 | Deep Text Classification Can Be Fooled IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present an effective method to craft text adversarial samples, revealing one important yet underestimated fact that DNN-based text classifiers are also prone to adversarial sample attack. |
BIN LIANG et. al. |
| 2018 | 15 | Cross-Modality Person Re-Identification With Generative Adversarial Training IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we tackle the above two challenges by proposing a novel cross-modality generative adversarial network (termed cmGAN). |
Pingyang Dai; Rongrong Ji; Haibin Wang; Qiong Wu; Yuyu Huang; |
| 2017 | 1 | DeepFM: A Factorization-Machine Based Neural Network For CTR Prediction IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we show that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions. |
Huifeng Guo; Ruiming TANG; Yunming Ye; Zhenguo Li; Xiuqiang He; |
| 2017 | 2 | A Dual-Stage Attention-Based Recurrent Neural Network For Time Series Prediction IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a dual-stage attention-based recurrent neural network (DA-RNN) to address these two issues. |
YAO QIN et. al. |
| 2017 | 3 | Interactive Attention Networks For Aspect-Level Sentiment Classification IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning. |
Dehong Ma; Sujian Li; Xiaodong Zhang; Houfeng Wang; |
| 2017 | 4 | Attentional Factorization Machines: Learning The Weight Of Feature Interactions Via Attention Networks IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we improve FM by discriminating the importance of different feature interactions. |
JUN XIAO et. al. |
| 2017 | 5 | Deep Matrix Factorization Models For Recommender Systems IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel matrix factorization model with neural network architecture. |
Hong-Jian Xue; Xinyu Dai; Jianbing Zhang; Shujian Huang; Jiajun Chen; |
| 2017 | 6 | Bilateral Multi-Perspective Matching For Natural Language Sentences IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a bilateral multi-perspective matching (BiMPM) model. |
Zhiguo Wang; Wael Hamza; Radu Florian; |
| 2017 | 7 | Deep Forest: Towards An Alternative To Deep Neural Networks IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose gcForest, a decision tree ensemble approach with performance highly competitive to deep neural networks in a broad range of tasks. |
Zhi-Hua Zhou; Ji Feng; |
| 2017 | 8 | Improved Deep Embedded Clustering With Local Structure Preservation IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address this issue, in this paper, we propose the Improved Deep Embedded Clustering (IDEC) algorithm to take care of data structure preservation. |
Xifeng Guo; Long Gao; Xinwang Liu; Jianping Yin; |
| 2017 | 9 | Value Iteration Networks IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce the value iteration network (VIN): a fully differentiable neural network with a `planning module’ embedded within. |
Aviv Tamar; Yi Wu; Garrett Thomas; Sergey Levine; Pieter Abbeel; |
| 2017 | 10 | Variational Deep Embedding: An Unsupervised And Generative Approach To Clustering IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Variational Deep Embedding (VaDE), a novel unsupervised generative clustering approach within the framework of Variational Auto-Encoder (VAE). |
Zhuxi Jiang; Yin Zheng; Huachun Tan; Bangsheng Tang; Hanning Zhou; |
| 2017 | 11 | Demystifying Neural Style Transfer IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel interpretation of neural style transfer by treating it as a domain adaptation problem. |
Yanghao Li; Naiyan Wang; Jiaying Liu; Xiaodi Hou; |
| 2017 | 12 | Right For The Right Reasons: Training Differentiable Models By Constraining Their Explanations IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a method to explain the predictions of any differentiable model via the gradient of the class label with respect to the input (which provides a normal to the decision boundary). |
Andrew Slavin Ross; Michael C. Hughes; Finale Doshi-Velez; |
| 2017 | 13 | Locality Preserving Matching IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We formulate the problem into a mathematical model, and derive a closed-form solution with linearithmic time and linear space complexities. |
JIAYI MA et. al. |
| 2017 | 14 | Multilingual Knowledge Graph Embeddings For Cross-lingual Knowledge Alignment IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, we propose MTransE, a translation-based model for multilingual knowledge graph embeddings, to provide a simple and automated solution. |
Muhao Chen; Yingtao Tian; Mohan Yang; Carlo Zaniolo; |
| 2017 | 15 | Self-weighted Multiview Clustering With Multiple Graphs IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we address this problem by exploring a Laplacian rank constrained graph, which can be approximately as the centroid of the built graph for each view with different confidences. |
Feiping Nie; Jing Li; Xuelong Li; |
| 2016 | 1 | Recurrent Neural Network For Text Classification With Multi-Task Learning IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we use the multi-task learning framework to jointly learn across multiple related tasks. |
Pengfei Liu; Xipeng Qiu; Xuanjing Huang; |
| 2016 | 2 | Detecting Rumors From Microblogs With Recurrent Neural Networks IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a novel method that learns continuous representations of microblog events for identifying rumors. |
JING MA et. al. |
| 2016 | 3 | Deep, Convolutional, And Recurrent Models For Human Activity Recognition Using Wearables IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we rigorously explore deep, convolutional, and recurrent approaches across three representative datasets that contain movement data captured with wearable sensors. |
Nils Y. Hammerla; Shane Halloran; Thomas Plötz; |
| 2016 | 4 | Feature Learning Based Deep Supervised Hashing With Pairwise Labels IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel deep hashing method, called deep pairwise-supervised hashing (DPSH), to perform simultaneous feature learning and hash-code learning for applications with pairwise labels. |
Wu-Jun Li; Sheng Wang; Wang-Cheng Kang; |
| 2016 | 5 | Deep Neural Decision Forests IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a novel approach to enrich classification trees with the representation learning ability of deep (neural) networks within an end-to-end trainable architecture. |
Peter Kontschieder; Madalina Fiterau; Antonio Criminisi; Samuel Rota Bulò; |
| 2016 | 6 | Parameter-Free Auto-Weighted Multiple Graph Learning: A Framework For Multiview Clustering And Semi-Supervised Classification IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on the real-world applications where the same instance can be represented by multiple heterogeneous features. |
Feiping Nie; Jing Li; Xuelong Li; |
| 2016 | 7 | Tri-Party Deep Network Representation IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose TriDNR, a tri-party deep network representation model, using information from three parties: node structure, node content, and node labels (if available) to jointly learn optimal node representation. |
Shirui Pan; Jia Wu; Xingquan Zhu; Chengqi Zhang; Yang Wang; |
| 2016 | 8 | The Malmo Platform For Artificial Intelligence Experimentation IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present Project Malmo – an AI experimentation platform built on top of the popular computer game Minecraft, and designed to support fundamental research in artificial intelligence. |
Matthew Johnson; Katja Hofmann; Tim Hutton; David Bignell; |
| 2016 | 9 | A Joint Model Of Intent Determination And Slot Filling For Spoken Language Understanding IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on the idea that the intent and semantic slots of a sentence are correlative, we propose a joint model for both tasks. |
Xiaodong Zhang; Houfeng Wang; |
| 2016 | 10 | Representation Learning Of Knowledge Graphs With Hierarchical Types IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel method named Type-embodied Knowledge Representation Learning (TKRL) to take advantages of hierarchical entity types. |
Ruobing Xie; Zhiyuan Liu; Maosong Sun; |
| 2016 | 11 | Staleness-Aware Async-SGD For Distributed Deep Learning IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a variant of the ASGD algorithm in which the learning rate is modulated according to the gradient staleness and provide theoretical guarantees for convergence of this algorithm. |
Wei Zhang; Suyog Gupta; Xiangru Lian; Ji Liu; |
| 2016 | 12 | Aligning Users Across Social Networks Using Network Embedding IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we adopt the representation learning approach to align users across multiple social networks where the social structures of the users are exploited. |
Li Liu; William K. Cheung; Xin Li; Lejian Liao; |
| 2016 | 13 | Max-Margin DeepWalk: Discriminative Learning Of Network Representation IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we overcome this challenge by proposing a novel semi-supervised model, max-margin DeepWalk (MMDW). |
Cunchao Tu; Weicheng Zhang; Zhiyuan Liu; Maosong Sun; |
| 2016 | 14 | Predict Anchor Links Across Social Networks Via An Embedding Approach IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To offer a robust method, we propose a novel supervised model, called PALE, which employs network embedding with awareness of observed anchor links as supervised information to capture the major and specific structural regularities and further learns a stable cross-network mapping for predicting anchor links. |
Tong Man; Huawei Shen; Shenghua Liu; Xiaolong Jin; Xueqi Cheng; |
| 2016 | 15 | Deep Subspace Clustering With Sparsity Prior IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel subspace clustering method — deeP subspAce clusteRing with sparsiTY prior (PARTY) — based on a new deep learning architecture. |
Xi Peng; Shijie Xiao; Jiashi Feng; Wei-Yun Yau; Zhang Yi; |
| 2015 | 1 | Deep Convolutional Neural Networks On Multichannel Time Series For Human Activity Recognition IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a systematic feature learning method for HAR problem. |
Jianbo Yang; Minh Nhut Nguyen; Phyo Phyo San; Xiao Li Li; Shonali Krishnaswamy; |
| 2015 | 2 | Network Representation Learning With Rich Text Information IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: By proving that DeepWalk, a state-of-the-art network representation method, is actually equivalent to matrix factorization (MF), we propose text-associated DeepWalk (TADW). |
Cheng Yang; Zhiyuan Liu; Deli Zhao; Maosong Sun; Edward Chang; |
| 2015 | 3 | Deep Learning For Event-Driven Stock Prediction IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a deep learning method for event-driven stock market prediction. |
Xiao Ding; Yue Zhang; Ting Liu; Junwen Duan; |
| 2015 | 4 | Using Social Media To Enhance Emergency Situation Awareness: Extended Abstract IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present key relevant approaches that we have investigated including burst detection, tweet filtering and classification, online clustering, and geotagging. |
JIE YIN et. al. |
| 2015 | 5 | Speeding Up Automatic Hyperparameter Optimization Of Deep Neural Networks By Extrapolation Of Learning Curves IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Automated hyperparameter optimization methods have recently been shown to yield settings competitive with those found by human experts, but their widespread adoption is hampered by the fact that they require more computational resources than human experts. |
Tobias Domhan; Jost Tobias Springenberg; Frank Hutter; |
| 2015 | 6 | Imaging Time-Series To Improve Classification And Imputation IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by recent successes of deep learning in computer vision, we propose a novel framework for encoding time series as different types of images, namely, Gramian Angular Summation/Difference Fields (GASF/GADF) and Markov Transition Fields (MTF). |
Zhiguang Wang; Tim Oates; |
| 2015 | 7 | Personalized Ranking Metric Embedding For Next New POI Recommendation IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a personalized ranking metric embedding method (PRME) to model personalized check-in sequences. |
SHANSHAN FENG et. al. |
| 2015 | 8 | Action2Activity: Recognizing Complex Activities From Sensor Data IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, this paper presents a novel approach for complex activity recognition comprising of two components. |
Ye Liu; Liqiang Nie; Lei Han; Luming Zhang; David S. Rosenblum; |
| 2015 | 9 | Target-Dependent Twitter Sentiment Classification With Rich Automatic Features IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we show that competitive results can be achieved without the use of syntax, by extracting a rich set of automatic features. |
Tin Duy Vo; Yue Zhang; |
| 2015 | 10 | Supervised Representation Learning: Transfer Learning With Deep Autoencoders IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a supervised representation learning method based on deep autoencoders for transfer learning. |
Fuzhen Zhuang; Xiaohu Cheng; Ping Luo; Sinno Jialin Pan; Qing He; |
| 2015 | 11 | Logic-Geometric Programming: An Optimization-Based Approach To Combined Task And Motion Planning IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose to formulate the problem holistically as a 1st-order logic extension of a mathematical program: a non-linear constrained program over the full world trajectory where the symbolic state-action sequence defines the (in-)equality constraints. |
Marc Toussaint; |
| 2015 | 12 | Joint Learning Of Character And Word Embeddings IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Hence, we take Chinese for example, and present a character-enhanced word embedding model (CWE). |
Xinxiong Chen; Lei Xu; Zhiyuan Liu; Maosong Sun; Huanbo Luan; |
| 2015 | 13 | Convolutional Neural Tensor Network Architecture For Community-Based Question Answering IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a convolutional neural tensor network architecture to encode the sentences in semantic space and model their interactions with a tensor layer. |
Xipeng Qiu; Xuanjing Huang; |
| 2015 | 14 | Reinforcement Learning From Demonstration Through Shaping IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate the intersection of these two approaches, leveraging the theoretical guarantees provided by reinforcement learning, and using expert demonstrations to speed up this learning by biasing exploration through a process called reward shaping. |
TIM BRYS et. al. |
| 2015 | 15 | Scalable Graph Hashing With Feature Transformation IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel method, called scalable graph hashing with feature transformation (SGH), for large-scale graph hashing. |
Qing-Yuan Jiang; Wu-Jun Li; |
| 2013 | 1 | Human Action Recognition Using A Temporal Hierarchy Of Covariance Descriptors On 3D Joint Locations IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a novel approach to human action recognition from 3D skeleton sequences extracted from depth data. |
Mohamed E. Hussein; Marwan Torki; Mohammad A. Gowayyed; Motaz El-Saban; |
| 2013 | 2 | Where You Like To Go Next: Successive Point-of-Interest Recommendation IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we consider the task of successive personalized POI recommendation in LBSNs, which is a much harder task than standard personalized POI recommendation or prediction. |
Chen Cheng; Haiqin Yang; Michael R. Lyu; Irwin King; |
| 2013 | 3 | Multi-View K-Means Clustering On Big Data IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new robust large-scale multi-view clustering method to integrate heterogeneous representations of large-scale data. |
Xiao Cai; Feiping Nie; Heng Huang; |
| 2013 | 4 | Linear Temporal Logic And Linear Dynamic Logic On Finite Traces IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we look into the assumption of interpreting LTL over finite traces. |
Giuseppe De Giacomo; Moshe Y. Vardi; |
| 2013 | 5 | Learning Discriminative Representations From RGB-D Video Data IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce an adaptive learning methodology to automatically extract (holistic) spatio-temporal features, simultaneously fusing the RGB and depth information, from RGBD video data for visual recognition tasks. The proposed method is systematically evaluated on a new hand gesture dataset, SKIG, that we collected ourselves and the public MSRDailyActivity3D dataset, respectively. |
Li Liu; Ling Shao; |
| 2013 | 6 | A Novel Bayesian Similarity Measure For Recommender Systems IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To solve these issues, we propose a novel Bayesian similarity measure based on the Dirichlet distribution, taking into consideration both the direction and length of rating vectors. |
Guibing Guo; Jie Zhang; Neil Yorke-Smith; |
| 2013 | 7 | Social Influence Locality For Modeling Retweeting Behaviors IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study an interesting phenomenon of social influence locality in a large microblogging network, which suggests that users’ behaviors are mainly influenced by close friends in their ego networks. |
Jing Zhang; Biao Liu; Jie Tang; Ting Chen; Juanzi Li; |
| 2013 | 8 | Robust Unsupervised Feature Selection IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A new unsupervised feature selection method, i.e., Robust Unsupervised Feature Selection (RUFS), is proposed. |
Mingjie Qian; Chengxiang Zhai; |
| 2013 | 9 | Exploiting Local And Global Social Context For Recommendation IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Users are likely to seek suggestions from both their local friends and users with high global reputations, motivating us to exploit social relations from local and global perspectives for online recommender systems in this paper. |
Jiliang Tang; Xia Hu; Huiji Gao; Huan Liu; |
| 2013 | 10 | Exact Recovery Of Sparsely-Used Dictionaries IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We consider the problem of learning sparsely used dictionaries with an arbitrary square dictionary and a random, sparse coefficient matrix. |
Daniel A. Spielman; Huan Wang; John Wright; |
| 2013 | 11 | Meta-Interpretive Learning Of Higher-Order Dyadic Datalog: Predicate Invention Revisited IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we generalise the approach of Meta-Interpretive Learning (MIL) to that of learning higher-order dyadic datalog programs. |
Stephen Muggleton; Dianhuan Lin; |
| 2013 | 12 | GBPR: Group Preference Based Bayesian Personalized Ranking For One-Class Collaborative Filtering IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As a response, we propose a new and improved assumption, group Bayesian personalized ranking (GBPR), via introducing richer interactions among users. |
Weike Pan; Li Chen; |
| 2013 | 13 | Histogram Of Oriented Displacements (HOD): Describing Trajectories Of Human Joints For Action Recognition IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we propose a novel descriptor for 2D trajectories: Histogram of Oriented Displacements (HOD). |
Mohammad A. Gowayyed; Marwan Torki; Mohamed E. Hussein; Motaz El-Saban; |
| 2013 | 14 | Abstract Dialectical Frameworks Revisited IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present various new concepts and results related to abstract dialectical frameworks (ADFs), a powerful generalization of Dung’s argumentation frameworks (AFs). |
Gerhard Brewka; Stefan Ellmauthaler; Hannes Strass; Johannes Peter Wallner; Stefan Woltran; |
| 2013 | 15 | On Stochastic Optimal Control And Reinforcement Learning By Approximate Inference (Extended Abstract) IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a reformulation of the stochastic optimal control problem in terms of KL divergence minimisation, not only providing a unifying perspective of previous approaches in this area, but also demonstrating that the formalism leads to novel practical approaches to the control problem. |
Konrad Rawlik; Marc Toussaint; Sethu Vijayakumar; |
| 2011 | 1 | Flexible, High Performance Convolutional Neural Networks For Image Classification IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a fast, fully parameterizable GPU implementation of Convolutional Neural Network variants. |
Dan C. Ciresan; Ueli Meier; Jonathan Masci; Luca Maria Gambardella; Jürgen Schmidhuber; |
| 2011 | 2 | WSABIE: Scaling Up To Large Vocabulary Image Annotation IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a strongly performing method that scales to such datasets by simultaneously learning to optimize precision at the top of the ranked list of annotations for a given image and learning a low-dimensional joint embedding space for both images and annotations. |
Jason Weston; Samy Bengio; Nicolas Usunier; |
| 2011 | 3 | Open Information Extraction: The Second Generation IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In 2007, we introduced the Open Information Extraction (Open IE) paradigm which eschews handlabeled training examples, and avoids domain-specific verbs and nouns, to develop unlexicalized, domain-independent extractors that scale to the Web corpus. |
Oren Etzioni; Anthony Fader; Janara Christensen; Stephen Soderland; |
| 2011 | 4 | Learning Hash Functions For Cross-View Similarity Search IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work we consider the problem of learning hash functions for similarity search across the views for such applications. |
Shaishav Kumar; Raghavendra Udupa; |
| 2011 | 5 | LIFT: Multi-Label Learning With Label-Specific Features IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on the above reflection, we propose a new strategy to multi-label learning by leveraging label-specific features, where a simple yet effective algorithm named LIFT is presented. |
Min-Ling Zhang; |
| 2011 | 6 | Heterogeneous Domain Adaptation Using Manifold Alignment IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a manifold alignment based approach for heterogeneous domain adaptation. |
Chang Wang; Sridhar Mahadevan; |
| 2011 | 7 | Learning To Identify Review Spam IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study this issue in the context of our product review mining system. |
Fangtao Li; Minlie Huang; Yi Yang; Xiaoyan Zhu; |
| 2011 | 8 | Feature Learning For Activity Recognition In Ubiquitous Computing IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We investigate the potential of recent machine learning methods for discovering universal features for context-aware applications of activity recognition. |
Thomas Plötz; Nils Y. Hammerla; Patrick Olivier; |
| 2011 | 9 | The Increasing Cost Tree Search For Optimal Multi-Agent Pathfinding IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a novel formalization for this problem which includes a search tree called the increasing cost tree (ICT) and a corresponding search algorithm that finds optimal solutions. |
Guni Sharon; Roni Stern; Meir Goldenberg; Ariel Felner; |
| 2011 | 10 | SDD: A New Canonical Representation Of Propositional Knowledge Bases IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We identify a new representation of propositional knowledge bases, the Sentential Decision Diagram SDD, which is interesting for a number of reasons. |
Adnan Darwiche; |
| 2011 | 11 | Short Text Conceptualization Using A Probabilistic Knowledgebase IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We enhance machine learning algorithms by first giving the machine a probabilistic knowledgebase that contains as big, rich, and consistent concepts (of worldly facts) as those in our mental world. |
Yangqiu Song; Haixun Wang; Zhongyuan Wang; Hongsong Li; Weizhu Chen; |
| 2011 | 12 | Fast Approximate Nearest-Neighbor Search With K-Nearest Neighbor Graph IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a new nearest neighbor search al-gorithm. |
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| 2011 | 13 | Reinforcement Learning To Adjust Robot Movements To New Situations IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we describe how to learn such mappings from circumstances to meta-parameters using reinforcement learning. |
Jens Kober; Erhan Oztop; Jan Peters; |
| 2011 | 14 | Connecting The Dots Between News Articles IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate methods for automatically connecting the dots – providing a structured, easy way to navigate within a new topic and discover hidden connections. |
Dafna Shahaf; Carlos Guestrin; |
| 2011 | 15 | Joint Feature Selection And Subspace Learning IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a framework for joint feature selection and subspace learning. |
Quanquan Gu; Zhenhui Li; Jiawei Han; |
| 2009 | 1 | Domain Adaptation Via Transfer Component Analysis IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to find such a representation through a new learning method, transfer component analysis (TCA), for domain adaptation. |
Sinno Jialin Pan; Ivor W. Tsang; James T. Kwok; Qiang Yang; |
| 2009 | 2 | Predicting Learnt Clauses Quality In Modern SAT Solvers IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We report in this work a key observation of CDCL solvers behavior on this family of benchmarks and explain it by an unsuspected side effect of their particular Clause Learning scheme. |
Gilles Audemard; Laurent Simon; |
| 2009 | 3 | Expanding Domain Sentiment Lexicon Through Double Propagation IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel propagation approach that exploits the relations between sentiment words and topics or product features that the sentiment words modify, and also sentiment words and product features themselves to extract new sentiment words. |
Guang Qiu; Bing Liu; Jiajun Bu; Chun Chen; |
| 2009 | 4 | Can Movies And Books Collaborate? Cross-Domain Collaborative Filtering For Sparsity Reduction IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we consider a novel approach for alleviating the sparsity problem in CF by transferring user-item rating patterns from a dense auxiliary rating matrix in other domains (e.g., a popular movie rating website) to a sparse rating matrix in a target domain (e.g., a new book rating website). |
Bin Li; Qiang Yang; Xiangyang Xue; |
| 2009 | 5 | Sensing And Predicting The Pulse Of The City Through Shared Bicycling IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on the digital footprints from one type of emerging urban infrastructure: shared bicycling systems. |
Jon Edward Froehlich; Joachim Neumann; Nuria Oliver; |
| 2009 | 6 | Discriminative Semi-Supervised Feature Selection Via Manifold Regularization IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address this problem, we propose a novel discriminative semi-supervised feature selection method based on the idea of manifold regularization. |
Zenglin Xu; Rong Jin; Michael R. Lyu; Irwin King; |
| 2009 | 7 | Plan Recognition As Planning IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work we aim to narrow the gap between plan recognition and planning by exploiting the powerand generality of recent planning algorithms for recognizing the set G∗ of goals G that explain a sequenceof observations given a domain theory. |
Miquel Ram?rez; Hector Geffner; |
| 2009 | 8 | Stratified Planning IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel and general framework to exploit domain decomposition. |
Yixin Chen; You Xu; Guohui Yao; |
| 2009 | 9 | Conjunctive Query Answering In The Description Logic EL Using A Relational Database System IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our main contribution is a novel approach to CQ answering that enables the use of standard relational database systems as the basis for query execution. |
Carsten Lutz; David Toman; David Toman; Frank Wolter; Frank Wolter; |
| 2009 | 10 | Consequence-Driven Reasoning For Horn SHIQ Ontologies IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a novel reasoning procedure for Horn SHIQ ontologies�SHIQ ontologies that can be translated to the Horn fragment of first-order logic. |
Yevgeny Kazakov; |
| 2009 | 11 | SATenstein: Automatically Building Local Search SAT Solvers From Components IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we demonstrate that this task can be automated in the context of stochastic local search (SLS) solvers for the propositional satisfiability problem (SAT). |
Ashiqur R. KhudaBukhsh; Lin Xu; Holger H. Hoos; Kevin Leyton-Brown; |
| 2009 | 12 | Nested Monte-Carlo Search IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The algorithm is studied theoretically on simple abstract problems and applied successfully to three different games: Morpion Solitaire, SameGame and 16×16 Sudoku. |
Tristan Cazenave; |
| 2009 | 13 | Manifold Alignment Without Correspondence IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we introduce a novel manifold alignment approach, which differs from semi-supervised alignment and Procrustes alignment in that it does not require predetermining correspondences. |
Chang Wang; Sridhar Mahadevan; |
| 2009 | 14 | Locality Preserving Nonnegative Matrix Factorization IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel algorithm, called {\em Locality Preserving Non-negative Matrix Factorization} (LPNMF), for this purpose. |
Deng Cai; Xiaofei He; Xuanhui Wang; Hujun Bao; Jiawei Han; |
| 2009 | 15 | A Characterisation Of Strategy-Proofness For Grounded Argumentation Semantics IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we characterise strategy-proofness under grounded semantics for a more realistic preference class (namely, focal arguments). |
Iyad Rahwan; Kate Larson; Fernando Tohm�; |
| 2007 | 1 | Open Information Extraction From The Web IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces Open IE (OIE), a new extraction paradigm where the system makes a single data-driven pass over its corpus and extracts a large set of relational tuples without requiring any human input. |
Michele Banko; Michael J Cafarella; Stephen Soderland; Matt Broadhead; Oren Etzioni; |
| 2007 | 2 | Computing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a high-dimensional space of concepts derived from Wikipedia. |
Evgeniy Gabrilovich; Shaul Markovitch; |
| 2007 | 3 | Bayesian Inverse Reinforcement Learning IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present efficient algorithms that find solutions for the reward learning and apprenticeship learning tasks that generalize well over these distributions. |
Deepak Ramachandran; Eyal Amir; |
| 2007 | 4 | WiFi-SLAM Using Gaussian Process Latent Variable Models IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GP-LVM) to determine the latent-space locations of unlabeled signal strength data. |
Brian Ferris; Dieter Fox; Neil Lawrence; |
| 2007 | 5 | Belief Change Based On Global Minimisation IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A general framework for minimisation-based belief change is presented. |
James P. Delgrande; Jérôme Lang; Torsten Schaub; |
| 2007 | 6 | Exploiting Known Taxonomies In Learning Overlapping Concepts IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present two concrete multi-label classification methods, a generalized version of Perceptron and a hierarchical multi-label SVM learning. |
Lijuan Cai; Thomas Hofmann; |
| 2007 | 7 | Conflict-Driven Answer Set Solving IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a new approach to computing answer sets of logic programs, based on concepts from constraint processing (CSP) and satisfiability checking (SAT). |
Martin Gebser; Benjamin Kaufmann; André Neumann; Torsten Schaub; |
| 2007 | 8 | Document Summarization Using Conditional Random Fields IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a Conditional Random Fields (CRF) based framework to keep the merits of the above two kinds of approaches while avoiding their disadvantages. |
Dou Shen; Jian-Tao Sun; Hua Li; Qiang Yang; Zheng Chen; |
| 2007 | 9 | ItemRank: A Random-Walk Based Scoring Algorithm For Recommender Engines IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present ItemRank, a random-walk based scoring algorithm, which can be used to rank products according to expected user preferences, in order to recommend top-rank items to potentially interested users. |
Augusto Pucci; Marco Gori; |
| 2007 | 10 | Automatic Gait Optimization With Gaussian Process Regression IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a Bayesian approach based on Gaussian process regression that addresses all three drawbacks. |
Daniel Lizotte; Tao Wang; Michael Bowling; Dale Schuurmans; |
| 2007 | 11 | Emergence Of Norms Through Social Learning IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a model that supports the emergence of social norms via learning from interaction experiences. |
Sandip Sen; Stéphane Airiau; |
| 2007 | 12 | Conjunctive Query Answering For The Description Logic SHIQ IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we consider conjunctive queries over knowledge bases formulated in the popular DL SHIQ and allow transitive roles in both the query and the knowledge base. |
Birte Glimm; Ian Horrocks; Carsten Lutz; Ulrike Sattler; |
| 2007 | 13 | ProbLog: A Probabilistic Prolog And Its Application In Link Discovery IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The key contribution of this paper is the introduction of an effective solver for computing success probabilities. |
Luc De Raedt; Angelika Kimmig; Hannu Toivonen; |
| 2007 | 14 | Building Portable Options: Skill Transfer In Reinforcement Learning IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce the notion of learning options in agent-space, the space generated by a feature set that is present and retains the same semantics across successive problem instances, rather than in problem-space. |
George Konidaris; Andrew G. Barto; |
| 2007 | 15 | Searching For Interacting Features IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recognizing the presence of feature interaction, we propose to efficiently handle feature interaction to achieve efficient feature selection and present extensive experimental results of evaluation. |
Zheng Zhao; Huan Liu; |
| 2005 | 1 | Pushing The EL Envelope IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Franz Baader; Sebastian Brandt; and Carsten Lutz; |
| 2005 | 2 | Topic And Role Discovery In Social Networks IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Andrew McCallum; Andrés Corrada-Emmanuel; and Xuerui Wang; |
| 2005 | 3 | A Scalable Method For Multiagent Constraint Optimization IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Adrian Petcu and Boi Faltings; |
| 2005 | 4 | A Hybrid Discriminative/Generative Approach For Modeling Human Activities IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Jonathan Lester; Tanzeem Choudhury; Nicky Kern; Gaetano Borriello; and Blake Hannaford; |
| 2005 | 5 | BLOG: Probabilistic Models With Unknown Objects IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View |
BRIAN MILCH et. al. |
| 2005 | 6 | A Tableaux Decision Procedure For SHOIQ IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Ian Horrocks and Ulrike Sattler; |
| 2005 | 7 | Location-Based Activity Recognition Using Relational Markov Networks IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Lin Liao; Dieter Fox; and Henry Kautz; |
| 2005 | 8 | Lifted First-Order Probabilistic Inference IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Rodrigo de Salvo Braz; Eyal Amir; and Dan Roth; |
| 2005 | 9 | Identifiability Of Path-Specific Effects IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Chen Avin; Ilya Shpitser; and Judea Pearl; |
| 2005 | 10 | Feature Generation For Text Categorization Using World Knowledge IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Evgeniy Gabrilovich and Shaul Markovitch; |
| 2005 | 11 | Semi-Supervised Regression With Co-Training IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Zhi-Hua Zhou and Ming Li; |
| 2005 | 12 | Beyond TFIDF Weighting For Text Categorization In The Vector Space Model IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Pascal Soucy and Guy W. Mineau; |
| 2005 | 13 | A Uniform Integration Of Higher-Order Reasoning And External Evaluations In Answer-Set Programming IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Thomas Eiter; Giovambattista Ianni; Roman Schindlauer; and Hans Tompits; |
| 2005 | 14 | Data Complexity Of Reasoning In Very Expressive Description Logics IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Ullrich Hustadt; Boris Motik; and Ulrike Sattler; |
| 2005 | 15 | Networked Distributed POMDPs: A Synergy Of Distributed Constraint Optimization And POMDPs IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Ranjit Nair; Pradeep Varakantham; Milind Tambe; and Makoto Yokoo; |
| 2003 | 1 | FastSLAM 2.0: An Improved Particle Filtering Algorithm For Simultaneous Localization And Mapping That Provably Converges IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Mike Montemerlo; Sebastian Thrun; Daphne Koller; and Ben Wegbreit; |
| 2003 | 2 | Point-based Value Iteration: An Anytime Algorithm For POMDPs IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Joelle Pineau; Geoff Gordon; and Sebastian Thrun; |
| 2003 | 3 | Extended Gloss Overlaps As A Measure Of Semantic Relatedness IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Satanjeev Banerjee and Ted Pedersen; |
| 2003 | 4 | Non-Standard Reasoning Services For The Debugging Of Description Logic Terminologies IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Stefan Schlobach and Ronald Cornet.; |
| 2003 | 5 | First-order Probabilistic Inference IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View |
David Poole.; |
| 2003 | 6 | Learning To Classify Texts Using Positive And Unlabeled Data IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Xiaoli Li and Bing Liu; |
| 2003 | 7 | Taming Decentralized POMDPs: Towards Efficient Policy Computation For Multiagent Settings IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View |
R. Nair; M. Tambe; M. Yokoo; D. Pynadath; and S. Marsella; |
| 2003 | 8 | AUC: A Statistically Consistent And More Discriminating Measure Than Accuracy IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Charles X. Ling; Jin Huang; and Harry Zhang.; |
| 2003 | 9 | Efficient Representation Of Adhoc Constraints IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View |
KeniI C. K Cheng; jimmy H. M. Lee; and Peter J. Stuckey.; |
| 2003 | 10 | DP-SLAM: Fast, Robust Simultaneous Localization And Mapping Without Predetermined Landmarks IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Austin Eliazar and Ronald Parr.; |
| 2003 | 11 | Backdoors To Typical Case Complexity IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Ryan Williams; Carla P. Gomes; and Bart Selman.; |
| 2003 | 12 | Responsibility And Blame: A Structural-Model Approach IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Hana Chockler and Joseph Y Halpern; |
| 2003 | 13 | Constructing Diverse Classifier Ensembles Using Artificial Training Examples IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Prem Melville and Raymond J. Mooney; |
| 2003 | 14 | Model-based Diagnosis Of Hybrid Systems IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Sriram Narasimhan and Gautam Biswas.; |
| 2003 | 15 | Approximating Game-Theoretic Optimal Strategies For Full-scale Poker IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View |
D. BILLINGS et. al. |