Results 51 to 60 of about 1,049,471 (309)

Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems [PDF]

The Web Conference, 2019
Social recommendation leverages social information to solve data sparsity and cold-start problems in traditional collaborative filtering methods. However, most existing models assume that social effects from friend users are static and under the forms of
Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen   +6 more
semanticscholar   +1 more source

PointGAT: Graph attention networks for 3D object detection

Intelligent and Converged Networks, 2022
3D object detection is a critical technology in many applications, and among the various detection methods, pointcloud-based methods have been the most popular research topic in recent years. Since Graph Neural Network (GNN) is considered to be effective
Haoran Zhou, Wei Wang, Gang Liu, Qingguo Zhou   +3 more
doaj   +1 more source

Relation-aware Graph Attention Networks with Relational Position Encodings for Emotion Recognition in Conversations

Conference on Empirical Methods in Natural Language Processing, 2020
Interest in emotion recognition in conversations (ERC) has been increasing in various fields, because it can be used to analyze user behaviors and detect fake news. Many recent ERC methods use graph-based neural networks to take the relationships between
Taichi Ishiwatari, Y. Yasuda, Taro Miyazaki, Jun Goto   +3 more
semanticscholar   +1 more source

GAEN: Graph Attention Evolving Networks [PDF]

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021
Real-world networked systems often show dynamic properties with continuously evolving network nodes and topology over time. When learning from dynamic networks, it is beneficial to correlate all temporal networks to fully capture the similarity/relevance between nodes. Recent work for dynamic network representation learning typically trains each single
Min Shi, Yu Huang, Xingquan Zhu, Yufei Tang, Yuan Zhuang, Jianxun Liu   +5 more
openaire   +1 more source

Multivariate Time-Series Anomaly Detection based on Enhancing Graph Attention Networks with Topological Analysis [PDF]

International Conference on Information and Knowledge Management
Unsupervised anomaly detection in time series is essential in industrial applications, as it significantly reduces the need for manual intervention.
Zhe Liu, Xiang Huang, Jingyun Zhang, Zhifeng Hao, L. Sun, Hao Peng   +5 more
semanticscholar   +1 more source

Predicting Propositional Satisfiability Based on Graph Attention Networks

International Journal of Computational Intelligence Systems, 2022
Boolean satisfiability problems (SAT) have very rich generic and domain-specific structures. How to capture these structural features in the embedding space and feed them to deep learning models is an important factor influencing the use of neural ...
Wenjing Chang, Hengkai Zhang, Junwei Luo
doaj   +1 more source

Electroencephalography (EEG)-Derived Markers to Measure Components of Attention Processing [PDF]

, 2017
Although extensively studied for decades, attention system remains an interesting challenge in neuroscience field. The Attention Network Task (ANT) has been developed to provide a measure of the efficiency for the three attention components ...
Anzolin, Alessandra, Astolfi, Laura, Cincotti, Febo, Mattia, Donatella, Pichiorri, Floriana, Riccio, Angela, Toppi, Jlenia   +6 more
core   +1 more source

Session-Based Social Recommendation via Dynamic Graph Attention Networks [PDF]

Web Search and Data Mining, 2019
Online communities such as Facebook and Twitter are enormously popular and have become an essential part of the daily life of many of their users. Through these platforms, users can discover and create information that others will then consume.
Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, Jian Tang   +5 more
semanticscholar   +1 more source

Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks [PDF]

Conference of the European Chapter of the Association for Computational Linguistics, 2021
This paper addresses the task of (complex) conversational question answering over a knowledge graph. For this task, we propose LASAGNE (muLti-task semAntic parSing with trAnsformer and Graph atteNtion nEworks).
Endri Kacupaj, Joan Plepi, Kuldeep Singh, Harsh Thakkar, Jens Lehmann, M. Maleshkova   +5 more
semanticscholar   +1 more source

High-order graph attention network [PDF]

Information Sciences, 2023
GCN is a widely-used representation learning method for capturing hidden features in graph data. However, traditional GCNs suffer from the oversmoothing problem, hindering their ability to extract high-order information and obtain robust data representation.
Liancheng He, Liang Bai, Xian Yang, Hangyuan Du, Jiye Liang   +4 more
openaire   +1 more source