Results 261 to 270 of about 21,280,601 (295)

Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting

IEEE International Conference on Data Engineering, 2023
This paper studies the problem of traffic flow forecasting, which aims to predict future traffic conditions on the basis of road networks and traffic conditions in the past. The problem is typically solved by modeling complex spatio-temporal correlations
Yusheng Zhao, Xiao Luo, Wei Ju, C. Chen, Xiansheng Hua, Ming Zhang   +5 more
semanticscholar   +1 more source

Structure Learning Via Meta-Hyperedge for Dynamic Rumor Detection

IEEE Transactions on Knowledge and Data Engineering, 2023
Online social networks have greatly facilitated our lives but have also propagated the spreading of rumours. Traditional works mostly find rumors from content, but content can be strategically manipulated to evade such detection, making these methods ...
Xiangguo Sun, Hongzhi Yin, Bo Liu, Qing Meng, Jiuxin Cao, Alexander Zhou, Hongxu Chen   +6 more
semanticscholar   +1 more source

Personalized Latent Structure Learning for Recommendation

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
In recommender systems, users’ behavior data are driven by the interactions of user-item latent factors. To improve recommendation effectiveness and robustness, recent advances focus on latent factor disentanglement via variational inference.
Shengyu Zhang, Fuli Feng, Kun Kuang, Wenqiao Zhang, Zhou Zhao, Hongxia Yang, Tat-seng Chua, Fei Wu   +7 more
semanticscholar   +1 more source

Mesochronal Structure Learning

Uncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence, 2015
Standard time series structure learning algorithms assume that the measurement timescale is approximately the same as the timescale of the underlying (causal) system. In many scientific contexts, however, this assumption is violated: the measurement timescale can be substantially slower than the system timescale (so intermediate time series datapoints ...
Sergey, Plis, David, Danks, Jianyu, Yang
openaire   +2 more sources

Supervised structure learning

Biological Psychology, 2023
This paper concerns structure learning or discovery of discrete generative models. It focuses on Bayesian model selection and the assimilation of training data or content, with a special emphasis on the order in which data are ingested. A key move-in the
Karl J. Friston, Lancelot Da Costa, Alexander Tschantz, Alex B. Kiefer, Tommaso Salvatori, Victorita Neacsu, Magnus T. Koudahl, Conor Heins, Noor Sajid, Dimitrije Markovic, Thomas Parr, Tim Verbelen, Christopher L. Buckley   +12 more
semanticscholar   +1 more source

Hierarchical Multi-View Graph Pooling With Structure Learning

IEEE Transactions on Knowledge and Data Engineering, 2021
Graph Neural Networks (GNNs), whch generalize deep neural networks to graph-structured data, have drawn considerable attention and achieved state-of-the-art performance in numerous graph related tasks.
Zhen Zhang, Jiajun Bu, Martin Ester, Jianfeng Zhang, Zhao Li, Chengwei Yao, Dai Huifen, Zhi Yu, Can Wang   +8 more
semanticscholar   +1 more source

mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding

Conference on Empirical Methods in Natural Language Processing
Structure information is critical for understanding the semantics of text-rich images, such as documents, tables, and charts. Existing Multimodal Large Language Models (MLLMs) for Visual Document Understanding are equipped with text recognition ability ...
Anwen Hu, Haiyang Xu, Jiabo Ye, Mingshi Yan, Liang Zhang, Bo Zhang, Chen Li, Ji Zhang, Qin Jin, Fei Huang, Jingren Zhou   +10 more
semanticscholar   +1 more source

A Comprehensive Survey on Spectral Clustering with Graph Structure Learning

arXiv.org
Spectral clustering is a powerful technique for clustering high-dimensional data, utilizing graph-based representations to detect complex, non-linear structures and non-convex clusters.
Kamal Berahmand, Farid Saberi-Movahed, Razieh Sheikhpour, Yuefeng Li, Mahdi Jalili   +4 more
semanticscholar   +1 more source

GraphEdit: Large Language Models for Graph Structure Learning

arXiv.org
Graph Structure Learning (GSL) focuses on capturing intrinsic dependencies and interactions among nodes in graph-structured data by generating novel graph structures.
Zirui Guo, Lianghao Xia, Yanhua Yu, Yuling Wang, Zixuan Yang, Wei Wei, Liang Pang, Tat-Seng Chua, Chao Huang   +8 more
semanticscholar   +1 more source

Structure and Learning

2022
This report documents the program and the outcomes of Dagstuhl Seminar 21362 "Structure and Learning", held from September 5 to 10, 2021. Structure and learning are among the most prominent topics in Artificial Intelligence (AI) today. Integrating symbolic and numeric inference was set as one of the next open AI problems at the Townhall meeting "A 20 ...
Dong, Tiansi, Rettinger, Achim, Tang, Jie, Tversky, Barbara, Harmelen, Frank van   +4 more
openaire   +1 more source