Results 11 to 20 of about 6,652,811 (287)
Learning graph normalization for graph neural networks [PDF]
Neurocomputing, 2022 15 pages, 3 figures, 6 ...
Chen, Yihao +4 more
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009 As a fundamental problem in pattern recognition, graph matching has applications in a variety of fields, from computer vision to computational biology. In graph matching, patterns are modeled as graphs and pattern recognition amounts to finding a correspondence between the nodes of different graphs.
Caetano, Tiberio +4 more
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Text-Graph Enhanced Knowledge Graph Representation Learning [PDF]
Frontiers in Artificial Intelligence, 2021 Knowledge Graphs (KGs) such as Freebase and YAGO have been widely adopted in a variety of NLP tasks. Representation learning of Knowledge Graphs (KGs) aims to map entities and relationships into a continuous low-dimensional vector space. Conventional KG embedding methods (such as TransE and ConvE) utilize only KG triplets and thus suffer from structure
Linmei Hu +6 more
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IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022 Electroencephalogram(EEG) signals are generated from central nervous system which are difficult to disguise, leading to its popularity in emotion recognition. Recently,semi-supervisedlearning exhibits promisingemotion recognition performance by involving
Yong Peng +5 more
doaj +1 more source
Graph Learning: A Survey [PDF]
IEEE Transactions on Artificial Intelligence, 2021 Graphs are widely used as a popular representation of the network structure of connected data. Graph data can be found in a broad spectrum of application domains such as social systems, ecosystems, biological networks, knowledge graphs, and information ...
Feng Xia +6 more
semanticscholar +1 more source
SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning [PDF]
arXiv.org, 2023 Textual graphs (TGs) are graphs whose nodes correspond to text (sentences or documents), which are widely prevalent. The representation learning of TGs involves two stages: (i) unsupervised feature extraction and (ii) supervised graph representation ...
Keyu Duan +6 more
semanticscholar +1 more source
Dynamic Graph CNN for Learning on Point Clouds [PDF]
ACM Transactions on Graphics, 2018 Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While hand-designed features on point clouds have long been proposed
Yue Wang +5 more
semanticscholar +1 more source
Self-Supervised Temporal Graph Learning With Temporal and Structural Intensity Alignment [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2023 Temporal graph learning aims to generate high-quality representations for graph-based tasks with dynamic information, which has recently garnered increasing attention.
Meng Liu +7 more
semanticscholar +1 more source
Multimodal learning with graphs
Nature Machine Intelligence, 2023 Artificial intelligence for graphs has achieved remarkable success in modeling complex systems, ranging from dynamic networks in biology to interacting particle systems in physics. However, the increasingly heterogeneous graph datasets call for multimodal methods that can combine different inductive biases: the set of assumptions that algorithms use to
Yasha Ektefaie +4 more
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Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction [PDF]
Briefings Bioinform., 2023 Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs).
Xuan Lin +11 more
semanticscholar +1 more source