Results 51 to 60 of about 6,652,811 (287)
Learning probabilistic decision graphs
International Journal of Approximate Reasoning, 2006 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jaeger, Manfred; id_orcid 0000-0002-5641-8153 +2 more
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SCDVit: Semantic Change Detection Based on Sam-Vit and Semantic Consistency
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingIn recent years, change detection has been a hot research topic in remote sensing. Previous research has focused on binary change detection (BCD), limiting its practical applications.
Ming Chen, Wanshou Jiang
doaj +1 more source
Interpreting Deep Graph Convolutional Networks with Spectrum Perspective
Mathematics, 2023 Graph convolutional network (GCN) architecture is the basis of many neural networks and has been widely used in processing graph-structured data. When dealing with large and sparse data, deeper GCN models are often required.
Sisi Zhang +3 more
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Quantitative Stock Selection Model Using Graph Learning and a Spatial–Temporal Encoder
Journal of Theoretical and Applied Electronic Commerce ResearchIn the rapidly evolving domain of finance, quantitative stock selection strategies have gained prominence, driven by the pursuit of maximizing returns while mitigating risks through sophisticated data analysis and algorithmic models.
Tianyi Cao +4 more
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Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems
IEEE Access, 2023 Fault diagnostics are extremely important to decide proper actions toward fault isolation and system restoration. The growing integration of inverter-based distributed energy resources imposes strong influences on fault detection using traditional ...
Bang L. H. Nguyen +4 more
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Federated Graph Learning under Domain Shift with Generalizable Prototypes
AAAI Conference on Artificial IntelligenceFederated Graph Learning is a privacy-preserving collaborative approach for training a shared model on graph-structured data in the distributed environment.
Guancheng Wan, Wenke Huang, Mang Ye
semanticscholar +1 more source
Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences [PDF]
The Web Conference, 2019 Incorporating knowledge graph (KG) into recommender system is promising in improving the recommendation accuracy and explainability. However, existing methods largely assume that a KG is complete and simply transfer the ”knowledge” in KG at the shallow ...
Yixin Cao +4 more
semanticscholar +1 more source
Learning Graph Representations [PDF]
, 2021 Social and information networks are gaining huge popularity recently due to their various applications. Knowledge representation through graphs in the form of nodes and edges should preserve as many characteristics of the original data as possible.
Rucha Bhalchandra Joshi +1 more
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Dual targeting of RET and SRC synergizes in RET fusion‐positive cancer cells
Molecular Oncology, EarlyView.Despite the strong activity of selective RET tyrosine kinase inhibitors (TKIs), resistance of RET fusion‐positive (RET+) lung cancer and thyroid cancer frequently occurs and is mainly driven by RET‐independent bypass mechanisms. Son et al. show that SRC TKIs significantly inhibit PAK and AKT survival signaling and enhance the efficacy of RET TKIs in ...
Juhyeon Son +13 more
wiley +1 more source
Multi-Target Feature Selection with Adaptive Graph Learning and Target Correlations
MathematicsIn this paper, we present a novel multi-target feature selection algorithm that incorporates adaptive graph learning and target correlations. Specifically, our proposed approach introduces the low-rank constraint on the regression matrix, allowing us to ...
Yujing Zhou, Dubo He
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