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Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2024; 33rd International C Michael Wand,Kristína Malinovská,Igor V. Tetko Conferenc

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樓主: invigorating
11#
發(fā)表于 2025-3-23 13:39:39 | 只看該作者
Key Substructure-Driven Backdoor Attacks on?Graph Neural Networkscker-chosen target class key substructures, modifying few critical edges and nodes. Our approach across real datasets spanning diverse domains highlights its efficiency. The proposed methodology establishes a pioneering direction for refining backdoor attack techniques on GNNs.
12#
發(fā)表于 2025-3-23 15:22:10 | 只看該作者
Missing Data Imputation via?Neighbor Data Feature-Enriched Neural Ordinary Differential Equationsetwork is then employed to learn adjacent information of neighboring variables. The temporal information is captured by applying a gate recurrent unit module, thereby obtaining a spatiotemporal prior. The decoder introduces an ordinary differential equation module to generate a series of continuous
13#
發(fā)表于 2025-3-23 19:05:28 | 只看該作者
14#
發(fā)表于 2025-3-24 00:37:08 | 只看該作者
STGNA: Spatial-Temporal Graph Convolutional Networks with Node Level Attention for Shortwave Communiatial-temporal patterns of shortwave communications parameters, yielding to enhanced forecasting accuracy. Comprehensive experiments on a targeted dataset demonstrate that our approach significantly outperforms other baselines in forecasting accuracy.
15#
發(fā)表于 2025-3-24 05:37:45 | 只看該作者
Virtual Nodes based Heterogeneous Graph Convolutional Neural Network for Efficient Long-Range Informormation aggregation with only 4 layers. Additionally, we demonstrate that VN-HGCN can serve as a versatile framework that can be seamlessly applied to other HGNN models, showcasing its generalizability. Empirical evaluations validate the effectiveness of VN-HGCN, and extensive experiments conducted
16#
發(fā)表于 2025-3-24 08:20:32 | 只看該作者
17#
發(fā)表于 2025-3-24 12:56:07 | 只看該作者
An Enhanced Prompt-Based LLM Reasoning Scheme via?Knowledge Graph-Integrated Collaboration of the reasoning results. Experimental results show that our scheme significantly progressed across multiple datasets, notably achieving an improvement of over 10% on the QALD10 dataset compared to both the best baseline and the fine-tuned state-of-the-art (SOTA) models.
18#
發(fā)表于 2025-3-24 17:42:30 | 只看該作者
19#
發(fā)表于 2025-3-24 22:39:48 | 只看該作者
20#
發(fā)表于 2025-3-25 02:50:50 | 只看該作者
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