相关实验视频

Updated: Jul 17, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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放下边缘并适应:公平性强制执行对图形神经网络的微调

Indro Spinelli1, Riccardo Bianchini2, Simone Scardapane3

  • 1Department of Computer Science (DI), Sapienza University of Rome, Via Salaria 113, 00198 Rome, Italy.

Neural networks : the official journal of the International Neural Network Society
|September 1, 2023
PubMed
概括

这项研究引入了一种新方法,即Drop Unfair Edges and Adapt (DEA),以使图形神经网络在链接预测中更加公平. 通过调整网络连接和模型参数,DEA提高了准确性和公平性.

关键词:
公平的 公平的 公平的图表神经网络的神经网络链接预测链接预测

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