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学术报告-Revisiting Graph Convolutional Networks: From Paradigm to Application（图）
学术报告 图神经网络 王石平
2024/4/15
图神经网络（Graph Neural Network，GNN）是指使用神经网络来学习图结构数据，提取和发掘图结构数据中的特征和模式，满足聚类、分类、预测、分割、生成等图学习任务需求的算法总称。如何从图数据（网络数据）中学习有效的数据表征是大数据与人工智能时代的一大挑战。为此，本次汇报将从一种特殊的谱图神经网络（图卷积神经网络）出发，介绍相应的消息传递范式，揭示传统机器学习和图神经网络的内在关联，并...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Graph Refinement via Simultaneously Low-rank and Sparse Approximation
低秩 稀疏近似 图细化
2023/4/18
Graphs play an important role in many fields of machine learning such as clustering. Many graph-based machine learning approaches assume that the graphs have hidden group structures. However, the grou...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Generalization of Graph Neural Networks and Graph Structural Learning for Robust Representation
图神经网络 泛化 图结构学习 鲁棒表示
2023/4/18
This report consists of two parts associated with graph neural networks: generalization and graph structural learning. We first study the Rademacher complexity of GNNs, as one of independent-algorithm...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：A diffusion scheme based on the foliation graph stripe for optimizing discrete harmonic foliation energy
叶子图条带 优化 离散谐波 叶能量 扩散方案
2023/4/28
Consumer expenditures in the United Kingdom account for over 50% of Gross Domestic Product on the expenditure side, yet their impact on economic activity is often Global geometric structures live on m...
