20210703_张林峰_DeePKS: a machine learning assisted electronic structure model
20210417_许志钦_理解神经网络的训练过程
20210619_Zhong Li,Qianxiao Li_Modelling Temporal Data: from RNNs to CNNs
20210731_董彬_Learning and Learning to Solve PDEs
20210509_王涵_强化动力学简介
20210828_纪辉_Self-supervised Deep Learning for Solving Inverse Problems
20211030_张仕俊_Deep Network Approximation Achieving Arbitrary Error with Fixed Siz
20210731_李宗宜-Neural Operator: Learning Maps Between Function Spaces
20210403_吴磊_神经网络和高维函数逼近
20210828_应乐兴_Solving Inverse Problems with Deep Learning
20211016_孙若愚_Global Loss Landscape of Neural Networks: What do We Know and What
20210717_许志钦_Frequency principle
20210703_王涵_基于深度学习的分子动力学模拟
20210522_文继荣_像孩子一样学习超大规模多模态预训练模型
20211030_杨海钊_Deep Network Approximation Error Characterization in term of Width
20211016_张耀宇_Embedding Principle of Loss Landscape of Deep Neural Networks
20210605_Stephan Wojtowytsch_Stochastic gradient descent for noise with ML-type
20210814_Jinchao Xu_Barron Spaces
深度学习基础和实践合集
20210605_Yuhai Tu_How Stochastic-Gradient-Descent finds flat minima
