Machine Learning for Physics and the Physics of Learning Tutorials 2019-UCLA

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2019-10-28 21:20:35
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hhttp://www.ipam.ucla.edu/programs/workshops/machine-learning-for-physics-and-the-physics-of-learning-tutorials/ 共36P,有CG字幕。 这个机构相关主题演讲还有许多,好几个系列,讲机器学习与物理学的协作,待日后更新。看我个人观看进度吧,这个账号上传视频都是自用的,当网盘用
Wir müssen wissen. Wir werden wissen.
视频选集
(10/13)
1.Frank Noé - 'Intro to Machine Learning Ⅰ
01:24:41
2.Frank Noé - 'Intro to Machine Learning Ⅱ
01:26:08
3.Steve Brunton - 'Dynamical Systems Ⅰ
01:17:38
4.Steve Brunton - 'Dynamical Systems Ⅱ
01:16:34
10.Lars Ruthotto - 'Deep Neural Networks Motivated By Differential Equations Ⅰ
01:06:45
15.Alexandre Tkatchenko - 'Towards a Unified Machine Learning Model of Molecular
01:06:28
16.Xavier Bresson - Graph Convolutional Neural Networks for Molecule Generation
01:00:43
17.Joshua Bloom - 'Physics-Informed (and -informative) Generative Modelling in
59:38
18.Laurent Dinh - 'A primer on normalizing flows'
26:19
19.Frank Noé - 'Deep Generative Learning for Physics Many-Body Systems'
01:01:56
20.Philip Kurian - 'New horizons in quantum biology - Learning complexi
39:37
21.Nicola De Cao - 'Deep Generative Models for Molecular Graphs'
01:01:39
22.Patrick Riley - 'Reinforcement learning for molecular generation'
42:20
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