Laurent Dinh: "A primer on normalizing flows"
Автор: Institute for Pure & Applied Mathematics (IPAM)
Загружено: 2019-10-09
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Machine Learning for Physics and the Physics of Learning 2019
Workshop I: From Passive to Active: Generative and Reinforcement Learning with Physics
"A primer on normalizing flows"
Laurent Dinh, Google
Abstract: Normalizing flows are a flexible family of probability distributions that can serve as generative models for a variety of data modalities. Because flows can be expressed as compositions of expressive functions, they have successfully harnessed recent advances in deep learning. An ongoing challenge in developing these methods is the definition of expressive yet tractable building blocks. In this talk, I will introduce the fundamentals and describe recent work (including my own) on this topic.
Institute for Pure and Applied Mathematics, UCLA
September 23, 2019
For more information: http://www.ipam.ucla.edu/mlpws1
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