Machine Learning for Physics and the Physics of Learning 2019
Workshop I: From Passive to Active: Generative and Reinforcement Learning with Physics

"A Numerical Analysis Perspective on Deep Neural Networks"
Lars Ruthotto - Emory University

Abstract: In this talk, I illustrate the use of numerical analysis tools for improving the effectiveness of deep learning algorithms. With a focus on deep neural networks that can be modeled as differential equations, I highlight the importance of choosing an adequate time integrator. I also compare, using a numerical example, the difference of the first-discretize-then-differentiate and the first-differentiate-then-discretize paradigms for training residual neural networks. Finally, I show that even simple (i.e., not deep) architectures can give rise to ill-conditioned learning problems.

Institute for Pure and Applied Mathematics, UCLA
September 25, 2019

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