Residual Networks (ResNet) [Physics Informed Machine Learning]

This video discusses Residual Networks, one of the most popular machine learning architectures that has enabled considerably deeper neural networks through jump/skip connections. This architecture mimics many of the aspects of a numerical integrator. This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company %%% CHAPTERS %%% 00:00 Intro 01:09 Concept: Modeling the Residual 03:26 Building Blocks 05:59 Motivation: Deep Network Signal Loss 07:43 Extending to Classification 09:00 Extending to DiffEqs 10:16 Impact of CVPR and Resnet 12:17 Resnets and Euler Integrators 13:34 Neural ODEs and Improved Integrators 16:07 Outro

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