In this workshop we will explore how to use physical intuition and ideas to design new classes of machine learning (ML) algorithms. Physics-inspired sampling algorithms could be used to train ML structures or sample the hyper-parameter space (e.g. Additionally, physics-based models such as Ising/Potts models or energy-based models have influenced ML inference frameworks such as Markov Random Fields and Restricted Boltzmann Machines, and we want to continue the discussion to facilitate this innovation transfer. Finally, physical insight could be used to enhance learning in the situation of scarce data by enforcing smoothness, differentiability or other physical properties relevant to a given problem. We will also explore the use of Koopmans' theorem to design learning algorithms for dynamical systems.

Here are the videos and slides of Workshop IV: Deep Geometric Learning of Big Data and Applications, Part of the Long Program Geometry and Learning from Data in 3D and Beyond at IPAM. The workshop took place May 20 - 24, 2019. And thank you to the organizing committee (Xavier Bresson, Yann LeCun, Stanley Osher, Rene Vidal, Rebecca Willett) for making this workshop happen!