Dynamical Systems for Machine Learning - Second Symposium on Machine Learning and Dynamical Systems Published 2020-11-06 Download video MP4 360p Recommendations 29:33 Neural Networks for Solving PDEs 1:19:28 The Story of Complexity - Christos Papadimitriou 52:22 Meta Learning and Self Play - Ilya Sutskever, OpenAI 1:04:53 How I became seduced by univalent foundations 51:30 The R-INLA project: Overview and recent developments 26:06 Reservoir Computing with Superconducting Circuits 1:36:55 Public Opening of the 2021 Fields Medal Symposium 1:02:46 Koopman Operator Theory Based Machine Learning of Dynamical Systems 53:24 Gaussian Processes for Time Series Forecasting 1:17:51 Chris Maddison | The future of representation learning 1:20:17 Manjul Bhargava, Fields Medal Symposium 2016: Patterns in Numbers and Nature 1:07:43 Neural SDEs, Deep Learning and Stochastic Control 17:58 Fields Medal Symposium 2021: Jared Weinstein introduces Perfectoid Spaces 01:55 Mathematician John Milnor's Advice on Exchanging Ideas and Interdisciplinary Research 1:06:14 An Introduction to the Langlands Program 1:25:38 Taming Infinities - Martin Hairer (2017 Fields Medal Symposium) Similar videos 04:12 Introductory Remarks to the Second Symposium on Machine Learning and Dynamical Systems 30:19 Machine Learning and Dynamical Systems Meet in Reproducing Kernel Hilbert Spaces 5:06:54 Data-driven modelling - Second Symposium on Machine Learning and Dynamical Systems 8:00:59 Reservoir Computing & Dynamical Systems - Second Symposium on Machine Learning and Dynamical Systems 1:14:39 Weinan E: Machine Learning and Dynamical Systems 40:01 Machine Learning via Dynamical Systems 36:07 Learning Dynamical Systems 06:23 Special Interest Group on "Machine Learning and Dynamical Systems" at the Alan Turing Institute 5:15:28 Geometric and Topological Data Analysis - Second Symposium on Machine Learning and Dynamical Systems 28:49 Some Time, Some Space, and Some Equations: Machine Learning of Model Error in Dynamical Systems 5:46:27 Learning Theory & Signature-based methods - Second Symposium on Machine Learning & Dynamical Systems 32:15 [MS130] Qianxiao Li: A Dynamical Systems Approach to Deep Learning (SIAM MDS 20) 33:12 Dynamical aspects of learning linear neural networks More results