Analyzing Optimization and Generalization in Deep Learning via Dynamics of Gradient Descent Published 2020-12-16 Download video MP4 360p Recommendations 56:25 The Unreasonable Effectiveness of Spectral Graph Theory: A Confluence of Algorithms, Geometry & ... 18:49 Optimization in Deep Learning | All Major Optimizers Explained in Detail 1:17:55 Optimization I 33:08 Training and generalization dynamics in simple deep networks 1:32:10 A Computational Framework for Solving Wasserstein Lagrangian Flows | Kirill Neklyudov 1:20:35 The Arrow of Time in Causal Networks 10:21 Optimization in Deep Learning 1:00:56 Are LLMs the Beginning or End of NLP? 28:00 The Most Underrated Concept in Number Theory 1:05:29 Variational Inference: Foundations and Innovations 48:12 Andrew Lo on the Future of Finance 51:32 Predictive Coding Models of Perception 1:27:56 Artificial Stupidity: The New AI and the Future of Fintech 1:03:45 Nonparametric Bayesian Methods: Models, Algorithms, and Applications II Similar videos 1:05:07 Nadav Cohen - Analyzing Optimization and Generalization in DL via Dynamics of Gradient Descent 46:27 Analyzing Optimization and Generalization in Deep Learning via Trajectories of Gradient Descent 1:28:48 Insights on gradient-based algorithms in high-dimensional learning 58:10 Dynamics and Generalization in deep neural networks 26:02 Karolina Dziugaite on Nonvacuous Generalization Bounds for Deep Neural Networks via PAC-Bayes 51:56 Tomaso Poggio - Dynamics and Generalization in Deep Neural Networks 22:35 'How neural networks learn' - Part III: Generalization and Overfitting 1:00:27 Two-Layer Neural Networks for PDEs: Optimization and Generalization Theory, HaizhaoYang@Purdue 53:03 25. Stochastic Gradient Descent 54:10 Guido Montúfar: Implicit bias of gradient descent for MSE regression with wide neural networks More results