1Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers

Channel:
United States Simons Institute for the Theory of Computing
Subscribers:
68,700
Published on ● Video Link: https://www.youtube.com/watch?v=ZFwmyP__p_4


Duration: 46:39
2,397 views
40

Jerry Li (Microsoft Research)
https://simons.berkeley.edu/talks/tbd-62
Frontiers of Deep Learning



Other Videos By Simons Institute for the Theory of Computing


2019-07-18Sample-complexity of Estimating Convolutional and Recurrent Neural Networks
2019-07-18Studying Generalization in Deep Learning via PAC-Bayes
2019-07-18Towards Understanding Transfer Learning with Applications to Medical Imaging
2019-07-18Kernel and Deep Regimes in Overparameterized Learning
2019-07-18Meta-learning of Optimizers and Update Rules
2019-07-18Robust Deep Learning Under Distribution Shift
2019-07-18Using Deep Learning for Perception in Autonomous Systems: A Perspective from Control Theory
2019-07-17Interpretability - now what?
2019-07-17Lessons Learned from Evaluating the Robustness of Defenses to Adversarial Examples
2019-07-17Interpreting Deep Neural Networks (DNNs)
2019-07-171Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers
2019-07-17A New Perspective on Adversarial Perturbations
2019-07-17Provable Robustness Beyond Bound Propagation
2019-07-16Splitting Gradient Descent for Incremental Learning of Neural Architectures
2019-07-16Mad Max: Affine Spline Insights into Deep Learning
2019-07-16Computation in Very Wide Neural Networks
2019-07-16Training on the Test Set and Other Heresies
2019-07-16Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets
2019-07-16A Primal-dual Analysis of Margin Maximization by Steepest Descent Methods
2019-07-16On the Foundations of Deep Learning: SGD, Overparametrization, and Generalization
2019-07-15Size-free Generalization Bounds for Convolutional Neural Networks


Tags:
Simons Institute
Theory of Computing
Theory of Computation
Theoretical Computer Science
Computer Science
UC Berkeley
Frontiers of Deep Learning
Jerry Li