Introduction to Reliable And Interpretable Artificial Intelligence Lecture 12 Randomized Smoothing
Let's dive into the details surrounding Reliable And Interpretable Artificial Intelligence Lecture 12 Randomized Smoothing. Randomized Smoothing
Reliable And Interpretable Artificial Intelligence Lecture 12 Randomized Smoothing Comprehensive Overview
Visualization of the decision process in neural networks, connection to adversarial robustness. Verification of neural networks, Box convex approximation, complete vs incomplete methods, sound vs unsound methods, ... Workshop on Software Correctness and
Jerry Li (Microsoft Research) https://simons.berkeley.edu/talks/tbd-62 Frontiers of Deep Learning.
Summary & Highlights for Reliable And Interpretable Artificial Intelligence Lecture 12 Randomized Smoothing
- Introductory
- Adversarial Defenses, PGD defense, min-max optimization, adversarial accuracy vs. natural accuracy.
- Certification of Geometric Robustness, Robustness of Deep Neural Networks to Geometric Transformations (Rotations, ...
- Adversarial Examples, Adversarial Attacks, FGSM, Targeted and Untargeted attacks, Carlini-Wagner attacks, Lp Norms.
- Querying Deep Neural Networks, Enforcing Background Priors in Neural Networks, Differentiable Logic, Generalized Adversarial ...
That wraps up our extensive overview of Reliable And Interpretable Artificial Intelligence Lecture 12 Randomized Smoothing.
