Media Summary: Introductory lecture of an ETH Zurich graduate course on topics such as adversarial attacks, experimental and provable defenses, ... Verification of neural networks, Box convex approximation, complete vs incomplete methods, sound vs unsound methods, ... Adversarial Defenses, PGD defense, min-max optimization, adversarial accuracy vs. natural accuracy.
Reliable And Interpretable Artificial Intelligence - Detailed Analysis & Overview
Introductory lecture of an ETH Zurich graduate course on topics such as adversarial attacks, experimental and provable defenses, ... Verification of neural networks, Box convex approximation, complete vs incomplete methods, sound vs unsound methods, ... Adversarial Defenses, PGD defense, min-max optimization, adversarial accuracy vs. natural accuracy. The speaker will discuss the importance of human Certification of Neural Networks, Complete Certification with MILP (Mixed-Integer Linear Solvers), Combination of Complete and ... A surprising fact about modern large language models is that nobody really knows how they work internally. At Anthropic, the ...
Let us consider a difficult computer vision challenge. Would you want an algorithm to determine whether you should get a biopsy, ... Randomized Smoothing for Robustness Certification, Statistical Certification of Deep Neural Networks, Confidence Intervals. Visualization of the decision process in neural networks, connection to adversarial robustness. Certification of Neural Networks, Polyhedra Convex Relaxations, Abstract Interpretation. In the first segment of the workshop, Professor Hima Lakkaraju motivates the need for Querying Deep Neural Networks, Enforcing Background Priors in Neural Networks, Differentiable Logic, Generalized Adversarial ...
