Gradient-Regularized Out-of-Distribution Detection

no code implementations • 18 Apr 2024 • Sina Sharifi, Taha Entesari, Bardia Safaei, Vishal M. Patel, Mahyar Fazlyab

In this work, we propose the idea of leveraging the information embedded in the gradient of the loss function during training to enable the network to not only learn a desired OOD score for each sample but also to exhibit similar behavior in a local neighborhood around each sample.

Out-of-Distribution Detection Out of Distribution (OOD) Detection

Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization

1 code implementation • NeurIPS 2023 • Mahyar Fazlyab, Taha Entesari, Aniket Roy, Rama Chellappa

As a result, there has been an increasing interest in developing training procedures that can directly manipulate the decision boundary in the input space.

Automated Reachability Analysis of Neural Network-Controlled Systems via Adaptive Polytopes

1 code implementation • 14 Dec 2022 • Taha Entesari, Mahyar Fazlyab

Over-approximating the reachable sets of dynamical systems is a fundamental problem in safety verification and robust control synthesis.

ReachLipBnB: A branch-and-bound method for reachability analysis of neural autonomous systems using Lipschitz bounds

1 code implementation • 1 Nov 2022 • Taha Entesari, Sina Sharifi, Mahyar Fazlyab

We propose a novel Branch-and-Bound method for reachability analysis of neural networks in both open-loop and closed-loop settings.