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cs.LG2024
Compositional Curvature Bounds for Deep Neural Networks
Taha Entesari, Sina Sharifi, Mahyar Fazlyab
A key challenge that threatens the widespread use of neural networks in safety-critical applications is their vulnerability to adversarial attacks. In this paper, we study the seco…
cs.LG2024
Provable Bounds on the Hessian of Neural Networks: Derivative-Preserving Reachability Analysis
Sina Sharifi, Mahyar Fazlyab
We propose a novel reachability analysis method tailored for neural networks with smooth activations. Our method relies on constructing an end-to-end first-order Taylor expansion o…
cs.CV2024★ 1 cited
Gradient-Regularized Out-of-Distribution Detection
Sina Sharifi, Taha Entesari, Bardia Safaei +2
One of the challenges for neural networks in real-life applications is the overconfident errors these models make when the data is not from the original training distribution. Addr…