16 citations · 32 across the 9 of their papers we have counts for
5 papers · 1 filter
Improving Local Effectiveness for Global robust training
Jingyue Lu, M. Pawan Kumar
Despite its popularity, deep neural networks are easily fooled. To alleviate this deficiency, researchers are actively developing new training strategies, which encourage models th…
Neural Network Branch-and-Bound for Neural Network Verification
Florian Jaeckle, Jingyue Lu, M. Pawan Kumar
Many available formal verification methods have been shown to be instances of a unified Branch-and-Bound (BaB) formulation. We propose a novel machine learning framework that can b…
Generating Adversarial Examples with Graph Neural Networks
Florian Jaeckle, M. Pawan Kumar
Recent years have witnessed the deployment of adversarial attacks to evaluate the robustness of Neural Networks. Past work in this field has relied on traditional optimization algo…
Comment on Stochastic Polyak Step-Size: Performance of ALI-G
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
This is a short note on the performance of the ALI-G algorithm (Berrada et al., 2020) as reported in (Loizou et al., 2021). ALI-G (Berrada et al., 2020) and SPS (Loizou et al., 202…
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition
Alessandro De Palma, Rudy Bunel, Alban Desmaison +4
We improve the scalability of Branch and Bound (BaB) algorithms for formally proving input-output properties of neural networks. First, we propose novel bounding algorithms based o…