119 citations · 468 across the 39 of their papers we have counts for
5 papers · 2 filters
Minimal Multi-Layer Modifications of Deep Neural Networks
Idan Refaeli, Guy Katz
Deep neural networks (DNNs) have become increasingly popular in recent years. However, despite their many successes, DNNs may also err and produce incorrect and potentially fatal o…
RoMA: a Method for Neural Network Robustness Measurement and Assessment
Natan Levy, Guy Katz
Neural network models have become the leading solution for a large variety of tasks, such as classification, language processing, protein folding, and others. However, their reliab…
Pruning and Slicing Neural Networks using Formal Verification
Ori Lahav, Guy Katz
Deep neural networks (DNNs) play an increasingly important role in various computer systems. In order to create these networks, engineers typically specify a desired topology, and…
Towards Scalable Verification of Deep Reinforcement Learning
Guy Amir, Michael Schapira, Guy Katz
Deep neural networks (DNNs) have gained significant popularity in recent years, becoming the state of the art in a variety of domains. In particular, deep reinforcement learning (D…
Neural Network Robustness as a Verification Property: A Principled Case Study
Marco Casadio, Ekaterina Komendantskaya, Matthew L. Daggitt +4
Neural networks are very successful at detecting patterns in noisy data, and have become the technology of choice in many fields. However, their usefulness is hampered by their sus…