119 citations · 127 across the 10 of their papers we have counts for
10 papers · 1 filter
Efficiently Finding Adversarial Examples with DNN Preprocessing
Avriti Chauhan, Mohammad Afzal, Hrishikesh Karmarkar +3
Deep Neural Networks (DNNs) are everywhere, frequently performing a fairly complex task that used to be unimaginable for machines to carry out. In doing so, they do a lot of decisi…
Efficient Neural Network Analysis with Sum-of-Infeasibilities
Haoze Wu, Aleksandar Zeljić, Guy Katz +1
Inspired by sum-of-infeasibilities methods in convex optimization, we propose a novel procedure for analyzing verification queries on neural networks with piecewise-linear activati…
Scenario-Assisted Deep Reinforcement Learning
Raz Yerushalmi, Guy Amir, Achiya Elyasaf +3
Deep reinforcement learning has proven remarkably useful in training agents from unstructured data. However, the opacity of the produced agents makes it difficult to ensure that th…
An Abstraction-Refinement Approach to Verifying Convolutional Neural Networks
Matan Ostrovsky, Clark Barrett, Guy Katz
Convolutional neural networks have gained vast popularity due to their excellent performance in the fields of computer vision, image processing, and others. Unfortunately, it is no…
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…
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…