119 citations · 468 across the 39 of their papers we have counts for
14 papers · 1 filter
veriFIRE: Verifying an Industrial, Learning-Based Wildfire Detection System
Guy Amir, Ziv Freund, Guy Katz +2
In this short paper, we present our ongoing work on the veriFIRE project -- a collaboration between industry and academia, aimed at using verification for increasing the reliabilit…
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…
Taming Reachability Analysis of DNN-Controlled Systems via Abstraction-Based Training
Jiaxu Tian, Dapeng Zhi, Si Liu +3
The intrinsic complexity of deep neural networks (DNNs) makes it challenging to verify not only the networks themselves but also the hosting DNN-controlled systems. Reachability an…
Towards Formal XAI: Formally Approximate Minimal Explanations of Neural Networks
Shahaf Bassan, Guy Katz
With the rapid growth of machine learning, deep neural networks (DNNs) are now being used in numerous domains. Unfortunately, DNNs are "black-boxes", and cannot be interpreted by h…
Tighter Abstract Queries in Neural Network Verification
Elazar Cohen, Yizhak Yisrael Elboher, Clark Barrett +1
Neural networks have become critical components of reactive systems in various domains within computer science. Despite their excellent performance, using neural networks entails n…
Neural Network Verification using Residual Reasoning
Yizhak Yisrael Elboher, Elazar Cohen, Guy Katz
With the increasing integration of neural networks as components in mission-critical systems, there is an increasing need to ensure that they satisfy various safety and liveness re…
On Optimizing Back-Substitution Methods for Neural Network Verification
Tom Zelazny, Haoze Wu, Clark Barrett +1
With the increasing application of deep learning in mission-critical systems, there is a growing need to obtain formal guarantees about the behaviors of neural networks. Indeed, ma…
Constrained Reinforcement Learning for Robotics via Scenario-Based Programming
Davide Corsi, Raz Yerushalmi, Guy Amir +3
Deep reinforcement learning (DRL) has achieved groundbreaking successes in a wide variety of robotic applications. A natural consequence is the adoption of this paradigm for safety…
Neural Network Verification with Proof Production
Omri Isac, Clark Barrett, Min Zhang +1
Deep neural networks (DNNs) are increasingly being employed in safety-critical systems, and there is an urgent need to guarantee their correctness. Consequently, the verification c…
Verifying Learning-Based Robotic Navigation Systems
Guy Amir, Davide Corsi, Raz Yerushalmi +4
Deep reinforcement learning (DRL) has become a dominant deep-learning paradigm for tasks where complex policies are learned within reactive systems. Unfortunately, these policies a…
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…
Verification-Aided Deep Ensemble Selection
Guy Amir, Tom Zelazny, Guy Katz +1
Deep neural networks (DNNs) have become the technology of choice for realizing a variety of complex tasks. However, as highlighted by many recent studies, even an imperceptible per…
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…