119 citations · 127 across the 10 of their papers we have counts for
5 papers · 1 filter
An SMT-Based Approach for Verifying Binarized Neural Networks
Guy Amir, Haoze Wu, Clark Barrett +1
Deep learning has emerged as an effective approach for creating modern software systems, with neural networks often surpassing hand-crafted systems. Unfortunately, neural networks…
Global Optimization of Objective Functions Represented by ReLU Networks
Christopher A. Strong, Haoze Wu, Aleksandar Zeljić +4
Neural networks can learn complex, non-convex functions, and it is challenging to guarantee their correct behavior in safety-critical contexts. Many approaches exist to find failur…
Guarded Deep Learning using Scenario-Based Modeling
Guy Katz
Deep neural networks (DNNs) are becoming prevalent, often outperforming manually-created systems. Unfortunately, DNN models are opaque to humans, and may behave in unexpected ways…
Parallelization Techniques for Verifying Neural Networks
Haoze Wu, Alex Ozdemir, Aleksandar Zeljić +7
Inspired by recent successes with parallel optimization techniques for solving Boolean satisfiability, we investigate a set of strategies and heuristics that aim to leverage parall…
Verifying Recurrent Neural Networks using Invariant Inference
Yuval Jacoby, Clark Barrett, Guy Katz
Deep neural networks are revolutionizing the way complex systems are developed. However, these automatically-generated networks are opaque to humans, making it difficult to reason…