48 citations · 64 across the 2 of their papers we have counts for
5 papers
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…
A Programmatic and Semantic Approach to Explaining and DebuggingNeural Network Based Object Detectors
Edward Kim, Divya Gopinath, Corina Pasareanu +1
Even as deep neural networks have become very effective for tasks in vision and perception, it remains difficult to explain and debug their behavior. In this paper, we present a pr…
Property Inference for Deep Neural Networks
Divya Gopinath, Hayes Converse, Corina S. Pasareanu +1
We present techniques for automatically inferring formal properties of feed-forward neural networks. We observe that a significant part (if not all) of the logic of feed forward ne…
Learning Probabilistic Systems from Tree Samples
Anvesh Komuravelli, Corina S. Pasareanu, Edmund M. Clarke
We consider the problem of learning a non-deterministic probabilistic system consistent with a given finite set of positive and negative tree samples. Consistency is defined with r…
Assume-Guarantee Abstraction Refinement for Probabilistic Systems
Anvesh Komuravelli, Corina S. Pasareanu, Edmund M. Clarke
We describe an automated technique for assume-guarantee style checking of strong simulation between a system and a specification, both expressed as non-deterministic Labeled Probab…