most citedA Formalization of Robustness for Deep Neural Networks

16 citations · 49 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG201916 cited

A Formalization of Robustness for Deep Neural Networks

Tommaso Dreossi, Shromona Ghosh, Alberto Sangiovanni-Vincentelli +1

Deep neural networks have been shown to lack robustness to small input perturbations. The process of generating the perturbations that expose the lack of robustness of neural netwo…

cs.LO20197 cited

A Model Counter's Guide to Probabilistic Systems

Marcell Vazquez-Chanlatte, Markus N. Rabe, Sanjit A. Seshia

In this paper, we systematize the modeling of probabilistic systems for the purpose of analyzing them with model counting techniques. Starting from unbiased coin flips, we show how…

cs.AI201913 cited

VERIFAI: A Toolkit for the Design and Analysis of Artificial Intelligence-Based Systems

Tommaso Dreossi, Daniel J. Fremont, Shromona Ghosh +4

We present VERIFAI, a software toolkit for the formal design and analysis of systems that include artificial intelligence (AI) and machine learning (ML) components. VERIFAI particu…

cs.LO201411 cited

Are There Good Mistakes? A Theoretical Analysis of CEGIS

Susmit Jha, Sanjit A. Seshia

Counterexample-guided inductive synthesis CEGIS is used to synthesize programs from a candidate space of programs. The technique is guaranteed to terminate and synthesize the corre…

cs.LO20142 cited

Speeding Up SMT-Based Quantitative Program Analysis

Daniel J. Fremont, Sanjit A. Seshia

Quantitative program analysis involves computing numerical quantities about individual or collections of program executions. An example of such a computation is quantitative inform…