Search Results for author: Divya Gopinath

Found 8 papers, 4 papers with code

NNrepair: Constraint-based Repair of Neural Network Classifiers

1 code implementation23 Mar 2021 Muhammad Usman, Divya Gopinath, Youcheng Sun, Yannic Noller, Corina Pasareanu

We present novel strategies to enable precise yet efficient repair such as inferring correctness specifications to act as oracles for intermediate layer repair, and generation of experts for each class.

Fault localization

NEUROSPF: A tool for the Symbolic Analysis of Neural Networks

1 code implementation27 Feb 2021 Muhammad Usman, Yannic Noller, Corina Pasareanu, Youcheng Sun, Divya Gopinath

This paper presents NEUROSPF, a tool for the symbolic analysis of neural networks.

Fast, Structured Clinical Documentation via Contextual Autocomplete

1 code implementation29 Jul 2020 Divya Gopinath, Monica Agrawal, Luke Murray, Steven Horng, David Karger, David Sontag

We present a system that uses a learned autocompletion mechanism to facilitate rapid creation of semi-structured clinical documentation.

Parallelization Techniques for Verifying Neural Networks

no code implementations17 Apr 2020 Haoze Wu, Alex Ozdemir, Aleksandar Zeljić, Ahmed Irfan, Kyle Julian, Divya Gopinath, Sadjad Fouladi, Guy Katz, Corina Pasareanu, Clark Barrett

Inspired by recent successes with parallel optimization techniques for solving Boolean satisfiability, we investigate a set of strategies and heuristics that aim to leverage parallel computing to improve the scalability of neural network verification.

A Programmatic and Semantic Approach to Explaining and DebuggingNeural Network Based Object Detectors

no code implementations1 Dec 2019 Edward Kim, Divya Gopinath, Corina Pasareanu, Sanjit Seshia

It is programmatic in that scenario representation is a program in a domain-specific probabilistic programming language which can be used to generate synthetic data to test a given perception module.

Probabilistic Programming

Property Inference for Deep Neural Networks

1 code implementation29 Apr 2019 Divya Gopinath, Hayes Converse, Corina S. Pasareanu, Ankur Taly

We present techniques for automatically inferring formal properties of feed-forward neural networks.

Compositional Verification for Autonomous Systems with Deep Learning Components

no code implementations18 Oct 2018 Corina S. Pasareanu, Divya Gopinath, Huafeng Yu

As autonomy becomes prevalent in many applications, ranging from recommendation systems to fully autonomous vehicles, there is an increased need to provide safety guarantees for such systems.

Autonomous Vehicles Recommendation Systems

DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks

no code implementations2 Oct 2017 Divya Gopinath, Guy Katz, Corina S. Pasareanu, Clark Barrett

We propose a novel approach for automatically identifying safe regions of the input space, within which the network is robust against adversarial perturbations.

Machine Translation Speech Recognition

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