Search Results for author: Divya Gopinath

Found 12 papers, 5 papers with code

An Overview of Structural Coverage Metrics for Testing Neural Networks

1 code implementation5 Aug 2022 Muhammad Usman, Youcheng Sun, Divya Gopinath, Rishi Dange, Luca Manolache, Corina S. Pasareanu

Deep neural network (DNN) models, including those used in safety-critical domains, need to be thoroughly tested to ensure that they can reliably perform well in different scenarios.

DNN Testing

VPN: Verification of Poisoning in Neural Networks

no code implementations8 May 2022 Youcheng Sun, Muhammad Usman, Divya Gopinath, Corina S. Păsăreanu

Neural networks are successfully used in a variety of applications, many of them having safety and security concerns.

Data Poisoning Image Classification +1

AntidoteRT: Run-time Detection and Correction of Poison Attacks on Neural Networks

1 code implementation31 Jan 2022 Muhammad Usman, Youcheng Sun, Divya Gopinath, Corina S. Pasareanu

For correction, we propose an input correction technique that uses a differential analysis to identify the trigger in the detected poisoned images, which is then reset to a neutral color.

Image Classification

QuantifyML: How Good is my Machine Learning Model?

no code implementations25 Oct 2021 Muhammad Usman, Divya Gopinath, Corina S. Păsăreanu

The efficacy of machine learning models is typically determined by computing their accuracy on test data sets.

BIG-bench Machine Learning

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

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.

Adversarial Robustness Machine Translation +2

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