Divya Gopinath | DeepAI

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∙ 05/27/2023

Assumption Generation for the Verification of Learning-Enabled Autonomous Systems

Providing safety guarantees for autonomous systems is difficult as these...

0 Corina Pasareanu, et al. ∙

∙ 02/06/2023

Closed-loop Analysis of Vision-based Autonomous Systems: A Case Study

Deep neural networks (DNNs) are increasingly used in safety-critical aut...

0 Corina S. Pasareanu, et al. ∙

∙ 08/05/2022

An Overview of Structural Coverage Metrics for Testing Neural Networks

Deep neural network (DNN) models, including those used in safety-critica...

0 Muhammad Usman, et al. ∙

∙ 05/08/2022

VPN: Verification of Poisoning in Neural Networks

Neural networks are successfully used in a variety of applications, many...

0 Youcheng Sun, et al. ∙

∙ 01/31/2022

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

We study backdoor poisoning attacks against image classification network...

0 Muhammad Usman, et al. ∙

∙ 10/25/2021

QuantifyML: How Good is my Machine Learning Model?

The efficacy of machine learning models is typically determined by compu...

0 Muhammad Usman, et al. ∙

∙ 09/23/2021

MedKnowts: Unified Documentation and Information Retrieval for Electronic Health Records

Clinical documentation can be transformed by Electronic Health Records, ...

0 Luke Murray, et al. ∙

∙ 03/23/2021

NNrepair: Constraint-based Repair of Neural Network Classifiers

We present NNrepair, a constraint-based technique for repairing neural n...

0 Muhammad Usman, et al. ∙

∙ 02/27/2021

NEUROSPF: A tool for the Symbolic Analysis of Neural Networks

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

0 Muhammad Usman, et al. ∙

∙ 07/29/2020

Fast, Structured Clinical Documentation via Contextual Autocomplete

We present a system that uses a learned autocompletion mechanism to faci...

9 Divya Gopinath, et al. ∙

∙ 04/17/2020

Parallelization Techniques for Verifying Neural Networks

Inspired by recent successes with parallel optimization techniques for s...

0 Haoze Wu, et al. ∙

∙ 12/01/2019

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

Even as deep neural networks have become very effective for tasks in vis...

39 Edward Kim, et al. ∙

∙ 09/30/2019

Hamiltonicity in Semi-Regular Tessellation Dual Graphs

This paper shows NP-completeness for finding Hamiltonian cycles in induc...

0 Divya Gopinath, et al. ∙

∙ 04/29/2019

Finding Invariants in Deep Neural Networks

We present techniques for automatically inferring invariant properties o...

0 Divya Gopinath, et al. ∙

∙ 10/18/2018

Compositional Verification for Autonomous Systems with Deep Learning Components

As autonomy becomes prevalent in many applications, ranging from recomme...

0 Corina S. Pasareanu, et al. ∙

∙ 07/27/2018

Symbolic Execution for Deep Neural Networks

Deep Neural Networks (DNN) are increasingly used in a variety of applica...

0 Divya Gopinath, et al. ∙

∙ 10/02/2017

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

Deep neural networks have become widely used, obtaining remarkable resul...

0 Divya Gopinath, et al. ∙

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