no code implementations • 28 Mar 2024 • Ravi Mangal, Nina Narodytska, Divya Gopinath, Boyue Caroline Hu, Anirban Roy, Susmit Jha, Corina Pasareanu
The analysis of vision-based deep neural networks (DNNs) is highly desirable but it is very challenging due to the difficulty of expressing formal specifications for vision tasks and the lack of efficient verification procedures.
no code implementations • 27 May 2023 • Corina Pasareanu, Ravi Mangal, Divya Gopinath, Huafeng Yu
Our insight is that we can analyze the system in the absence of the DNN perception components by automatically synthesizing assumptions on the DNN behaviour that guarantee the satisfaction of the required safety properties.
no code implementations • 6 Feb 2023 • Corina S. Pasareanu, Ravi Mangal, Divya Gopinath, Sinem Getir Yaman, Calum Imrie, Radu Calinescu, Huafeng Yu
We address the above challenges by replacing the camera and the network with a compact probabilistic abstraction built from the confusion matrices computed for the DNN on a representative image data set.
1 code implementation • 5 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.
no code implementations • 8 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.
1 code implementation • 31 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.
no code implementations • 25 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.
1 code implementation • 23 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.
no code implementations • 27 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.
1 code implementation • 29 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.
no code implementations • 17 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.
no code implementations • 1 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.
1 code implementation • 29 Apr 2019 • Divya Gopinath, Hayes Converse, Corina S. Pasareanu, Ankur Taly
We present techniques for automatically inferring formal properties of feed-forward neural networks.
no code implementations • 18 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.
no code implementations • 2 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.