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06/23/2023
QNNRepair: Quantized Neural Network Repair
We present QNNRepair, the first method in the literature for repairing q...
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05/24/2023
A New Era in Software Security: Towards Self-Healing Software via Large Language Models and Formal Verification
In this paper we present a novel solution that combines the capabilities...
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11/24/2022
AIREPAIR: A Repair Platform for Neural Networks
We present AIREPAIR, a platform for repairing neural networks. It featur...
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11/23/2022
Safety Analysis of Autonomous Driving Systems Based on Model Learning
We present a practical verification method for safety analysis of the au...
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08/05/2022
An Overview of Structural Coverage Metrics for Testing Neural Networks
Deep neural network (DNN) models, including those used in safety-critica...
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05/25/2022
VeriFi: Towards Verifiable Federated Unlearning
Federated learning (FL) is a collaborative learning paradigm where parti...
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05/08/2022
VPN: Verification of Poisoning in Neural Networks
Neural networks are successfully used in a variety of applications, many...
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01/31/2022
AntidoteRT: Run-time Detection and Correction of Poison Attacks on Neural Networks
We study backdoor poisoning attacks against image classification network...
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12/10/2021
Copy, Right? A Testing Framework for Copyright Protection of Deep Learning Models
Deep learning (DL) models, especially those large-scale and high-perform...
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08/03/2021
Tutorials on Testing Neural Networks
Deep learning achieves remarkable performance on pattern recognition, bu...
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03/23/2021
NNrepair: Constraint-based Repair of Neural Network Classifiers
We present NNrepair, a constraint-based technique for repairing neural n...
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03/05/2021
Compositional Explanations for Image Classifiers
Existing algorithms for explaining the output of image classifiers perfo...
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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...
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02/11/2021
RobOT: Robustness-Oriented Testing for Deep Learning Systems
Recently, there has been a significant growth of interest in applying so...
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10/16/2020
Formal Verification of Robustness and Resilience of Learning-Enabled State Estimation Systems for Robotics
This paper presents a formal verification guided approach for a principl...
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08/31/2020
Ranking Policy Decisions
Policies trained via Reinforcement Learning (RL) are often needlessly co...
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02/06/2020
Reliability Validation of Learning Enabled Vehicle Tracking
This paper studies the reliability of a real-world learning-enabled syst...
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11/05/2019
Test Metrics for Recurrent Neural Networks
Recurrent neural networks (RNNs) have been applied to a broad range of a...
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08/06/2019
Explaining Deep Neural Networks Using Spectrum-Based Fault Localization
Deep neural networks (DNNs) increasingly replace traditionally developed...
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06/20/2019
testRNN: Coverage-guided Testing on Recurrent Neural Networks
Recurrent neural networks (RNNs) have been widely applied to various seq...
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12/18/2018
Safety and Trustworthiness of Deep Neural Networks: A Survey
In the past few years, significant progress has been made on deep neural...
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04/30/2018
Concolic Testing for Deep Neural Networks
Concolic testing alternates between CONCrete program execution and symbO...
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04/16/2018
Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for L0 Norm
Deployment of deep neural networks (DNNs) in safety or security-critical...
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03/10/2018
Testing Deep Neural Networks
Deep neural networks (DNNs) have a wide range of applications, and softw...
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12/07/2015
Parallel and sequential reclaiming in multicore real-time global scheduling
When integrating hard, soft and non-real-time tasks in general purpose o...
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04/01/2014
Toward Parametric Timed Interfaces for Real-Time Components
We propose here a framework to model real-time components consisting of ...
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02/06/2013
