Tag Archives: abraham

Evaluating the Effect of Common Annotation Faults on Object Detection Techniques

Posted on August 7, 2023 by | Comments Off on Evaluating the Effect of Common Annotation Faults on Object Detection Techniques

Abraham Chan, Arpan Gujarati, Karthik Pattabiraman and Sathish Gopalakrishnan, Proceedings of the IEEE International Symposium on Software Reliability Engineering (ISSRE), 2023. (Acceptance Rate: 28.5%) [ PDF | Talk ] (Code). Artifacts Available and Reviewed.

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Resilience Assessment of Large Language Models under Transient Hardware Faults

Posted on August 7, 2023 by | Comments Off on Resilience Assessment of Large Language Models under Transient Hardware Faults

Udit Agarwal, Abraham Chan, and Karthik Pattabiraman, Proceedings of the IEEE International Symposium on Software Reliability Engineering (ISSRE), 2023. (Acceptance Rate: 28.5%) [ PDF | Talk ] (Code). Artifacts Available and Reviewed.
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Mixed Precision Support in HPC Applications: What About Reliability?

Posted on July 20, 2023 by | Comments Off on Mixed Precision Support in HPC Applications: What About Reliability?

Alessio Netti, Yang Peng, Patrik Omland, Michael Paulitsch, Jorge Parra, Gustavo Espinosa, Udit Agarwal, Abraham Chan, and Karthik Pattabiraman, Journal of Parallel and Distributed Computing (JPDC). [ PDF ] (code)
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Towards Reliability Assessment of Systolic Arrays against Stuck-at Faults

Posted on July 8, 2023 by | Comments Off on Towards Reliability Assessment of Systolic Arrays against Stuck-at Faults

Udit Kumar Agarwal, Abraham Chan, Ali Asgari, and Karthik Pattabiraman. 19th IEEE Workshop on Silicon Errors in Logic – System Effects (SELSE), 2023. Received Best-of-SELSE award (one of three papers). [ PDF  | Presentation ] (Code)
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LLTFI: Framework Agnostic Fault Injection for Machine Learning Applications (Tools and Artifact Track)

Posted on July 29, 2022 by | Comments Off on LLTFI: Framework Agnostic Fault Injection for Machine Learning Applications (Tools and Artifact Track)

Udit Agarwal, Abraham Chan, and Karthik Pattabiraman, IEEE International Symposium on Software Reliability Engineering (ISSRE), 2022. (Acceptance Rate: 29%) [ PDF | Talk (video) ] (Code)
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The Fault in Our Data Stars: Studying Mitigation Techniques against Faulty Training Data in ML Applications

Posted on March 14, 2022 by | Comments Off on The Fault in Our Data Stars: Studying Mitigation Techniques against Faulty Training Data in ML Applications

Abraham Chan, Arpan Gujarati, Karthik Pattabiraman, and Sathish Gopalakrishnan. IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2022. (Acceptance rate: 18.7%) [ PDF | Talk ] (Code)
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Understanding the Resilience of Neural Network Ensembles against Faulty Training Data

Posted on October 22, 2021 by | Comments Off on Understanding the Resilience of Neural Network Ensembles against Faulty Training Data

Abraham Chan, Niranjhana Narayananan, Arpan Gujarati, Karthik Pattabiraman, and Sathish Gopalakrishnan, IEEE International Symposium on Quality, Reliability and Security (QRS), 2021. Full paper (Acceptance Rate: 25.1%) [ PDF | Talk | Video ] Best Paper Award (1 of 3)

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(WiP) LLTFI: Low-Level Tensor Fault Injector

Posted on August 31, 2021 by | Comments Off on (WiP) LLTFI: Low-Level Tensor Fault Injector

Abraham Chan, Udit Agarwal, and Karthik Pattabiraman. IEEE International Workshop on Software Certification (WoSoCER’21), co-held with the IEEE International Symposium on Software Reliability Engineering (ISSRE), 2021. [ PDF | Talk ] (Code)
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IPA: Error Propagation Analysis of Multi-threaded Programs Using Likely Invariants

Posted on March 24, 2017 by | Comments Off on IPA: Error Propagation Analysis of Multi-threaded Programs Using Likely Invariants

Abraham Chan, Stefan Winter, Habib Saissi, Karthik Pattabiraman and Neeraj Suri. Proceedings of the IEEE International Conference on Software Testing, Verification and Validation (ICST), 2017. (Acceptance Rate: 27%) [PDF | Talk]
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