Tag Archives: ML

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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Fault Injection for TensorFlow Applications

Posted on May 10, 2022 by | Comments Off on Fault Injection for TensorFlow Applications

Niranjhana Narayanan, Zitao Chen, Bo Fang, Guanpeng Li, Karthik Pattabiraman, and Nathan DeBardeleben, IEEE Transactions on Dependable and Secure Computing (TDSC). Acceptance Date: May 2022. [ PDF ] (code1, code2)
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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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Towards a safety case for hardware-fault tolerance in convolutional neural networks using activation range supervision

Posted on August 21, 2021 by | Comments Off on Towards a safety case for hardware-fault tolerance in convolutional neural networks using activation range supervision

Florian Geissler, Syed Qutub, Sayanta Roychowdhury, Ali Asgari, Yang Peng, Akash Dhamasia, Ralf Graefe, Karthik Pattabiraman and Michael Paulitsch, AI Safety Workshop 2021, Best Paper Award Nominee (1 of 4) [ PDF | Talk ] (arXIV)
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A Low-cost Fault Corrector for Deep Neural Networks through Range Restriction

Posted on March 10, 2021 by | Comments Off on A Low-cost Fault Corrector for Deep Neural Networks through Range Restriction

Zitao Chen, Guanpeng Li, and Karthik Pattabiraman, IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2021. (Acceptance Rate: 16.5%). [ PDF | Talk , Video] (arXIV, code) Best Paper Award Runner up (1 of 2 among nearly 300 submissions). Incorporated into Intel’s OpenVino2 Framework (More details, Documentation).
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New Wine in an Old Bottle: N-Version Programming for Machine Learning Components

Posted on September 1, 2020 by | Comments Off on New Wine in an Old Bottle: N-Version Programming for Machine Learning Components

Arpan Gujarati, Sathish Gopalakrishnan, and Karthik Pattabiraman, IEEE International Workshop on Software Certification (WoSoCER), 2020. Held in conjunction with the IEEE International Symposium on Software Reliability Engineering (ISSRE), 2020. [PDF][Talk]
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TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications

Posted on August 14, 2020 by | Comments Off on TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications

Zitao Chen, Niranjhana Narayanan, Bo Fang, Guanpeng Li, Karthik Pattabiraman, and Nathan DeBardeleben, IEEE International Symposium on Software Reliability Engineering (ISSRE), 2020. (Acceptance Rate: 26%) [ PDF | Talk ] (Code)
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BinFI: An Efficient Fault Injector for Safety-Critical Machine Learning Systems

Posted on June 16, 2019 by | Comments Off on BinFI: An Efficient Fault Injector for Safety-Critical Machine Learning Systems

Zitao Chen, Guanpeng Li, Karthik Pattabiraman, and Nathan DeBardeleben, The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), 2019. (Acceptance Rate: 21%) [ PDF | Talk ] ( Code Finalist for the SC reproducibility challenge (one of 3 papers))
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