Tag Archives: ML

Structural Coding: A Low-Cost Scheme to Protect CNNs from Large-Granularity Memory Faults

Ali Asgari, Florian Geissler, Syed Qutub, Michael Paulitsch, Prashant Nair, and Karthik Pattabiraman, Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), 2023. (Acceptance Rate: 23.9%) [ PDF | Talk ] (code). Artifacts Available and Functional
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A Low-cost Strategic Monitoring Approach for Scalable and Interpretable Error Detection in Deep Neural Networks

Florian Geissler, Syed Qutub, Michael Paulitsch and Karthik Pattabiraman, Proceedings of the International Conference on Computer Safety, Reliability and Security (SafeComp), 2023. (Acceptance Rate: 20%) [PDF | Talk]
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Jujutsu: A Two-stage Defense against Adversarial Patch Attacks on Deep Neural Networks

Zitao Chen, Pritam Dash, and Karthik Pattabiraman. Proceedings of the 18th ACM ASIA Conference on Computer and Communications Security (ACM ASIACCS), 2023. (Acceptance Rate: 16%) [ PDF | Talk ] (code)
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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

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

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

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

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

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

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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