Tag Archives: reliability

LLTFI: Framework Agnostic Fault Injection for Machine Learning Applications

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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Nekara: Generalized Consistency Testing

Udit Agarwal, Pantazis Deligiannis, Cheng Huang, Kumseok Jung, Akash Lal, Immad Naseer, Matthew Parkinson, Arun Thangamani, Jyothi Vedurada, Yunpeng Xiao, Proceedings of the ACM/IEEE International Conference on Automated Software Engineering (ASE), 2021. [ PDF | Talk Slides]

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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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ThingsMigrate: Platform-Independent Migration of Stateful JavaScript IoT Applications

Kumseok Jung, Julien Gascon-Samson, Shivanshu Goyal, Armin Rezalean-Asel, and Karthik Pattabiraman, To appear in the Journal of Software Practice and Experience (SPE). [ PDF ]
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An Empirical Study of the Impact of Single and Multiple Bit-Flip Errors in Programs

Behrooz Sangchoolie, Karthik Pattabiraman and Johan Karlsson, To appear in the IEEE Transactions on Dependable and Secure Computing (TDSC). [ PDF ]
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