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DNN Analysis through Synthetic Data Variation.

, , и . CSCS, стр. 4:1-4:10. ACM, (2020)

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Sha Liu

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Min Sha

Jiang Sha

 

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Structural Coding: A Low-Cost Scheme to Protect CNNs from Large-Granularity Memory Faults., , , , , и . SC, стр. 85:1-85:17. ACM, (2023)Large-Scale Application of Fault Injection into PyTorch Models -an Extension to PyTorchFI for Validation Efficiency., , , и . DSN-S, стр. 56-62. IEEE, (2023)BEA: Revisiting anchor-based object detection DNN using Budding Ensemble Architecture., , , , , , , , и . CoRR, (2023)BEA: Revisiting anchor-based object detection DNN using Budding Ensemble Architecture., , , , , , , , и . BMVC, стр. 792-797. BMVA Press, (2023)A Low-Cost Strategic Monitoring Approach for Scalable and Interpretable Error Detection in Deep Neural Networks., , , и . SAFECOMP, том 14181 из Lecture Notes in Computer Science, стр. 75-88. Springer, (2023)Hardware Faults that Matter: Understanding and Estimating the Safety Impact of Hardware Faults on Object Detection DNNs., , , , , , и . SAFECOMP, том 13414 из Lecture Notes in Computer Science, стр. 298-318. Springer, (2022)DNN Analysis through Synthetic Data Variation., , и . CSCS, стр. 4:1-4:10. ACM, (2020)Towards a Safety Case for Hardware Fault Tolerance in Convolutional Neural Networks Using Activation Range Supervision., , , , , , , , и . AISafety@IJCAI, том 2916 из CEUR Workshop Proceedings, CEUR-WS.org, (2021)