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SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud.

, , and . NIPS, page 4672-4681. (2017)

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ZKROWNN: Zero Knowledge Right of Ownership for Neural Networks., , , and . DAC, page 1-6. IEEE, (2023)Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles., , , , , , , and . IV, page 157-164. IEEE, (2021)zPROBE: Zero Peek Robustness Checks for Federated Learning., , , , , and . ICCV, page 4837-4847. IEEE, (2023)Optimal checkpointing for secure intermittently-powered IoT devices., , and . ICCAD, page 376-383. IEEE, (2017)AdaGL: Adaptive Learning for Agile Distributed Training of Gigantic GNNs., , , , and . DAC, page 1-6. IEEE, (2023)ThUnderVolt: Enabling Aggressive Voltage Underscaling and Timing Error Resilience for Energy Efficient Deep Neural Network Accelerators., , , and . CoRR, (2018)SafeTPU: A Verifiably Secure Hardware Accelerator for Deep Neural Networks., , and . VTS, page 1-6. IEEE, (2020)Circa: Stochastic ReLUs for Private Deep Learning., , , and . NeurIPS, page 2241-2252. (2021)Characterizing and Optimizing End-to-End Systems for Private Inference., , , , and . ASPLOS (3), page 89-104. ACM, (2023)SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud., , and . NIPS, page 4672-4681. (2017)