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MAPLE-Edge: A Runtime Latency Predictor for Edge Devices.

, , , and . CVPR Workshops, page 3659-3667. IEEE, (2022)

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Deep Neural Network Perception Models and Robust Autonomous Driving Systems., , , , and . CoRR, (2020)Faster Attention Is What You Need: A Fast Self-Attention Neural Network Backbone Architecture for the Edge via Double-Condensing Attention Condensers., , , , and . CoRR, (2022)A Simple Fine-tuning Is All You Need: Towards Robust Deep Learning Via Adversarial Fine-tuning., , and . CoRR, (2020)SANE: Towards Improved Prediction Robustness via Stochastically Activated Network Ensembles., , and . CVPR Workshops, page 103-111. Computer Vision Foundation / IEEE, (2019)COVID-Net CT-S: 3D Convolutional Neural Network Architectures for COVID-19 Severity Assessment using Chest CT Images., , , and . CoRR, (2021)High-Throughput, High-Performance Deep Learning-Driven Light Guide Plate Surface Visual Quality Inspection Tailored for Real-World Manufacturing Environments., , , , and . AAAI, page 15745-15751. AAAI Press, (2023)MAPLE: Microprocessor A Priori for Latency Estimation., , and . CVPR Workshops, page 2746-2755. IEEE, (2022)MAPLE-Edge: A Runtime Latency Predictor for Edge Devices., , , and . CVPR Workshops, page 3659-3667. IEEE, (2022)CellDefectNet: A Machine-designed Attention Condenser Network for Electroluminescence-based Photovoltaic Cell Defect Inspection., , , , , and . CRV, page 219-223. IEEE, (2022)SANE: Exploring Adversarial Robustness With Stochastically Activated Network Ensembles., , , , and . CVPR Workshops, page 95-98. Computer Vision Foundation / IEEE, (2019)