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Pruning In Time (PIT): A Lightweight Network Architecture Optimizer for Temporal Convolutional Networks., , , , , , , и . CoRR, (2022)Lightweight Neural Architecture Search for Temporal Convolutional Networks at the Edge., , , , , , , , и . CoRR, (2023)Tiny-PULP-Dronets: Squeezing Neural Networks for Faster and Lighter Inference on Multi-Tasking Autonomous Nano-Drones., , , , , , и . AICAS, стр. 287-290. IEEE, (2022)A Sim-to-Real Deep Learning-based Framework for Autonomous Nano-drone Racing., , , , , , , , , и . CoRR, (2023)Automated Tuning of End-to-end Neural Flight Controllers for Autonomous Nano-drones., , , и . AICAS, стр. 1-4. IEEE, (2021)Bio-inspired Autonomous Exploration Policies with CNN-based Object Detection on Nano-drones., , , , , и . DATE, стр. 1-6. IEEE, (2023)Reducing neural architecture search spaces with training-free statistics and computational graph clustering., , , , , и . CF, стр. 213-214. ACM, (2022)Pruning In Time (PIT): A Lightweight Network Architecture Optimizer for Temporal Convolutional Networks., , , , , , , и . DAC, стр. 1015-1020. IEEE, (2021)A Sim-to-Real Deep Learning-Based Framework for Autonomous Nano-Drone Racing., , , , , , , , , и . IEEE Robotics Autom. Lett., 9 (2): 1899-1906 (февраля 2024)Improving Autonomous Nano-Drones Performance via Automated End-to-End Optimization and Deployment of DNNs., , , , и . IEEE J. Emerg. Sel. Topics Circuits Syst., 11 (4): 548-562 (2021)