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Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM., , , , , , , , , и 4 other автор(ы). CoRR, (2019)A New Fusion Approach for Extracting Urban Built-up Areas from Multisource Remotely Sensed Data., , , и . Remote. Sens., 11 (21): 2516 (2019)Tiny but Accurate: A Pruned, Quantized and Optimized Memristor Crossbar Framework for Ultra Efficient DNN Implementation., , , , , , , , и . CoRR, (2019)An area and energy efficient design of domain-wall memory-based deep convolutional neural networks using stochastic computing., , , , , , , и . ISQED, стр. 314-321. IEEE, (2018)An Extremely Simple Reinforcement Learning Rule for Neural Networks.. ISNN (1), том 4491 из Lecture Notes in Computer Science, стр. 434-440. Springer, (2007)Algorithms for regret theory and group satisfaction degree under interval-valued dual hesitant fuzzy sets in stochastic multiple attribute decision making method., , , , и . J. Intell. Fuzzy Syst., 37 (3): 3639-3653 (2019)Efficient Cloud Resource Management using Neuromorphic Modeling and Prediction for Virtual Machine Resource Utilization., , , , и . ICESS, стр. 1-8. IEEE, (2019)Fault-Tolerant Deep Neural Networks for Processing-In-Memory based Autonomous Edge Systems., , , , , и . DATE, стр. 424-429. IEEE, (2022)FILM-QNN: Efficient FPGA Acceleration of Deep Neural Networks with Intra-Layer, Mixed-Precision Quantization., , , , , , , и . FPGA, стр. 134-145. ACM, (2022)AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates., , , , , и . AAAI, стр. 4876-4883. AAAI Press, (2020)