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When Bio-Inspired Computing meets Deep Learning: Low-Latency, Accurate, & Energy-Efficient Spiking Neural Networks from Artificial Neural Networks.

, , , , и . CoRR, (2023)

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Error Analysis of ZFP Compression for Floating-Point Data., , , , и . SIAM J. Sci. Comput., 41 (3): A1867-A1898 (2019)A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness., , , , и . NeurIPS, стр. 664-676. (2021)GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations., , , , , , , , и . CoRR, (2024)Stability Analysis of Inline ZFP Compression for Floating-Point Data in Iterative Methods., , , , и . CoRR, (2020)Neural Image Compression: Generalization, Robustness, and Spectral Biases., , , и . CoRR, (2023)DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training., , , , , , , , , и . CoRR, (2023)A Framework for Error-Bounded Approximate Computing, with an Application to Dot Products., , и . SIAM J. Sci. Comput., 44 (3): 1290- (2022)Construction of a full row-rank matrix system for multiple scanning directions in discrete tomography., , и . J. Comput. Appl. Math., (2017)Unsupervised Test-Time Adaptation of Deep Neural Networks at the Edge: A Case Study., , , и . DATE, стр. 412-417. IEEE, (2022)Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices., , , и . ISPASS, стр. 236-238. IEEE, (2022)