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The Scientific Method in the Science of Machine Learning.

, и . CoRR, (2019)

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Ulrich Paganini

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Neural Algorithmic Reasoning with Causal Regularisation., , , , , , , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 2272-2288. PMLR, (2023)Preface., , , , и . Preregister@NeurIPS, том 148 из Proceedings of Machine Learning Research, стр. i. PMLR, (2020)Unified Scaling Laws for Routed Language Models., , , , , , , , , и 16 other автор(ы). ICML, том 162 из Proceedings of Machine Learning Research, стр. 4057-4086. PMLR, (2022)Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis., , и . Comput. Softw. Big Sci., (ноября 2017)The Scientific Method in the Science of Machine Learning., и . CoRR, (2019)Streamlining Tensor and Network Pruning in PyTorch., и . CoRR, (2020)Improving language models by retrieving from trillions of tokens, , , , , , , , , и 18 other автор(ы). (2021)cite arxiv:2112.04426Comment: Fix incorrect reported numbers in Table 14.Improving Language Models by Retrieving from Trillions of Tokens., , , , , , , , , и 18 other автор(ы). ICML, том 162 из Proceedings of Machine Learning Research, стр. 2206-2240. PMLR, (2022)Machine Learning Solutions for High Energy Physics: Applications to Electromagnetic Shower Generation, Flavor Tagging, and the Search for di-Higgs Production.. CoRR, (2019)One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers., , , и . NeurIPS, стр. 4933-4943. (2019)