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Machine learning and the physical sciences

, , , , , , , и . (2019)cite arxiv:1903.10563.

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Thomas Cranmer

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Другие публикации лиц с тем же именем

Equivariant flow-based sampling for lattice gauge theory., , , , , , , и . CoRR, (2020)Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions., , , , , , , , , и 2 other автор(ы). CoRR, (2022)Introduction to Normalizing Flows for Lattice Field Theory, , , , , , , и . (2021)cite arxiv:2101.08176Comment: 38 pages, 5 numbered figures, Jupyter notebook included as ancillary file.Adversarial Variational Optimization of Non-Differentiable Simulators., , и . BNAIC/BENELEARN, том 2491 из CEUR Workshop Proceedings, CEUR-WS.org, (2019)Discovering Symbolic Models from Deep Learning with Inductive Biases., , , , , , и . NeurIPS, (2020)The frontier of simulation-based inference, , и . (2019)Unifying generative models and exact likelihood-free inference with conditional bijections, и . (2016)Machine learning and the physical sciences, , , , , , , и . (2019)cite arxiv:1903.10563.Normalizing Flows on Tori and Spheres., , , , , , и . ICML, том 119 из Proceedings of Machine Learning Research, стр. 8083-8092. PMLR, (2020)Etalumis: bringing probabilistic programming to scientific simulators at scale., , , , , , , , , и 7 other автор(ы). SC, стр. 29:1-29:24. ACM, (2019)