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Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows

, , , и . (2021)cite arxiv:2110.02673Comment: 8 pages, 5 figures. Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021).

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Nathalie de Haan

Martha Lanwer-de Haan

Pim Blomaard

Pim Schravendijk

Gerhard Haan

 

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Natural Graph Networks., , и . NeurIPS, (2020)Rigid Body Flows for Sampling Molecular Crystal Structures., , , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 17301-17326. PMLR, (2023)Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs., , , и . CoRR, (2020)Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows, , , и . (2021)cite arxiv:2110.02673Comment: 8 pages, 5 figures. Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021).Covariance in Physics and Convolutional Neural Networks, , , , , и . (2019)cite arxiv:1906.02481.Mesh Convolutional Neural Networks for Wall Shear Stress Estimation in 3D Artery Models., , , , и . STACOM@MICCAI, том 13131 из Lecture Notes in Computer Science, стр. 93-102. Springer, (2021)Causal Confusion in Imitation Learning., , и . NeurIPS, стр. 11693-11704. (2019)Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers., , и . AISTATS, том 238 из Proceedings of Machine Learning Research, стр. 3088-3096. PMLR, (2024)Learning Lattice Quantum Field Theories with Equivariant Continuous Flows., , , , и . CoRR, (2022)Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers., , и . CoRR, (2023)