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Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows, , , and . (2021)cite arxiv:2110.02673Comment: 8 pages, 5 figures. Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021).Neural Simulated Annealing., , and . AISTATS, volume 206 of Proceedings of Machine Learning Research, page 4946-4962. PMLR, (2023)Bayesian Optimization for Macro Placement., , , , , , , , and . CoRR, (2022)Accurate Learning of Equivariant Quantum Systems from a Single Ground State., and . CoRR, (2024)Probabilistic Numeric Convolutional Neural Networks., , and . ICLR, OpenReview.net, (2021)Robust Scheduling with GFlowNets., , , and . ICLR, OpenReview.net, (2023)Learning Lattice Quantum Field Theories with Equivariant Continuous Flows., , , , and . CoRR, (2022)Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel., , , and . NeurIPS, (2022)Learning Symmetries of Classical Integrable Systems, and . (2019)cite arxiv:1906.04645Comment: 8 pages, 5 figures. Presented at the ICML 2019 Workshop on Theoretical Physics for Deep Learning.Deterministic Gibbs Sampling via Ordinary Differential Equations., , and . CoRR, (2021)