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Sampling from known probability distributions is a ubiquitous task in computational science, underlying calculations in domains from linguistics to biology and physics.
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SE(3) equivariant graph neural networks with complete local frames
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Path-gradient estimators for continuous normalizing flows
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Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions
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Learning Lattice Quantum Field Theories with Equivariant Continuous Flows
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Flow-based sampling in the lattice Schwinger model at criticality
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Aspects of scaling and scalability for flow-based sampling of lattice QCD
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Gauge-equivariant pooling layers for preconditioners in lattice QCD
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Sampling QCD field configurations with gauge-equivariant flow models
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Learning trivializing gradient flows for lattice gauge theories
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