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Equivariant Hamiltonian Flows

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arxiv 1909.13739 v1 pith:X4B4WNSY submitted 2019-09-30 stat.ML cs.LG

Equivariant Hamiltonian Flows

classification stat.ML cs.LG
keywords equivariantflowsdatahamiltonianlearningrepresentationsymmetryaddition
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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This paper introduces equivariant hamiltonian flows, a method for learning expressive densities that are invariant with respect to a known Lie-algebra of local symmetry transformations while providing an equivariant representation of the data. We provide proof of principle demonstrations of how such flows can be learnt, as well as how the addition of symmetry invariance constraints can improve data efficiency and generalisation. Finally, we make connections to disentangled representation learning and show how this work relates to a recently proposed definition.

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