Explicit constructions approximate diffeomorphisms and pushforward measures via continuity equation flows with perceptron velocity fields of piecewise constant weights, using polar-like decompositions and probabilistic methods for regular maps.
A friendly introduction to triangular transport
2 Pith papers cite this work. Polarity classification is still indexing.
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FLUID uses a recurrent encoder to create a fixed-size summary of observations, then learns coupled forward and backward flows to approximate filtering distributions and recover smoothing paths for nonlinear dynamics, with support for extrapolation.
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Constructive conditional normalizing flows
Explicit constructions approximate diffeomorphisms and pushforward measures via continuity equation flows with perceptron velocity fields of piecewise constant weights, using polar-like decompositions and probabilistic methods for regular maps.
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FLUID: Flow-based Unified Inference for Dynamics
FLUID uses a recurrent encoder to create a fixed-size summary of observations, then learns coupled forward and backward flows to approximate filtering distributions and recover smoothing paths for nonlinear dynamics, with support for extrapolation.