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Neural Autoregressive Flows

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arxiv 1804.00779 v1 pith:EY4SPFY7 submitted 2018-04-03 cs.LG stat.ML

Chin-Wei Huang , David Krueger , Alexandre Lacoste , Aaron Courville This is my paper · ORCID
classification cs.LGstat.ML
keywords autoregressiveflowsneuralstate-of-the-artdensitydistributionsestimationtransformations
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Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, via Masked Autoregressive Flows (MAF), and to accelerate state-of-the-art WaveNet-based speech synthesis to 20x faster than real-time, via Inverse Autoregressive Flows (IAF). We unify and generalize these approaches, replacing the (conditionally) affine univariate transformations of MAF/IAF with a more general class of invertible univariate transformations expressed as monotonic neural networks. We demonstrate that the proposed neural autoregressive flows (NAF) are universal approximators for continuous probability distributions, and their greater expressivity allows them to better capture multimodal target distributions. Experimentally, NAF yields state-of-the-art performance on a suite of density estimation tasks and outperforms IAF in variational autoencoders trained on binarized MNIST.

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Forward citations

Cited by 4 Pith papers

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    Three new analytic bijections and a radial flow architecture give globally smooth, closed-form invertible normalizing flows that match or beat spline baselines on benchmarks and improve phi^4 lattice-field sampling.

  3. Copula & Marginal Flows: Disentangling the Marginal from its Joint

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    CM flows disentangle marginals from joints in normalizing flows to enable exact tail asymptotics and prior CDF assumptions via copula separation.

  4. Revisiting the Exo-Mercury Candidate GJ 367 b with ESPRESSO and a Self-Consistent Tidal Distortion Model

    astro-ph.EP 2026-06 unverdicted novelty 4.0 of 10

    Revised mass of 0.503 M_Earth and radius of 0.736 R_Earth for GJ 367 b give a density of 6.9 g cm^{-3} and an iron fraction of 50-70% via new tidal and composition modeling.

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