• Corpus ID: 4622742

Neural Autoregressive Flows

  title={Neural Autoregressive Flows},
  author={Chin-Wei Huang and David Krueger and Alexandre Lacoste and Aaron C. Courville},
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. [] Key Result 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.
Block Neural Autoregressive Flow
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Transformation Autoregressive Networks
This work attempts to systematically characterize methods for density estimation, and proposes multiple novel methods to model non-Markovian dependencies, and introduces a novel data driven framework for learning a family of distributions.
Masked Autoregressive Flow for Density Estimation
This work describes an approach for increasing the flexibility of an autoregressive model, based on modelling the random numbers that the model uses internally when generating data, which is called Masked Autoregressive Flow.
MADE: Masked Autoencoder for Distribution Estimation
This work introduces a simple modification for autoencoder neural networks that yields powerful generative models and proves that this approach is competitive with state-of-the-art tractable distribution estimators.
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