Transformation Autoregressive Networks

Anonymous

Nov 03, 2017 (modified: Dec 08, 2017) ICLR 2018 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: The fundamental task of general density estimation has been of keen interest to machine learning. Recent advances in density estimation have either: a) proposed using a flexible model to estimate the conditional factors of the chain rule; or b) used flexible, non-linear transformations of variables of a simple base distribution. Instead, this work jointly leverages transformations of variables and autoregressive conditional models, and proposes novel methods for both. We provide a deeper understanding of our models, showing a considerable improvement with our methods through a comprehensive study over both real world and synthetic data. Moreover, we illustrate the use of our models in outlier detection and image modeling task.
  • Keywords: density estimation, autoregressive models, RNNs

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