Transformation Autoregressive Networks
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The fundamental task of general density estimation has been of keen interest to machine learning. Recent advances in density estimation have either: a) proposed a flexible model to estimate the conditional factors of the chain rule, p(x_i | x_i-1, ...); 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 methods, showing a considerable improvement 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 tasks.
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