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Transformation Autoregressive Networks
Junier Oliva, Avinava Dubey, Barnabás Póczos, Eric P. Xing, Jeff Schneider
Feb 15, 2018 (modified: Feb 15, 2018)ICLR 2018 Conference Blind Submissionreaders: everyoneShow 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.