ICML 2018oral100 citations
Transformation Autoregressive Networks
Junier Oliva, Avinava Dubey, Manzil Zaheer, Barnabas Poczos, Ruslan Salakhutdinov, Eric Xing, Jeff Schneider
Abstract
The fundamental task of general density estimation $p(x)$ has been of keen interest to machine learning. In this work, we attempt to systematically characterize methods for density estimation. Broadly speaking, most of the existing methods can be categorized into either using:
BibTeX
@InProceedings{pmlr-v80-oliva18a,
title = {Transformation Autoregressive Networks},
author = {Oliva, Junier and Dubey, Avinava and Zaheer, Manzil and Poczos, Barnabas and Salakhutdinov, Ruslan and Xing, Eric and Schneider, Jeff},
booktitle = {Proceedings of the 35th International Conference on Machine Learning},
pages = {3898--3907},
year = {2018},
editor = {Dy, Jennifer and Krause, Andreas},
volume = {80},
series = {Proceedings of Machine Learning Research},
month = {10--15 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v80/oliva18a/oliva18a.pdf},
url = {https://proceedings.mlr.press/v80/oliva18a.html},
abstract = {The fundamental task of general density estimation $p(x)$ has been of keen interest to machine learning. In this work, we attempt to systematically characterize methods for density estimation. Broadly speaking, most of the existing methods can be categorized into either using: