ICML 2018oral64 citations
Continuous-Time Flows for Efficient Inference and Density Estimation
Changyou Chen, Chunyuan Li, Liqun Chen, Wenlin Wang, Yunchen Pu, Lawrence Carin Duke
Abstract
Two fundamental problems in unsupervised learning are efficient inference for latent-variable models and robust density estimation based on large amounts of unlabeled data. Algorithms for the two tasks, such as normalizing flows and generative adversarial networks (GANs), are often developed independently. In this paper, we propose the concept of
BibTeX
@InProceedings{pmlr-v80-chen18d,
title = {Continuous-Time Flows for Efficient Inference and Density Estimation},
author = {Chen, Changyou and Li, Chunyuan and Chen, Liqun and Wang, Wenlin and Pu, Yunchen and Duke, Lawrence Carin},
booktitle = {Proceedings of the 35th International Conference on Machine Learning},
pages = {824--833},
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/chen18d/chen18d.pdf},
url = {https://proceedings.mlr.press/v80/chen18d.html},
abstract = {Two fundamental problems in unsupervised learning are efficient inference for latent-variable models and robust density estimation based on large amounts of unlabeled data. Algorithms for the two tasks, such as normalizing flows and generative adversarial networks (GANs), are often developed independently. In this paper, we propose the concept of