Authors
Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio
Publication date
2016/5/27
Journal
arXiv preprint arXiv:1605.08803
Description
Abstract: Unsupervised learning of probabilistic models is a central yet challenging problem
in machine learning. Specifically, designing models with tractable learning, sampling,
inference and evaluation is crucial in solving this task. We extend the space of such models
using real-valued non-volume preserving (real NVP) transformations, a set of powerful
invertible and learnable transformations, resulting in an unsupervised learning algorithm
with exact log-likelihood computation, exact sampling, exact inference of latent variables, ...
Total citations
20162
Scholar articles
L Dinh, J Sohl-Dickstein, S Bengio - arXiv preprint arXiv:1605.08803, 2016