Authors
Shixiang Gu, Sergey Levine, Ilya Sutskever, Andriy Mnih
Publication date
2015/11/16
Journal
arXiv preprint arXiv:1511.05176
Description
Abstract: Deep neural networks are powerful parametric models that can be trained
efficiently using the backpropagation algorithm. Stochastic neural networks combine the
power of large parametric functions with that of graphical models, which makes it possible to
learn very complex distributions. However, as backpropagation is not directly applicable to
stochastic networks that include discrete sampling operations within their computational
graph, training such networks remains difficult. We present MuProp, an unbiased gradient ...
Total citations
20163
Scholar articles
S Gu, S Levine, I Sutskever, A Mnih - arXiv preprint arXiv:1511.05176, 2015