Iterative Refinement of the Approximate Posterior for Directed Belief Networks

NIPS, pp. 4691-4699, 2016.

Cited by: 0|Bibtex|Views47
EI
Other Links: dblp.uni-trier.de|academic.microsoft.com

Abstract:

Variational methods that rely on a recognition network to approximate the posterior of directed graphical models offer better inference and learning than previous methods. Recent advances that exploit the capacity and flexibility in this approach have expanded what kinds of models can be trained. However, as a proposal for the posterior, ...More

Code:

Data:

Your rating :
0

 

Tags
Comments