A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
Variational Generative Stochastic Networks with Collaborative Shaping
2015
International Conference on Machine Learning
We develop an approach to training generative models based on unrolling a variational autoencoder into a Markov chain, and shaping the chain's trajectories using a technique inspired by recent work in Approximate Bayesian computation. We show that the global minimizer of the resulting objective is achieved when the generative model reproduces the target distribution. To allow finer control over the behavior of the models, we add a regularization term inspired by techniques used for regularizing
dblp:conf/icml/BachmanP15
fatcat:pyq33sdwdncilo7k5msdn7da5a