English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We address the problem of biased gradient estimation in deep Boltzmannmachines (DBMs). The existing method to obtain an unbiased estimator uses amaximal coupling based on a Gibbs sampler, but when the state ishigh-dimensional, it takes a long time to converge. In this study, we proposeto use a coupling based on the Metropolis-Hastings (MH) and to initialize thestate around a local mode of the target distribution. Because of the propensityof MH to reject proposals, the coupling tends to converge in only one step witha high probability, leading to high efficiency. We find that our method allowsDBMs to be trained in an end-to-end fashion without greedy pretraining. We alsopropose some practical techniques to further improve the performance of DBMs.We empirically demonstrate that our training algorithm enables DBMs to showcomparable generative performance to other deep generative models, achievingthe FID score of 10.33 for MNIST.

End-to-end Training of Deep Boltzmann Machines by Unbiased Contrastive Divergence with Local Mode Initialization