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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This study investigates the effects of Markov chain Monte Carlo (MCMC)sampling in unsupervised Maximum Likelihood (ML) learning. Our attention isrestricted to the family of unnormalized probability densities for which thenegative log density (or energy function) is a ConvNet. We find that many ofthe techniques used to stabilize training in previous studies are notnecessary. ML learning with a ConvNet potential requires only a fewhyper-parameters and no regularization. Using this minimal framework, weidentify a variety of ML learning outcomes that depend solely on theimplementation of MCMC sampling.  On one hand, we show that it is easy to train an energy-based model which cansample realistic images with short-run Langevin. ML can be effective and stableeven when MCMC samples have much higher energy than true steady-state samplesthroughout training. Based on this insight, we introduce an ML method withpurely noise-initialized MCMC, high-quality short-run synthesis, and the samebudget as ML with informative MCMC initialization such as CD or PCD. Unlikeprevious models, our energy model can obtain realistic high-diversity samplesfrom a noise signal after training.  On the other hand, ConvNet potentials learned with non-convergent MCMC do nothave a valid steady-state and cannot be considered approximate unnormalizeddensities of the training data because long-run MCMC samples differ greatlyfrom observed images. We show that it is much harder to train a ConvNetpotential to learn a steady-state over realistic images. To our knowledge,long-run MCMC samples of all previous models lose the realism of short-runsamples. With correct tuning of Langevin noise, we train the first ConvNetpotentials for which long-run and steady-state MCMC samples are realisticimages.

On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models