[drams]

I can try. (I am a coauthor of this paper) First off, Unlearn.ai is a startup working to build new tools that make precision medicine a reality. We needed to be able to build generative models which allow us to 1. model multimodal data easily (consider medical datasets with categorical data, binary, and continuous, with various bounds etc. all mixed together) 2. be able to answer counterfactual questions about data (for example if I down regulate a gene how does this effect the rest of the gene expression?) 3. be able to build models which handle time-series data (give me a likely progression of this person's cognitive scores given their current scores and other indicators)

RBMs are natural candidates for models which handle these kind of issues quite well. 1. Although people have done work trying to get GANs to work well with multimodal data, it's pretty kludgy. 2. GANs do not provide a means of inference (contrast VAEs which can satisfy this demand). 3. We have built a solid extension of RBMs to temporal models which work quite well.

However, as explained in this paper, stock RBMs have significant training issues. This paper attempts to improve the situation.

[tlarkworthy]

RBMs have a native probabilistic output (the output is a distribution you can slice), but vanilla neural networks don't (the output is a vector). Is that right?

[drams]

It's best to say that an RBM is an undirected NN which models a probability distribution of some variables. You can sample from the distribution (which is a stochastic process). There are other NN models which use feed-forward NNs to do similarly --such as GANs and VAEs and others. The generation process is also stochastic, but the difference is that you sample a noise distribution and then feed that through the NN. In all cases the generated samples are still vectors.