[drams]

To clarify: in the case of a BEAM both the generator and all but the top layer of the discriminator is replaced with an RBM. The adversary in this case operates on features encoded by the RBM, not raw data samples. Secondly the RBM is trained with a combined loss involving log-likelihood and the adversarial term.

[cs702]

Yes. For simplicity's and brevity's sake, I ignored many important details in my summary.

[drams]

No worries! :)

[cs702]

Thanks. Have you made any code available online?

[drams]

Yes; The following recent review article actually provides code samples: https://arxiv.org/abs/1803.08823 which use an open-source version of our software called 'paysage' (https://github.com/drckf/paysage). This has currently not been updated too recently, but we expect to put out a new update quite soon. The update will clean up code, docs, features, but might not yet contain the BEAM training code. The latter is pending some decisions about IP, etc.

[cs702]

Thank you. I'll take a look!