{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/boltzmann-encoded-adversarial-machines","title":"Boltzmann Encoded Adversarial Machines","arxiv_id":"1804.08682","date":"2018-04-23","proceeding":null,"authors":["Charles K. Fisher","Aaron M. Smith","Jonathan R. Walsh"],"abstract":"Restricted Boltzmann Machines (RBMs) are a class of generative neural network\nthat are typically trained to maximize a log-likelihood objective function. We\nargue that likelihood-based training strategies may fail because the objective\ndoes not sufficiently penalize models that place a high probability in regions\nwhere the training data distribution has low probability. To overcome this\nproblem, we introduce Boltzmann Encoded Adversarial Machines (BEAMs). A BEAM is\nan RBM trained against an adversary that uses the hidden layer activations of\nthe RBM to discriminate between the training data and the probability\ndistribution generated by the model. We present experiments demonstrating that\nBEAMs outperform RBMs and GANs on multiple benchmarks.","url_abs":"http://arxiv.org/abs/1804.08682v1","url_pdf":"http://arxiv.org/pdf/1804.08682v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"boltzmann-encoded-adversarial-machines","repo_url":"https://github.com/annachen/beam","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}