{"url":"/method/deep-belief-network","slug":"deep-belief-network","name":"Deep Belief Network","full_name":"Deep Belief Network","full_name_withheld":false,"description_markdown":"A **Deep Belief Network (DBN)** is a multi-layer generative graphical model. DBNs have bi-directional connections ([RBM](https://paperswithcode.com/method/restricted-boltzmann-machine)-type connections) on the top layer while the bottom layers only have top-down connections. They are trained using layerwise pre-training. Pre-training occurs by training the network component by component bottom up: treating the first two layers as an RBM and training, then treating the second layer and third layer as another RBM and training for those parameters.\r\n\r\nSource: [Origins of Deep Learning](https://arxiv.org/pdf/1702.07800.pdf)\r\n\r\nImage Source: [Wikipedia](https://en.wikipedia.org/wiki/Deep_belief_network)","description_state":"present","introduced_year":2009,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":null,"title":null,"url_on_a_paper_host":false},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Generative Models","url":"/methods/category/generative-models","pwc_aliases":[]}],"n_papers_tagged":71,"archive_num_papers":71,"papers_newest_first":[{"paper":null,"title":"A Novel Approach using CapsNet and Deep Belief 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