{"type":"ScholarlyArticle","title":"What kind of graphical model is the brain?","authors":[{"name":"Geoffrey E. Hinton","slug":"geoffrey-e-hinton","given":"Geoffrey E.","family":"Hinton","affiliation":""}],"abstract":"If neurons are treated as latent variables, our visual systems are non-linear, densely-connected graphical models containing billions of variables and thousands of billions of parameters. Current algorithms would have difficulty learning a graphical model of this scale. Starting with an algorithm that has difficulty learning more than a few thousand parameters, I describe a series of progressively better learning algorithms all of which are designed to run on neuron-like hardware. The latest member of this series can learn deep, multi-layer belief nets quite rapidly. It turns a generic network with three hidden layers and 1.7 million connections into a very good generative model of handwritten digits. After learning, the model gives classification performance that is comparable to the best discriminative methods.","doi":null,"url":"https://ijcai.org/proceedings/2005/238","pdf":"https://ijcai.org/proceedings/2005/0238.pdf","markdown":"https://ijcai.org/proceedings/2005/238.md","bibtex":"https://ijcai.org/proceedings/2005/bibtex/238","volume":{"slug":"2005","title":"Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2005","url":"https://ijcai.org/proceedings/2005/","year":2005,"location":"Edinburgh, Scotland, UK","dates":"","editors":"","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":238,"pages":{"first":1765,"last":-1},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}