{"action":"closed","issue":{"url":"https://api.github.com/repos/pymc-devs/pymc3/issues/851","labels_url":"https://api.github.com/repos/pymc-devs/pymc3/issues/851/labels{/name}","comments_url":"https://api.github.com/repos/pymc-devs/pymc3/issues/851/comments","events_url":"https://api.github.com/repos/pymc-devs/pymc3/issues/851/events","html_url":"https://github.com/pymc-devs/pymc3/issues/851","id":110735286,"number":851,"title":"Blackbox Inference Workshop (Due today)","user":{"login":"jsalvatier","id":270513,"avatar_url":"https://avatars.githubusercontent.com/u/270513?v=3","gravatar_id":"","url":"https://api.github.com/users/jsalvatier","html_url":"https://github.com/jsalvatier","followers_url":"https://api.github.com/users/jsalvatier/followers","following_url":"https://api.github.com/users/jsalvatier/following{/other_user}","gists_url":"https://api.github.com/users/jsalvatier/gists{/gist_id}","starred_url":"https://api.github.com/users/jsalvatier/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/jsalvatier/subscriptions","organizations_url":"https://api.github.com/users/jsalvatier/orgs","repos_url":"https://api.github.com/users/jsalvatier/repos","events_url":"https://api.github.com/users/jsalvatier/events{/privacy}","received_events_url":"https://api.github.com/users/jsalvatier/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"milestone":null,"comments":2,"created_at":"2015-10-09T21:01:41Z","updated_at":"2015-10-12T16:09:26Z","closed_at":"2015-10-12T16:09:26Z","body":"@twiecki @fonnesbeck \r\n\r\nDid you guys see this workshop? \r\n\r\nThis workshop brings together developers of black box inference technologies, probabilistic programming systems, and connectionist computing frameworks. The goal is to formulate a shared understanding of how black box methods can enable advances in the design of intelligent learning systems. Topics of discussion will include:\r\n\r\nBlack box techniques for gradient ascent, variational inference, Markov chain- and sequential Monte Carlo.\r\nImplementation of black box techniques in probabilistic programming systems and computing frameworks for connectionist model families.\r\nModels that integrate top-down and bottom-up model representations to perform amortized inference: variational autoencoders, deep latent Gaussian models, restricted Boltzmann machines, neural network based proposals in MCMC.\r\nApplications to vision, speech, reinforcement learning, motor control, language learning.\r\n\r\nhttp://www.blackboxworkshop.org/"}}