{"id":190055,"date":"2013-10-10T00:00:00","date_gmt":"2013-10-24T09:09:45","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/explorations-in-probabilistic-programming-generative-probabilistic-graphics-programming-and-new-research-directions\/"},"modified":"2016-08-10T07:18:35","modified_gmt":"2016-08-10T14:18:35","slug":"explorations-in-probabilistic-programming-generative-probabilistic-graphics-programming-and-new-research-directions","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/explorations-in-probabilistic-programming-generative-probabilistic-graphics-programming-and-new-research-directions\/","title":{"rendered":"Explorations in Probabilistic Programming: Generative Probabilistic Graphics Programming and New Research Directions"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Probabilistic programming has recently attracted much attention in Computer Science and Machine Learning communities. I will demonstrate two generative probabilistic graphics programs (models), which I contributed to develop. The first can read text from simple CAPTCHAs, and the second can find roads from real-world images. Both work by performing approximate inference over the executions of simple renderers, using the general-purpose Metropolis-Hastings inference engine built into a probabilistic programming system. I will briefly touch on two other research directions I am interested in pursuing: a path to scaling up general-purpose approximate inference in probabilistic programs using parallelism, based on preliminary work on a multithreaded approximate MCMC scheme for Church-like languages, and a much longer-term path to automatic programming via general-purpose approximate inference.<\/p>\n<p>This is based on joint work with Vikash Mansinghka, Tejas Kulkarni, and Joshua Tenenbaum, especially &#8220;Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs&#8221;, http:\/\/arxiv.org\/abs\/1307.0060<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Probabilistic programming has recently attracted much attention in Computer Science and Machine Learning communities. I will demonstrate two generative probabilistic graphics programs (models), which I contributed to develop. The first can read text from simple CAPTCHAs, and the second can find roads from real-world images. Both work by performing approximate inference over the executions of [&hellip;]<\/p>\n","protected":false},"featured_media":197948,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_hide_image_in_river":0,"footnotes":""},"research-area":[],"msr-video-type":[],"msr-locale":[268875],"msr-post-option":[],"msr-session-type":[],"msr-impact-theme":[],"msr-pillar":[],"msr-episode":[],"msr-research-theme":[],"class_list":["post-190055","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/UHOLC2FaLF8","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/190055","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-video"}],"version-history":[{"count":0,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/190055\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/197948"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=190055"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=190055"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=190055"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=190055"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=190055"},{"taxonomy":"msr-session-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-session-type?post=190055"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=190055"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=190055"},{"taxonomy":"msr-episode","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-episode?post=190055"},{"taxonomy":"msr-research-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-theme?post=190055"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}