{"id":44610,"date":"2020-10-01T00:49:47","date_gmt":"2020-10-01T04:49:47","guid":{"rendered":"https:\/\/www.opensource.im\/uncategorized\/differential-machine-learning-the-shape-of-things-to-come-risk-net.php"},"modified":"2020-10-01T00:49:47","modified_gmt":"2020-10-01T04:49:47","slug":"differential-machine-learning-the-shape-of-things-to-come-risk-net","status":"publish","type":"post","link":"https:\/\/euvolution.com\/open-source-convergence\/machine-learning\/differential-machine-learning-the-shape-of-things-to-come-risk-net.php","title":{"rendered":"Differential machine learning: the shape of things to come &#8211; Risk.net"},"content":{"rendered":"<p><p>CLICK HERE TO DOWNLOAD THE PDF<\/p>\n<p>Brian Huge and Antoine Savine combine automatic adjoint differentiation with modern machine learning. In addition, they introduce general machinery for training fast, accurate pricing and risk approximations, applicable to arbitrary transactions or trading books, and arbitrary stochastic models, effectively resolving the computational bottlenecks of derivatives risk reports and regulations<\/p>\n<p>Pricing approximation has proved tremendously useful with advanced<\/p>\n<p>Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content. <\/p>\n<p>To access these options, along with all other subscription benefits, please contact [emailprotected] or view our subscription options here: <a href=\"http:\/\/subscriptions.risk.net\/subscribe\" rel=\"nofollow\">http:\/\/subscriptions.risk.net\/subscribe<\/a><\/p>\n<p>You are currently unable to print this content. Please contact [emailprotected] to find out more.<\/p>\n<p>You are currently unable to copy this content. Please contact [emailprotected] to find out more.<\/p>\n<p><!-- Auto Generated --><\/p>\n<p>Read more:<br \/>\n<a target=\"_blank\" href=\"https:\/\/www.risk.net\/cutting-edge\/banking\/7688441\/differential-machine-learning-the-shape-of-things-to-come\" title=\"Differential machine learning: the shape of things to come - Risk.net\" rel=\"noopener noreferrer\">Differential machine learning: the shape of things to come - Risk.net<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p> CLICK HERE TO DOWNLOAD THE PDF Brian Huge and Antoine Savine combine automatic adjoint differentiation with modern machine learning. <\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[27373],"tags":[],"class_list":["post-44610","post","type-post","status-publish","format-standard","hentry","category-machine-learning"],"_links":{"self":[{"href":"https:\/\/euvolution.com\/open-source-convergence\/wp-json\/wp\/v2\/posts\/44610"}],"collection":[{"href":"https:\/\/euvolution.com\/open-source-convergence\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/euvolution.com\/open-source-convergence\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/euvolution.com\/open-source-convergence\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/euvolution.com\/open-source-convergence\/wp-json\/wp\/v2\/comments?post=44610"}],"version-history":[{"count":0,"href":"https:\/\/euvolution.com\/open-source-convergence\/wp-json\/wp\/v2\/posts\/44610\/revisions"}],"wp:attachment":[{"href":"https:\/\/euvolution.com\/open-source-convergence\/wp-json\/wp\/v2\/media?parent=44610"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/euvolution.com\/open-source-convergence\/wp-json\/wp\/v2\/categories?post=44610"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/euvolution.com\/open-source-convergence\/wp-json\/wp\/v2\/tags?post=44610"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}