{"id":666,"date":"2019-01-01T12:00:00","date_gmt":"2019-01-01T12:00:00","guid":{"rendered":"https:\/\/michaelkamp.org\/?p=666"},"modified":"2019-01-01T12:00:00","modified_gmt":"2019-01-01T12:00:00","slug":"a-reparameterization-invariant-flatness-measure-for-deep-neural-networks-2","status":"publish","type":"post","link":"https:\/\/michaelkamp.org\/?p=666","title":{"rendered":"A Reparameterization-Invariant Flatness Measure for Deep Neural Networks"},"content":{"rendered":"<p><div class=\"tp_single_publication\"><span class=\"tp_single_author\">Henning Petzka, Linara Adilova, Michael Kamp, Cristian Sminchisescu: <\/span> <span class=\"tp_single_title\">A Reparameterization-Invariant Flatness Measure for Deep Neural Networks<\/span>. <span class=\"tp_single_additional\"><span class=\"tp_pub_additional_booktitle\">Science meets Engineering of Deep Learning workshop at NeurIPS, <\/span><span class=\"tp_pub_additional_year\">2019<\/span>.<\/span><\/div><!--more--><\/p>\n<h2 class=\"tp_abstract\">Abstract<\/h2><p class=\"tp_abstract\"><\/p>\n<h2 class=\"tp_links\">Links<\/h2><p class=\"tp_abstract\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-arxiv\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/arxiv.org\/pdf\/1912.00058\" title=\"https:\/\/arxiv.org\/pdf\/1912.00058\" target=\"_blank\">https:\/\/arxiv.org\/pdf\/1912.00058<\/a><\/li><\/ul><\/p>\n<h2 class=\"tp_bibtex\">BibTeX (<a href=\"https:\/\/michaelkamp.org?feed=tp_pub_bibtex&amp;key=petzka2019reparameterization\">Download<\/a>)<\/h2><pre class=\"tp_bibtex\">@workshop{petzka2019reparameterization,\r\ntitle = {A Reparameterization-Invariant Flatness Measure for Deep Neural Networks},\r\nauthor = {Henning Petzka and Linara Adilova and Michael Kamp and Cristian Sminchisescu},\r\nurl = {https:\/\/arxiv.org\/pdf\/1912.00058},\r\nyear  = {2019},\r\ndate = {2019-01-01},\r\nurldate = {2019-01-01},\r\nbooktitle = {Science meets Engineering of Deep Learning workshop at NeurIPS},\r\nkeywords = {deep learning, flatness, generalization, learning theory, loss surface, neural networks, robustness},\r\npubstate = {published},\r\ntppubtype = {workshop}\r\n}\r\n<\/pre>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-666","post","type-post","status-publish","format-standard","hentry","category-publications"],"_links":{"self":[{"href":"https:\/\/michaelkamp.org\/index.php?rest_route=\/wp\/v2\/posts\/666","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/michaelkamp.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/michaelkamp.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/michaelkamp.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/michaelkamp.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=666"}],"version-history":[{"count":0,"href":"https:\/\/michaelkamp.org\/index.php?rest_route=\/wp\/v2\/posts\/666\/revisions"}],"wp:attachment":[{"href":"https:\/\/michaelkamp.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=666"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/michaelkamp.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=666"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/michaelkamp.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=666"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}