{"id":7538,"date":"2025-10-13T07:02:34","date_gmt":"2025-10-13T07:02:34","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/10\/13\/2510-09177\/"},"modified":"2025-10-13T07:02:34","modified_gmt":"2025-10-13T07:02:34","slug":"2510-09177","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/10\/13\/2510-09177\/","title":{"rendered":"Distributionally robust approximation property of neural networks"},"content":{"rendered":"<p>    Distributionally robust approximation property of neural networks<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>arXiv:2510.09177v1 Announce Type: new<br \/>\nAbstract: The universal approximation property uniformly with respect to weakly compact families of measures is established for several classes of neural networks. To that end, we prove that these neural networks are dense in Orlicz spaces, thereby extending classical universal approximation theorems even beyond the traditional $L^p$-setting. The covered classes of neural networks include widely used architectures like feedforward neural networks with non-polynomial activation functions, deep narrow networks with ReLU activation functions and functional input neural networks.<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    Mihriban Ceylan, David J. Pr&quot;omel<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/arxiv.org\/abs\/2510.09177\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Distributionally robust approximation property of neural networks arXiv:2510.09177v1 Announce Type: new Abstract: The universal approximation property uniformly with respect to weakly compact families of measures is established for several classes of neural networks. To that end, we prove that these neural networks are dense in Orlicz spaces, thereby extending classical universal approximation theorems even beyond [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,113,1172,420,112],"tags":[1660,491,118],"class_list":["post-7538","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-lg","category-math-fa","category-math-pr","category-stat-ml","tag-approximation","tag-networks","tag-neural"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/7538"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=7538"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/7538\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=7538"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=7538"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=7538"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}