{"id":4279,"date":"2025-06-02T07:02:30","date_gmt":"2025-06-02T07:02:30","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/06\/02\/2505-23869\/"},"modified":"2025-06-02T07:02:30","modified_gmt":"2025-06-02T07:02:30","slug":"2505-23869","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/06\/02\/2505-23869\/","title":{"rendered":"Gibbs randomness-compression proposition: An efficient deep learning"},"content":{"rendered":"<p>    Gibbs randomness-compression proposition: An efficient deep learning<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>arXiv:2505.23869v1 Announce Type: new<br \/>\nAbstract: A proposition that connects randomness and compression put forward via Gibbs entropy over set of measurement vectors associated with a compression process. The proposition states that a lossy compression process is equivalent to {it directed randomness} that preserves information content. The proposition originated from the observed behaviour in newly proposed {it Dual Tomographic Compression} (DTC) compress-train framework. This is akin to tomographic reconstruction of layer weight matrices via building compressed sensed projections, so called {it weight rays}. This tomographic approach is applied to previous and next layers in a dual fashion, that triggers neuronal-level pruning. This novel model compress-train scheme appear in iterative fashion and act as smart neural architecture search, Experiments demonstrated utility of this dual-tomography producing state-of-the-art performance with efficient compression during training, accelerating and supporting lottery ticket hypothesis. However, random compress-train iterations having similar performance demonstrated the connection between randomness and compression from statistical physics perspective, we formulated so called {it Gibbs randomness-compression proposition}, signifying randomness-compression relationship via Gibbs entropy. Practically, DTC framework provides a promising approach for massively energy and resource efficient deep learning training approach.<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    M. S&quot;uzen<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/arxiv.org\/abs\/2505.23869\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Gibbs randomness-compression proposition: An efficient deep learning arXiv:2505.23869v1 Announce Type: new Abstract: A proposition that connects randomness and compression put forward via Gibbs entropy over set of measurement vectors associated with a compression process. The proposition states that a lossy compression process is equivalent to {it directed randomness} that preserves information content. The proposition originated [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,113,112],"tags":[2831,2833,2832],"class_list":["post-4279","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-lg","category-stat-ml","tag-compression","tag-proposition","tag-randomness"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/4279"}],"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=4279"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/4279\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=4279"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=4279"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=4279"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}