{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/gibbs-randomness-compression-proposition-an","title":"Gibbs randomness-compression proposition: An efficient deep learning","arxiv_id":"2505.23869","date":"2025-05-29","proceeding":null,"authors":["M. Süzen"],"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 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.","url_abs":"https://arxiv.org/abs/2505.23869v1","url_pdf":"https://arxiv.org/pdf/2505.23869v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"gibbs-randomness-compression-proposition-an","repo_url":"https://github.com/msuzen/research/tree/main/gibbs-randomness-compression","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"architecture-search","task_name":"Neural Architecture Search"}],"methods":[{"method_slug":"set","method_name":"SET"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}