{"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/diversity-networks-neural-network-compression","title":"Diversity Networks: Neural Network Compression Using Determinantal Point Processes","arxiv_id":"1511.05077","date":"2015-11-16","proceeding":null,"authors":["Zelda Mariet","Suvrit Sra"],"abstract":"We introduce Divnet, a flexible technique for learning networks with diverse\nneurons. Divnet models neuronal diversity by placing a Determinantal Point\nProcess (DPP) over neurons in a given layer. It uses this DPP to select a\nsubset of diverse neurons and subsequently fuses the redundant neurons into the\nselected ones. Compared with previous approaches, Divnet offers a more\nprincipled, flexible technique for capturing neuronal diversity and thus\nimplicitly enforcing regularization. This enables effective auto-tuning of\nnetwork architecture and leads to smaller network sizes without hurting\nperformance. Moreover, through its focus on diversity and neuron fusing, Divnet\nremains compatible with other procedures that seek to reduce memory footprints\nof networks. We present experimental results to corroborate our claims: for\npruning neural networks, Divnet is seen to be notably superior to competing\napproaches.","url_abs":"http://arxiv.org/abs/1511.05077v6","url_pdf":"http://arxiv.org/pdf/1511.05077v6.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":"diversity-networks-neural-network-compression","repo_url":"https://github.com/mcordier/Papers-Presentation-ML-Reading-Group-","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"diversity-networks-neural-network-compression","repo_url":"https://github.com/nobug-code/Diversity_Networks_CNN_Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"neural-network-compression","task_name":"Neural Network Compression"},{"task_slug":"point-processes","task_name":"Point Processes"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1511.05077","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}