{"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/lightnet-a-versatile-standalone-matlab-based","title":"LightNet: A Versatile, Standalone Matlab-based Environment for Deep Learning","arxiv_id":"1605.02766","date":"2016-05-09","proceeding":null,"authors":["Chengxi Ye","Chen Zhao","Yezhou Yang","Cornelia Fermuller","Yiannis Aloimonos"],"abstract":"LightNet is a lightweight, versatile and purely Matlab-based deep learning\nframework. The idea underlying its design is to provide an easy-to-understand,\neasy-to-use and efficient computational platform for deep learning research.\nThe implemented framework supports major deep learning architectures such as\nMultilayer Perceptron Networks (MLP), Convolutional Neural Networks (CNN) and\nRecurrent Neural Networks (RNN). The framework also supports both CPU and GPU\ncomputation, and the switch between them is straightforward. Different\napplications in computer vision, natural language processing and robotics are\ndemonstrated as experiments.","url_abs":"http://arxiv.org/abs/1605.02766v3","url_pdf":"http://arxiv.org/pdf/1605.02766v3.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":"lightnet-a-versatile-standalone-matlab-based","repo_url":"https://github.com/yechengxi/LightNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":null,"task_name":"GPU"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}