{"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/gradient-target-propagation","title":"Gradient target propagation","arxiv_id":"1810.09284","date":"2018-10-19","proceeding":null,"authors":["Tiago de Souza Farias","Jonas Maziero"],"abstract":"We report a learning rule for neural networks that computes how much each\nneuron should contribute to minimize a giving cost function via the estimation\nof its target value. By theoretical analysis, we show that this learning rule\ncontains backpropagation, Hebian learning, and additional terms. We also give a\ngeneral technique for weights initialization. Our results are at least as good\nas those obtained with backpropagation. The neural networks are trained and\ntested in three problems: MNIST, MNIST-Fashion, and CIFAR-10 datasets. The\nassociated code is available at https://github.com/tiago939/target.","url_abs":"http://arxiv.org/abs/1810.09284v3","url_pdf":"http://arxiv.org/pdf/1810.09284v3.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":"gradient-target-propagation","repo_url":"https://github.com/tiago939/target","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}