{"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/nonlinear-acceleration-of-cnns","title":"Nonlinear Acceleration of CNNs","arxiv_id":"1806.00370","date":"2018-06-01","proceeding":null,"authors":["Damien Scieur","Edouard Oyallon","Alexandre d'Aspremont","Francis Bach"],"abstract":"The Regularized Nonlinear Acceleration (RNA) algorithm is an acceleration\nmethod capable of improving the rate of convergence of many optimization\nschemes such as gradient descend, SAGA or SVRG. Until now, its analysis is\nlimited to convex problems, but empirical observations shows that RNA may be\nextended to wider settings. In this paper, we investigate further the benefits\nof RNA when applied to neural networks, in particular for the task of image\nrecognition on CIFAR10 and ImageNet. With very few modifications of exiting\nframeworks, RNA improves slightly the optimization process of CNNs, after\ntraining.","url_abs":"http://arxiv.org/abs/1806.00370v1","url_pdf":"http://arxiv.org/pdf/1806.00370v1.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":"nonlinear-acceleration-of-cnns","repo_url":"https://github.com/windows7lover/RegularizedNonlinearAcceleration","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[],"methods":[{"method_slug":"saga","method_name":"SAGA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1806.00370","atlas_url":"https://app.syntology.ai/?focus=1806.00370","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.00370"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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