{"id":543690,"date":"2018-10-17T21:50:00","date_gmt":"2018-10-18T04:50:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=543690"},"modified":"2018-10-23T13:52:09","modified_gmt":"2018-10-23T20:52:09","slug":"natasha-2-faster-non-convex-optimization-than-sgd","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/natasha-2-faster-non-convex-optimization-than-sgd\/","title":{"rendered":"Natasha 2: Faster Non-Convex Optimization Than SGD"},"content":{"rendered":"

We design a stochastic algorithm to train any smooth neural network to \u03b5-approximate local minima, using O(\u03b5\u22123.25) backpropagations. The best result was essentially O(\u03b5\u22124) by SGD. More broadly, it finds \u03b5-approximate local minima of any smooth nonconvex function in rate O(\u03b5\u22123.25), with only oracle access to stochastic gradients.<\/p>\n","protected":false},"excerpt":{"rendered":"

We design a stochastic algorithm to train any smooth neural network to \u03b5-approximate local minima, using O(\u03b5\u22123.25) backpropagations. The best result was essentially O(\u03b5\u22124) by SGD. More broadly, it finds \u03b5-approximate local minima of any smooth nonconvex function in rate O(\u03b5\u22123.25), with only oracle access to stochastic gradients.<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13561,13556,13546],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-543690","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-algorithms","msr-research-area-artificial-intelligence","msr-research-area-computational-sciences-mathematics","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2018-12-2","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/arxiv.org\/abs\/1708.08694","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"user_nicename","value":"Zeyuan 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