{"id":47155,"date":"2023-12-20T15:08:34","date_gmt":"2023-12-20T20:08:34","guid":{"rendered":"https:\/\/engineering.jhu.edu\/ams\/?post_type=tribe_events&#038;p=47155"},"modified":"2024-03-25T09:17:29","modified_gmt":"2024-03-25T13:17:29","slug":"ams-weekly-seminar-professor-jared-tanner","status":"publish","type":"tribe_events","link":"https:\/\/engineering.jhu.edu\/ams\/event\/ams-weekly-seminar-professor-jared-tanner\/","title":{"rendered":"AMS Weekly Seminar | Professor Jared Tanner"},"content":{"rendered":"<p><strong>Location:\u00a0<\/strong>Olin 305<\/p>\n<p><strong>When: <\/strong>March 28th\u00a0at 1:30 p.m.<\/p>\n<p><strong>Title:<\/strong> Deep neural network stability at initialization: Nonlinear activations impact on the Gaussian process<\/p>\n<p><strong>Abstract:<\/strong> Randomly initialised deep neural networks are known to generate a Gaussian process for their pre-activation intermediate layers.\u00a0 We will review this line of research with extensions to deep networks having structured random entries such as block-sparse or low-rank weight matrices.\u00a0 We will then discuss how the choice of nonlinear activations impacts the evolution of the Gaussian process.\u00a0 Specifically we will discuss why sparsifying nonlinear activations such as soft thresholding are unstable, we will show conditions to overcome such issues, and we will show how non-sparsifying activations can be improved to be more stable when acting on a data manifold.<\/p>\n<p><span><strong>Bio:<\/strong> Prof. Jared Tanner is the Professor of the Mathematics of Information at the University of Oxford Mathematical Institute where he leads the Machine Learning and Data Science Research Group.\u00a0 His research focus is on the design, analysis, and application of algorithms for the processing of information.\u00a0 His current focus is understanding how to improve the stability and computational efficiency of deep networks.\u00a0 This includes both theory and experimental work on the design of network activations and weight structures.\u00a0 He is currently applying these techniques in medical imaging, multispectral sensing, and hardware aware deep learning algorithms.\u00a0 Previous research contributions include theory, algorithms, and applications of compressed sensing, matrix completion, low-rank plus sparse models, and grid free super-resolution.<\/span><\/p>\n<p><strong>Zoom link: <\/strong>https:\/\/wse.zoom.us\/j\/94601022340<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Location:\u00a0Olin 305 When: March 28th\u00a0at 1:30 p.m. Title: Deep neural network stability at initialization: Nonlinear activations impact on the Gaussian process Abstract: Randomly initialised deep neural networks are known to&hellip;<\/p>\n","protected":false},"author":69,"featured_media":0,"template":"","meta":{"_acf_changed":false,"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[260],"class_list":["post-47155","tribe_events","type-tribe_events","status-publish","hentry","tribe_events_cat-seminars-and-endowed-lectures","cat_seminars-and-endowed-lectures"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AMS Weekly Seminar | Professor Jared Tanner | Department of 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