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X-WR-CALDESC:FAU DCN-AvH. Chair for Dynamics, Control, Machine Learning and Numerics -Alexander von Humboldt Professorship
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DTSTART;TZID=Europe/Berlin:20230120T110000
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SUMMARY:Consensus-based High Dimensional Global Non-convex Optimization in Machine Learning
DESCRIPTION:Next Friday, January 20, 2023:\nEvent: FAU DCN-AvH Seminar\nOrganized by: FAU DCN-AvH, Chair for Dynamics, Control and Numerics – Alexander von Humboldt Professorship at FAU, Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany)\nTitle: Consensus-based High Dimensional Global Non-convex Optimization in Machine Learning\nSpeaker: Prof. Dr. Shi Jin\nAffiliation: Shanghai Jiao Tong University, China\nAbstract. We introduce a stochastic interacting particle consensus system for global optimization of high dimensional non-convex functions.  This algorithm does not use gradient of the function thus is suitable for non-smooth functions.  We prove， for fully discrete systems,  that under dimension-independent conditions on the parameters, with suitable initial data, the algorithms converge to the neighborhood of the global minimum almost surely. We also introduce an Adaptive Moment Estimation (ADAM) based version to significantly improve its performance in high-space dimension.\nWHERE?\nOn-site / online\nOn-site:\nRoom H13 (Johann-Radon-Hörsaal)\nFriedrich-Alexander-Universität Erlangen-Nürnberg\nCauerstraße 11, 91058 Erlangen\nGPS-Koord. Raum: 49.573764N, 11.030028E\nOnline:\nZoom meeting link\nMeeting ID: 614 4658 159 | PIN code: 914397\nThis event on LinkedIn\nYou might like:\nFAU DCN-AvH Seminar by Prof. Dr. Shi Jin (April 1st, 2022): “Quantum algorithms for computing observables of nonlinear partial differential equations”\n
URL:https://dcn.nat.fau.eu/events/consensus-based-high-dimensional-global-non-convex-optimization-in-machine-learning/
ORGANIZER;CN=FAU DCN-AvH:MAILTO:
CATEGORIES:FAU DCN-AvH Jr. Seminar,Seminar/Talk
LOCATION:DDS, Friedrich-Alexander-Universität Erlangen-Nürnberg
ATTACH;FMTTYPE=image/png:https://dcn.nat.fau.eu/wp-content/uploads/FAUDCNAvH-seminar-20jan2023-sJin.png
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