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DTSTART:20210314T100000
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DTSTART;TZID=America/Los_Angeles:20211008T133000
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DTSTAMP:20260516T030615
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UID:1691-1633699800-1633703400@ifds.info
SUMMARY:IFDS All-Hands: Qin Li
DESCRIPTION:Title:\nMean field theory in Inverse Problems from Bayesian inference to overparameterization of networks \n\nAbstract:\nBayesian sampling and neural networks are seemingly two different machine learning areas\, but they both deal with many particle systems. In sampling\, one evolves a large number of samples (particles) to match a target distribution function\, and in optimizing over-parameterized neural networks\, one can view neurons particles that feed each other information in the DNN flow. These perspectives allow us to employ mean-field theory\, a powerful tool that translates dynamics of many particle system into a partial differential equation (PDE)\, so rich PDE analysis techniques can be used to understand both the convergence of sampling methods and the zero-loss property of over-parameterization of ResNets. We showcase the use of mean-field theory in these two machine learning areas\, and we also invite the audience to brainstorm other possible applications.
URL:https://ifds.info/event/ifds-all-hands-qin-li/
LOCATION:WI
CATEGORIES:Monthly All-Hands
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