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Simon S. Du (University of Washington): Ultra-wide Neural Network and Neural Tangent Kernel

Ort: MPI für Mathematik in den Naturwissenschaften Leipzig,  , Videobroadcast

Video broadcast: Math Machine Learning seminar MPI MIS + UCLA I will talk about the result on the equivalence between the over-parameterized neural network and a new kernel, Neural Tangent Kernel. This equivalence implies two surprising phenomena: 1) the simple algorithm gradient descent provably finds the global optimum of the highly non-convex empirical risk, and 2) the learned neural network generalizes well despite being highly over-parameterized. I will also present empirical results showing Neural Tangent Kernel is a strong predictor.

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Beginn: Aug. 6, 2020, 5 p.m.

Ende: Aug. 6, 2020, 6:30 p.m.