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Institute for Pure & Applied Mathematics (IPAM) · Science

Institute for Pure & Applied Mathematics (IPAM)

The Institute for Pure and Applied Mathematics (IPAM) is a National Science Foundation Math Institute at the University of California, Los Angeles (UCLA) that fosters the interaction of mathematics with a broad range of science and technology, builds new interdisciplinary research communities, promotes mathematical innovation, and engages and transforms the world through mathematics. IPAM fulfills its mission through workshops and programs that connect mathematics and other disciplines or multiple areas of mathematics. These activities bring in thousands of visitors annually from academia, government, and industry. The NSF has encouraged IPAM to make the scientific presentations of our speakers available to a wider audience. While this YouTube channel contains many of our most popular videos, our full video archive is available at www.ipam.ucla.edu/videos.

Episodes

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Francisco Silva: "On the approximation of first order mean field games"

10 Jul 2020
51m
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Jianfeng Zhang: "Set Values for Mean Field Games with Multiple Equilibriums"

10 Jul 2020
49m
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Paola Mannucci: "Deterministics Mean Field Games with control on the acceleration"

10 Jul 2020
41m
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Alpár Mészáros: "Global well-posedness of master equations for deterministic displacement convex..."

10 Jul 2020
56m
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Nicolás García Trillos: "From clustering with graph cuts to isoperimetric inequalities..."

06 Jul 2020
52m
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Pratik Chaudhari: "Learning with few labeled data"

06 Jul 2020
51m
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Quanquan Gu: "Learning Over-parameterized Neural Networks: From Neural Tangent Kernel to Mean-fi..."

06 Jul 2020
44m
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Rebecca Willett: "Learning to Solve Inverse Problems in Imaging"

06 Jul 2020
47m
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Christopher Finlay: "Training neural ODEs for density estimation"

06 Jul 2020
30m
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Wuchen Li: "Accelerated Information Gradient Flow"

06 Jul 2020
55m
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Tom Goldstein: "An empirical look at generalization in neural nets"

06 Jul 2020
53m
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Hossein Mobahi: "Differential Operators for Generating Structured Adversarial Examples"

06 Jul 2020
35m
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Anthony Yezzi: "Accelerated Optimization in the PDE Framework"

06 Jul 2020
56m
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Yunan Yang: "The joint power of deterministic and Bayesian approaches for seismic inverse problems"

06 Jul 2020
50m
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Irène Waldspurger: "Rank optimality of the Burer-Monteiro factorization"

06 Jul 2020
47m
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Mikhail Belkin: "Optimization for over-parameterized systems of non-linear equations"

06 Jul 2020
52m
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Maxim Raginsky: "A mean-field theory of lazy training in two-layer neural nets"

06 Jul 2020
49m
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Eldad Haber: "Active learning and experimental design - who should we test?"

06 Jul 2020
52m
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Peng Chen: "Projected Stein variational methods for high-dimensional Bayesian inversion"

06 Jul 2020
46m
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Maxime Laborde: "A Lyapunov analysis for accelerated gradient methods: From deterministic to sto..."

06 Jul 2020
43m
Page 98

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