Qin Li - Mean Field Theory In Inverse Problems: From Bayesian Inference To Overparametrized Networks

Qin Li - Mean field theory in Inverse Problems: From Bayesian inference to overparametrized networks

One world theoretical machine learning

Qin Li - Mean field theory in Inverse Problems: from Bayesian inference to overparameterization...

Erwin Schrödinger International Institute for Mathematics and Physics (ESI)

Nathan Glatt Holtz - The Bayesian Approach to PDE Inverse Problems

USC CAMS Colloquium

Kinetic Theory and Data Science: "Who Feeds Whom What?" | Qin Li | University of Wisconsin - Madison

Women in Data Science (WiDS) - Alexandria

Qin Li - Multiscale inverse problem, from Schroedinger to Newton to Boltzmann - IPAM at UCLA

Institute for Pure & Applied Mathematics (IPAM)

Bayesian Inference and Uncertainty Quantification for Inverse Problems

Introduction to Bayesian Methods for Inverse Problems

Solving Bayesian Inverse Problems via Variational Autoencoders

International Zoom Inverse Problems Seminar, Dec 10, 2020, Ozan Öktem (KTH)

International Zoom Inverse Problems Seminar, UCI

Dr. Peng Chen | Projected variational inference for high-dimensional Bayesian inverse problems

INI Seminar Room 1

Mean-Field Theory of Two-Layers Neural Networks: Dimension Free Bounds and Example

Simons Institute

On the Use of (Linear) Surrogate Models for Bayesian Inverse Problems

Society for Industrial and Applied Mathematics

Daniel Zhengyu Huang - Efficient Derivative-free Bayesian Inference for Large-Scale Inverse Problems

Erwin Schrödinger International Institute for Mathematics and Physics (ESI)

Jan Povala - Variational Bayesian Approximation of Inverse Problems using Sparse Precision Matrices

The Alan Turing Institute

An introduction to mean field theory