Videos

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Why Deep Learning Works: Heavy-Tailed Random Matrix Theory as an Example of Physics Informed Machine Learning Thumbnail

Why Deep Learning Works: Heavy-Tailed Random Matrix Theory as an Example of Physics Informed Machine Learning

Presenter
  • Michael Mahoney
October 14, 2019
IPAM
Extremal cases of Rapoport-Zink spaces Thumbnail

Extremal cases of Rapoport-Zink spaces

Presenter
  • Michael Rapoport
October 10, 2019
IAS

Forming fabric from knitted curves

Presenter
  • Michael Dimitriyev
September 27, 2019
ICERM
Introduction to Holomorphic Differentials, III Thumbnail

Introduction to Holomorphic Differentials, III

Presenter
  • Michael Wolf
August 21, 2019
SLMath
Introduction to Holomorphic Differentials, II Thumbnail

Introduction to Holomorphic Differentials, II

Presenter
  • Michael Wolf
August 20, 2019
SLMath
Introduction to Holomorphic Differentials, I Thumbnail

Introduction to Holomorphic Differentials, I

Presenter
  • Michael Wolf
August 19, 2019
SLMath

Topology Talk 2

Presenter
  • Michael Robinson
August 8, 2019
ICERM

Min-Max affine approximants and sketches

Presenter
  • Michael Werman
April 29, 2019
ICERM
Dimension of the stationary measure for random matrix products in $SL_2(\mathbb{R})$ Thumbnail

Dimension of the stationary measure for random matrix products in $SL_2(\mathbb{R})$

Presenter
  • Michael Hochman
April 18, 2019
IAS
Geometric deep learning for 3D shape analysis (Part 2/2) Thumbnail

Geometric deep learning for 3D shape analysis (Part 2/2)

Presenter
  • MIchael Bronstein
March 12, 2019
IPAM
Geometric deep learning for 3D shape analysis (Part 1/2) Thumbnail

Geometric deep learning for 3D shape analysis (Part 1/2)

Presenter
  • Michael Bronstein
March 12, 2019
IPAM
Normalized harmonic map flow Thumbnail

Normalized harmonic map flow

Presenter
  • Michael Struwe
March 6, 2019
IAS