Geometry & Learning from Data (Online)
Videos from CMO Workshop
Juergen Jost, MPI MIS
Monday Oct 25, 2021 09:00 - 09:46
Geometry and topology of data
Anna Seigal, Harvard University
Monday Oct 25, 2021 10:00 - 10:49
Groups and symmetries in Gaussian graphical models
Shantanu Joshi, UCLA
Monday Oct 25, 2021 11:00 - 11:55
Aligning Shape Data from Brain Imaging: applications to fMRI time series, diffusion tractography
Nancy Arana-Daniel, Universidad de Guadalajara
Monday Oct 25, 2021 13:00 - 13:47
Environmental object mapping using geometric algebra and machine learning
Benjamin Sanchez-Lengeling, Google Research
Monday Oct 25, 2021 14:00 - 14:49
Evaluating attribution with Graph Neural Networks
Sophie Achard, CNRS University of Grenoble
Tuesday Oct 26, 2021 09:00 - 09:44
Learning from brain data
Nihat Ay, TUHH
Tuesday Oct 26, 2021 10:00 - 10:54
On the invariance of the natural gradient for learning in deep neural networks
Facundo Memoli, The Ohio State University
Tuesday Oct 26, 2021 11:00 - 12:00
The ultrametric Gromov-Wasserstein distance
Ruriko Yoshida, Naval Postgraduate School
Tuesday Oct 26, 2021 13:00 - 13:54
Tree Topologies along a Tropical Line Segment
Jun Zhang, University of Michigan
Tuesday Oct 26, 2021 14:00 - 15:01
Information Geometry: A Tutorial
Yalbi Itzel Balderas-Martinez, Instituto Nacional de Enfermedades Respiratorias
Tuesday Oct 26, 2021 16:30 - 17:36
Panel: AI & Public Institutions, with Dr. Eduardo Ulises Moya, Dra. Paola Villareal, and Dra. Yalbi Itzel Balderas Martinez.
Maks Ovsjanikov,, LIX Ecole Polytechnique
Wednesday Oct 27, 2021 09:00 - 09:56
Efficient learning on curved surfaces via diffusion
Xavier Pennec, Université Côte d'Azur and INRIA
Wednesday Oct 27, 2021 10:00 - 10:51
Curvature effects in Geometric statistics : empirical Frechet mean and parallel transport accuracy.
Chris Connell, Indiana University Bloomington
Wednesday Oct 27, 2021 11:00 - 12:07
Tensor decomposition based network embedding algorithms for prediction tasks on dynamic networks.
Nina Miolane, UC Santa Barbara
Wednesday Oct 27, 2021 13:00 - 13:51
Geomstats: a Python Package for Riemannian Geometry in Statistics and Machine Learning
Katy Craig, University of California, Santa Barbara
Wednesday Oct 27, 2021 14:00 - 15:03
A Blob Method for Diffusion and Applications to Sampling and Two Layer Neural Networks
Tina Eliassi-Rad, Northeastern University
Wednesday Oct 27, 2021 16:30 - 17:41
Panel: Professional Development, with Prof. Tina Eliassi-Rad and Prof. Jesús de Loera
Alexander Cloninger, University of California San Diego
Thursday Oct 28, 2021 09:00 - 09:56
Learning with Optimal Transport
Ron Kimmel, Technion-Israel Institute of Technology
Thursday Oct 28, 2021 10:00 - 10:52
On Geometry and Learning
Pratik Chaudhari, University of Pennsylvania
Thursday Oct 28, 2021 11:00 - 12:02
Does the Data Induce Capacity Control in Deep Learning?
Elizabeth Gross, University of Hawaii at Manoa
Thursday Oct 28, 2021 14:00 - 14:54
Learning phylogenetic networks using invariants
Michael Bronstein, Imperial College
Friday Oct 29, 2021 09:00 - 10:00
Neural diffusion PDEs, differential geometry, and graph neural networks
Nina Otter, Queen Mary University London
Friday Oct 29, 2021 10:00 - 10:47
A topological perspective on weather regimes
Joe Kileel, UT Austin
Friday Oct 29, 2021 11:00 - 11:58
Structure in point clouds by tensor decompositions
Eliza O'Reilly, Caltech
Friday Oct 29, 2021 13:00 - 13:57
Random Tessellation Features and Forests
