Saturday Morning Videos: IPAM Workshop IV: Deep Geometric Learning of Big Data and Applications (May 20 - 24, 2019)

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Hat tip to Xavier for letting us know about these. Here are the videos and slides of Workshop IV: Deep Geometric Learning of Big Data and Applications, Part of the Long Program Geometry and Learning from Data in 3D and Beyond at IPAM. The workshop took place May 20 - 24, 2019. And thank you to the organizing committee (Xavier Bresson, Yann LeCun, Stanley Osher, Rene Vidal, Rebecca Willett) for making this workshop happen !

Arthur Szlam (Facebook)

Language, Interaction, and Perception in Minecraft

Soumith Chintala (Facebook AI Research)

Automatic Differentiation, PyTorch and Graph Neural Networks

Jeremias Sulam (Johns Hopkins University)

Deep Learning as Sparsity Enforcing Algorithms 

 

Marc Pollefeys (ETH Zurich)

Semantic 3D reconstruction 

 

Bahram Jalali (University of California, Los Angeles (UCLA))

Low Latency Deep Imaging Cytometry

Tom Goldstein (University of Maryland)

A theoretical look at adversarial examples: a perspective from high-dimensional geometry 

Hongyang Zhang (Carnegie Mellon University)

Theoretically Principled Trade-off between Robustness and Accuracy 

 

Roy Lederman (Yale University)

On Inverse Problems and Unsupervised Learning in the Mapping of Heterogeneous Molecular Structures in Cryo-Electron Microscopy

 

Xavier Bresson (Nanyang Technological University, Singapore)

Graph Convolutional Neural Networks for Molecule Generation and Travelling Salesman Problem 

Hamed Pirsiavash (University of Maryland Baltimore County)

Self-supervised learning for visual recognition

Jian Tang (HEC Montréal)

GMNN: Graph Markov Neural Networks 

Thomas Kipf (Universiteit van Amsterdam)

Unsupervised Learning with Graph Neural Networks 

Jure Leskovec (Stanford University)

Deep Generative Models for Graphs: Methods & Applications

Mathias Niepert (NEC Laboratories Europe)

Relational Representation Learning with Graph Neural Networks

Federico Monti (Universita della Svizzera Italiana

Geometric Deep Learning: approaches and applications

 

Mikhail Belkin (Ohio State University)

From classical statistics to modern machine learning

Thiago Serra (Mitsubishi Electric Research Laboratories (Merl))

Bounding and Counting Linear Regions of Deep Neural Networks 

Rene Vidal (Johns Hopkins University)

On the Implicit Bias of Dropout

Stanley Osher (University of California, Los Angeles (UCLA))

Unnormalized Optimal Transport 

Srikumar Ramalingam (University of Utah)

Deriving Equivalent Networks and hyperparameter optimization

Luc Van Gool (ETH Zurich)

The creation of dense depth maps for autonomous cars

Taco Cohen (Universiteit van Amsterdam)

Gauge Equivariant Convolutional Networks

Kostas Daniilidis (University of Pennsylvania)

Geometry-aware deep learning: A brief history of equivariant representations and recent results

Ersin Yumer (Uber ATG)

Exploiting the 3D Geometry and the Structure of the World in Deep Learning

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