Workshop IV: Deep Geometric Learning of Big Data and Applications

May 20 - 24, 2019

Schedule


Monday, May 20, 2019

9:00 - 9:40
10:10 - 10:50
11:20 - 12:00
Jeremias Sulam (Johns Hopkins University)

Deep Learning as Sparsity Enforcing Algorithms
PDF Presentation

 
2:00 - 2:40
Marc Pollefeys (ETH Zurich)

Semantic 3D reconstruction
PDF Presentation

 
3:10 - 3:50
Bahram Jalali (University of California, Los Angeles (UCLA))

Low Latency Deep Imaging Cytometry

 

Tuesday, May 21, 2019

9:00 - 9:40
10:10 - 10:50
11:20 - 12:00
2:00 - 2:40
 
3:10 - 3:50
Hamed Pirsiavash (University of Maryland Baltimore County)

Self-supervised learning for visual recognition
PDF Presentation

 

Wednesday, May 22, 2019

9:00 - 9:40
Jian Tang (HEC Montréal)

GMNN: Graph Markov Neural Networks
PDF Presentation

 
10:10 - 10:50
Thomas Kipf (Universiteit van Amsterdam)

Unsupervised Learning with Graph Neural Networks
PDF Presentation

 
11:20 - 12:00
Jure Leskovec (Stanford University)

Deep Generative Models for Graphs: Methods & Applications

 
2:00 - 2:40
 
3:10 - 3:50
Federico Monti (Universita della Svizzera Italiana)

Geometric Deep Learning: approaches and applications


Thursday, May 23, 2019

9:00 - 9:40
Mikhail Belkin (Ohio State University)

From classical statistics to modern machine learning

 
10:10 - 10:50
Thiago Serra (Mitsubishi Electric Research Laboratories (Merl))

Bounding and Counting Linear Regions of Deep Neural Networks
PDF Presentation

 
11:20 - 12:00
Rene Vidal (Johns Hopkins University)

On the Implicit Bias of Dropout

 
2:00 - 2:40
Stanley Osher (University of California, Los Angeles (UCLA))

Unnormalized Optimal Transport
PDF Presentation

 
3:10 - 3:50
 
4:20 - 5:00

Friday, May 24, 2019

9:00 - 9:40
Taco Cohen (Qualcomm AI Research)

Gauge Equivariant Convolutional Networks

 
10:10 - 10:50
11:20 - 12:00