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An Information Theoretic Approach to Validate Deep Learning-Based Algorithms
Presenter
- Gitta Kutyniok
April 10, 2021
ICERM
Task-driven Network Discovery via Deep Reinforcement Learning on Embedded Spaces
Presenter
- Tina Eliassi-Rad
February 25, 2021
IPAM
Parameter inference for binary black holes using deep learning
Presenter
- Stephen Green
November 16, 2020
ICERM
Operator-split reduced order models for problems with moving interfaces
Presenter
- George Biros
April 24, 2020
ICERM
Evaluating Lossy Compression Rates of Deep Generative Models
Presenter
- Roger Grosse
April 15, 2020
IAS
Integrating optimization, constraints, and control within deep learning models
Presenter
- Zico Kolter
February 26, 2020
IPAM
Direct and dual Information Bottleneck frameworks for Deep Learning
Presenter
- Tali Tishby
February 24, 2020
IAS
Convolutional autoencoders and LSTMs - using deep learning to overcome Kolmogorov-width limitations and accurately model errors in nonlinear model reduction
Presenter
- Kevin Carlberg
February 21, 2020
ICERM
Momentum in Stochastic Gradient Descent and Deep Neural Nets
Presenter
- Bao Wang
January 29, 2020
IPAM
Geometric Understanding of Supervised and Unsupervised Deep Learning for Biomedical Image Reconstruction
Presenter
- Jong Chul Ye
January 28, 2020
IPAM
Splines and imaging: From compressed sensing to deep neural networks
Presenter
- MIchael Unser
January 27, 2020
IPAM
An Information Theoretic Approach to Validate Deep Learning-Based Algorithms
Presenter
- Gitta Kutyniok
October 30, 2019
IPAM