Location
This workshop took place at Gross Hall on the campus of Duke University.
Description
The SAMSI Deep Learning program brought together mathematical, statistical, and computer scientists interested in understanding the theoretical capabilities and limitations of deep learning methodology. This workshop featured overview talks that introduce the basic concepts of deep networks (including convolutional neural networks, recursive neural networks, generative adversarial networks, and various kinds of autoencoders). Additionally, there were a series of talks that highlighted recent research in this area. Later in the week participants broke out into working groups, and developed plans for their semester of work on these topics. On the final day, the leaders of the working groups shared their plans of their group with all of the workshop participants.
Confirmed speakers for this event include:
- Amitabh Basu, Applied Mathematics and Statistics, Johns Hopkins University
- Larry Carin, Electrical and Computer Engineering, Duke University
- Guang Cheng, Statistics, Purdue University
- Jianqing Fan, Statistics, Princeton University
- Xiaoming Huo, Statistics, Georgia Institute of Technology
- Adam Klivans, Computer Science, University of Texas-Austin
- Jason Klusowski, Statistics, Rutgers University
- Guanghui (George) Lan, Industrial and Systems Engineering, Georgia Institute of Technology
- Poh-Ling Loh, Electrical and Computer Engineering, University of Wisconsin University
- Deanna Needell, Mathematics, UCLA
- Rebecca Willet, Statistics, University of Chicago
- Johannes Schmidt-Hieber, Statistics, University of Twente
- Harrison Zhou, Statistics, Yale University
Schedule and Supporting Media
Printed Schedule
Titles & Abstracts
Poster Titles
Participants List
Monday, August 12, 2019
Ahmadieh Family Auditorium, Gross Hall, Rm 107, Duke University, Durham, NC
| Description | Speaker | Slides | Video |
|---|---|---|---|
| Registration | |||
| Welcome | David Banks, Duke University and Director, SAMSI | ||
| A Survey of Statistical Research in Neural Networks and Deep Learning | Xiaoming Huo, Georgia Institute of Technology | ||
| Admissibility of Solution Estimators for Stochastic Optimization | Amitabh Basu, Johns Hopkins University | ||
| Statistical and Computational Guarantees of EM with Random Initialization | Harrison Zhou, Yale University | ||
| Robust Information Bottleneck | Poh-Ling Loh, University of Wisconsin | ||
| Towards Deep Learning: Understanding Statistical Properties by Bridging Convex and Nonconvex Optimization | Jianqing Fan, Princeton University | ||
| Horseshoe Regularization for Machine Learning in Complex and Deep Models | Anindya Bhadra, Purdue University | ||
| Posterior Concentration for Sparse Deep Learning | Veronika Rockova, University of Chicago | ||
| Deep Compositional Spatial Models | Andrew Zammit Mangion, University of Woolongong, Australia | ||
| Training DNN with Dynamic SMD | Shih-Kang Chao, University of Missouri | ||
| Posters and Reception |
Tuesday, August 13, 2019
Ahmadieh Family Auditorium, Gross Hall, Rm 107, Duke University, Durham, NC
| Description | Speaker | Slides |
|---|---|---|
| On Adversarial Learning | Larry Carin, Duke University | |
| Deep Instrumental Variables Estimator | Ruiqi Liu, University of Indiana | |
| Improving Generative Models | Junier Oliva, University of North Carolina at Chapel Hill | |
| Learning to Solve Inverse Problems in Imaging | Rebecca Willett, University of Chicago | |
| An Adaptively Weighted Stochastic Gradient MCMC Algorithm for Global Optimization in Deep Learning | Faming Liang, Purdue University | |
| Information Geometric and Topological Approaches to Deep Learning | Wyatt Bridgman and Sorin Mitran, University of North Carolina at Chapel Hill | |
| Domain Adaptation Challenges in Genomics: a deep learning take on medical pathology | Bianca Dumitrascu, Princeton University and SAMSI | |
| Neural Network Density Estimation | Deborshee Sen, Duke University and SAMSI | |
| Complexity Bounds for Deep Learning Networks via the Probabilistic Method | Jason Klusowski, Rutgers University |
Wednesday, August 14, 2019
Ahmadieh Family Auditorium, Gross Hall, Rm 107, Duke University, Durham, NC
| Description | Speaker | Slides |
|---|---|---|
| Statistical Inference for Online Decision Making via Stochastic Gradient Descent | Rui Song, N.C. State University | |
| Modern Statistical Theory Inspired by Deep Learning | Guang Cheng, Purdue University | |
| Deep ReLU Networks Viewed as a Statistical Method | Johannes Schmidt-Hieber, University of Twente | |
| ReLU regression: Complexity and Approximation Algorithms | Yao Xie, Georgia Institute of Technology | |
| ProxSARAH Algorithms for Stochastic Composite Nonconvex Optimization | Quoc Tran-Dinh, University of North Carolina at Chapel Hill | |
| Deep Models for Improved Topic Recovery | Deanna Needell, UCLA | |
| Group-equivariant Representation by Jointly Decomposed Convolution | Xiuyuan Cheng, Duke University | |
| Optimization and Learning with Nonconvex Functional Constraints | Guanghui (George) Lan, Georgia Institute of Technology | |
| Working Group Formation |
Thursday, August 15, 2019
Breakout Rooms: 318, 324, 351, Gross Hall
| Description | Location |
|---|---|
| Working Group Planning Meeting: Bayesian Methods in Deep Learning | Gross Hall, Rm 318 |
| Working Group Planning Meeting: Teaching Deep Learning | Gross Hall, Rm 324 |
| Working Group Planning Meeting: Uncertainty Quantification for Deep Models | Gross Hall, Rm 351 |
| Working Group Planning Meeting: Novel Applications | Gross Hall, Rm 318 |
| Working Group Planning Meeting: Interpretable Deep Networks | Gross Hall, Rm 324 |
| Working Group Planning Meeting: Architecture Search | Gross Hall, Rm 351 |
| Working Group Planning Meeting: Deep Learning for Dimension Reduction | Gross Hall, Rm 318 |
| Working Group Planning Meeting: Statistical Optimality | Gross Hall, Rm 324 |
| Working Group Planning Meeting: Regularization Techniques for Training Deep Networks | Gross Hall, Rm 351 |
Friday, August 16, 2019
Ahmadieh Family Auditorium Gross Hall, Rm 107, Duke University, Durham, NC
| Description |
|---|
| Working Group Leaders provide description of research plans |
| Working Group Leaders provide description of research plans |
| Closing Remarks |
Questions: email [email protected]