Description
Deep learning is becoming a cornerstone for many applications. Finding appropriate models in the presence of data requires merging physical, computational, and statistical models. This workshop brings together experts working on mathematical, statistical, computational, and engineering aspects of deep learning to share their research experience in model learning.
The workshop will be hosted by Texas A&M University in College Station, Texas, and is supported by the Institute for Scientific Computation and Energi Simulation.
We will provide and update information for this workshop.
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Organizing Committee
- Yalchin Efendiev, Institute for Scientific Computation
- Eduardo Gildin, Energi Simulation
- Joseph Kwon, Artie McFerrin Department of Chemical Engineering
- Zhangyang (Atlas) Wang, Department of Computer Science & Engineering
- Ulisses Braga-Neto, Department of Electrical & Computer Engineering
- Pattern Recognition for Small-Sample Applications
- Yalchin Efendiev, Department of Mathematics
- Deep Multiscale Model Learning
- Eduardo Gildin, Harold Vance Department of Petroleum Engineering
- Development of Proxy Models for Reservoir Simulation by Sparsity Promoting Methods and Machine Learning Techniques
- Youngjib Ham, Department of Construction Science
- Deep Learning in Construction Science: Automated Contextual Information Analysis for Resource Allocation
- Boris Hanin, Department of Mathematics
- Which ReLU Net Architectures Give Rise to Exploding and Vanishing Gradients?
- Shuiwang Ji, Department of Computer Science & Engineering
- Deep Learning: Methods and Applications
- Peter Kuchment, Department of Mathematics
- Deep Learning in Detecting Illicit Nuclear Materials
- Joseph Kwon, Artie McFerrin Department of Chemical Engineering
- Data-Driven Identification of Interpretable Reduced-Order Models Using Sparse Regression
- Rajib Mukherjee, Texas A&M Energy Institute
- Optimal Hyperparameter Estimation in Support Vector Machine
- Guni Sharon, Department of Computer Science & Engineering
- Learning the Right Tolls - Traffic Flow Optimization Through Micro-Tolling
- Yang Shen, Department of Electrical & Computer Engineering
- Interpretable Deep Learning for Drug Discovery
- Zhangyang (Atlas) Wang, Department of Computer Science & Engineering
- A (Fairly Rough) Tour of Our Recent Works in Computer Vision, Machine Learning, and Their Applications
All workshop presentations will take place in Joe C. Richardson Petroleum Engineering Building (RICH) 910. Please note that the schedule is tentative and subject to change. All times listed are local time.
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- Welcome
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- Zhangyang (Atlas) Wang, Department of Computer Science & Engineering
- A (Fairly Rough) Tour of Our Recent Works in Computer Vision, Machine Learning, and Their Applications
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- Shuiwang Ji, Department of Computer Science & Engineering
- Deep Learning: Methods and Applications
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- Peter Kuchment, Department of Mathematics
- Deep Learning in Detecting Illicit Nuclear Materials
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- Break
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- Yalchin Efendiev, Department of Mathematics
- Deep Multiscale Model Learning
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- Ulisses Braga-Neto, Department of Electrical & Computer Engineering
- Pattern Recognition for Small-Sample Applications
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- Youngjib Ham, Department of Construction Science
- Deep Learning in Construction Science: Automated Contextual Information Analysis for Resource Allocation
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- Lunch Break
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- Boris Hanin, Department of Mathematics
- Which ReLU Net Architectures Give Rise to Exploding and Vanishing Gradients?
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- Rajib Mukherjee, Texas A&M Energy Institute
- Optimal Hyperparameter Estimation in Support Vector Machine
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- Guni Sharon, Department of Computer Science & Engineering
- Learning the Right Tolls - Traffic Flow Optimization Through Micro-Tolling
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- Break
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- Yang Shen, Department of Electrical & Computer Engineering
- Interpretable Deep Learning for Drug Discovery
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- Joseph Kwon, Artie McFerrin Department of Chemical Engineering
- Data-Driven Identification of Interpretable Reduced-Order Models Using Sparse Regression
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- Eduardo Gildin, Harold Vance Department of Petroleum Engineering
- Development of Proxy Models for Reservoir Simulation by Sparsity Promoting Methods and Machine Learning Techniques
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- Closing Remarks
Contact Information
If you have any questions concerning this workshop, email Brad Shumbera at shumbera@tamu.edu.