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About Preliminary OWR 2018 11
This document summarizes the proceedings of a mini-workshop on deep learning and inverse problems held from March 4-10, 2018 at the Mathematisches Forschungsinstitut Oberwolfach in Germany. The workshop brought together 15 participants to discuss developing a solid mathematical theory for analyzing deep neural networks for inverse problems. Several talks addressed interpreting neural network layers as discretizations of continuous systems like ODEs and PDEs, allowing for analysis of network stability and novel designs. Other talks discussed optimization of networks through functional analysis and regularization schemes. Harmonic analysis was also discussed as a way to interpret network behavior. Applications presented included CT reconstruction and photoacoustic tomography. Developing a consistent mathematical theory for deep learning and
Detailed Information
| Author: | ['Jaweria Amjad'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 29 |
| Language: | English |
| Format: | |
| Price: | FREE |
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