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About New Bridges Between Deep Learning and Partial Differential Equations
1) The document discusses new connections between deep learning and partial differential equations (PDEs). It explores how techniques from PDEs can provide insights into deep learning algorithms and how neural networks can help solve high-dimensional PDEs. 2) Specifically, it describes how residual neural networks can be interpreted as approximations of solutions to ordinary differential equations (ODEs). This continuous viewpoint has led to more stable and better-understood neural network architectures. 3) It also discusses how PDE-based imaging algorithms provide inspiration for applying PDE techniques to deep learning, such as using convolutional neural networks with operators related to PDEs.
Detailed Information
| Author: | ['Aman Jalan'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 5 |
| Language: | English |
| Format: | |
| Price: | FREE |
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