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About KERNEL Geometric Deep Learning: LeCun
This document discusses geometric deep learning, which aims to generalize deep neural networks to non-Euclidean data structures like graphs and manifolds. It presents examples of non-Euclidean data that arise in applications like social networks, sensor networks, genetics, neuroscience, and computer graphics. Traditional deep learning methods assume an underlying Euclidean structure and properties like translation invariance, but these do not apply to non-Euclidean domains. The document outlines two classes of geometric learning problems - characterizing the structure of non-Euclidean data, and analyzing functions defined on such domains. It aims to show how concepts from deep learning like convolutions can be adapted to these settings.
Detailed Information
| Author: | ['Rubens Zimbres'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 22 |
| Language: | English |
| Format: | |
| Price: | FREE |
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