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A Structural Approach to the Design of Domain Specific Neural Network Architectures

arXiv 2301.09381 ↗First posted 23 Jan 2023cs.LG cs.NEcited by 0

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Abstract (arXiv)

This is a master's thesis concerning the theoretical ideas of geometric deep learning. Geometric deep learning aims to provide a structured characterization of neural network architectures, specifically focused on the ideas of invariance and equivariance of data with respect to given transformations. This thesis aims to provide a theoretical evaluation of geometric deep learning, compiling theoretical results that characterize the properties of invariant neural networks with respect to learning performance.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 66 references that are in the map. First posted on arXiv 23 Jan 2023.

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27 of 66 references.

References · 27

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15 more
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