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

Gerrit Nolte

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arXiv · 2301.09381

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

Gerrit Nolte

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Abstract

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.

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Gerrit Nolte. 2023-01-23. A Structural Approach to the Design of Domain Specific Neural Network Architectures. https://arxiv.org/abs/2301.09381

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