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

Gerrit Nolte
1/23/2023
cs.LGcs.NE

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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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Cite this paper

@article{nolte2023structural,
  title  = {A Structural Approach to the Design of Domain Specific Neural Network Architectures},
  author = {Gerrit Nolte},
  year   = {2023},
  eprint = {2301.09381},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2301.09381v1}
}

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