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Talk

Achieving equivariance in neural networks

  • Axel Flinth (Umea University)
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Abstract

There are several competing strategies for achieving equivariance in neural networks. In this talk, we are going to take a theoretical closer look at two of them: data augmentation and architecture restriction. Our main question is when the two strategies are equivalent. The analysis will reveal that a geometrical relation between the network architecture and the space of equivariant linear layers will imply a weak equivalence of the two strategies. This talk is based on joint work with Fredrik Ohlsson and Oskar Nordenfors.

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seminar
05.12.24 19.12.24

Math Machine Learning seminar MPI MIS + UCLA

MPI for Mathematics in the Sciences Live Stream

Katharina Matschke

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