no code implementations • 25 Jan 2024 • Emma Andersdotter, Fredrik Ohlsson
In this paper we develop a manifestly geometric framework for equivariant manifold neural ordinary differential equations (NODEs), and use it to analyse their modelling capabilities for symmetric data.
1 code implementation • 14 Jul 2023 • Oscar Carlsson, Jan E. Gerken, Hampus Linander, Heiner Spieß, Fredrik Ohlsson, Christoffer Petersson, Daniel Persson
High-resolution wide-angle fisheye images are becoming more and more important for robotics applications such as autonomous driving.
1 code implementation • 23 Mar 2023 • Axel Flinth, Fredrik Ohlsson
We investigate the optimization of multilayer perceptrons on symmetric data.
no code implementations • 10 Feb 2022 • Johannes Borgqvist, Fredrik Ohlsson, Ruth E. Baker
We discuss the role and merits of symmetry methods for the analysis of biological systems.
1 code implementation • 8 Feb 2022 • Jan E. Gerken, Oscar Carlsson, Hampus Linander, Fredrik Ohlsson, Christoffer Petersson, Daniel Persson
We compare the performance of the group equivariant networks known as S2CNNs and standard non-equivariant CNNs trained with an increasing amount of data augmentation.
no code implementations • 28 May 2021 • Jan E. Gerken, Jimmy Aronsson, Oscar Carlsson, Hampus Linander, Fredrik Ohlsson, Christoffer Petersson, Daniel Persson
We also discuss group equivariant neural networks for homogeneous spaces $\mathcal{M}=G/K$, which are instead equivariant with respect to the global symmetry $G$ on $\mathcal{M}$.