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Found 6 papers, 3 papers with code
Equivariant Manifold Neural ODEs and Differential Invariants
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.
HEAL-SWIN: A Vision Transformer On The Sphere
High-resolution wide-angle fisheye images are becoming more and more important for robotics applications such as autonomous driving.
Optimization Dynamics of Equivariant and Augmented Neural Networks
Under natural assumptions on the data, network, loss, and group of symmetries, we show that compatibility of the spaces of admissible layers and equivariant layers, in the sense that the corresponding orthogonal projections commute, implies that the sets of equivariant stationary points are identical for the two strategies.
Symmetries of systems of first order ODEs: Symbolic symmetry computations, mechanistic model construction and applications in biology
We discuss the role and merits of symmetry methods for the analysis of biological systems.
Equivariance versus Augmentation for Spherical Images
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.
Geometric Deep Learning and Equivariant Neural Networks
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 $\mathcal{M}$.
