arXiv Machine Learning
Rotational Equivariance in Machine Learning: A Comprehensive Tutorial
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Representation Learning for Equivariant Inference with Guarantees
arXiv:2505. 19809v3 Announce Type: replace-cross Abstract: In many real-world applications of regression, conditional probability estimation, and uncertainty quantification, exploiting symmetries rooted in physics or geometry can dramatically improve generalization and sample efficiency.
Hugging Face Trending Papers
Jul 1
Group-Equivariant Poincaré Convolutional Networks
While recent advancements like the Poincaré ResNet have demonstrated the potential of learning visual representations directly in hyperbolic space, their optimisation remains hampered by the computationally intensive nature of Riemannian gradients and the strict boundaries of the manifold. Furthermore, standard hyperbolic networks treat spatial transformations of the same object as distinct hierarchical concepts, leading to redundant parameter usage and vanishing signals.
arXiv AI
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Group-Equivariant Poincar\'e Convolutional Networks
arXiv:2607. 00556v1 Announce Type: cross Abstract: While recent advancements like the Poincar\'e ResNet have demonstrated the potential of learning visual representations directly in hyperbolic space, their optimisation remains hampered by the computationally intensive nature of Riemannian gradients and the strict boundaries of the manifold.
arXiv Machine Learning
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Moment kernels: a simple and scalable approach for equivariance to rotations and reflections in deep convolutional networks
arXiv:2505. 21736v2 Announce Type: replace-cross Abstract: Translation equivariance is a central reason convolutional neural networks have been successful in computer vision.
arXiv AI
Jun 3
EqGINO: Equivariant Geometry-Informed Fourier Neural Operators for 3D PDEs
arXiv:2606. 03260v1 Announce Type: cross Abstract: Deep learning surrogates for 3D Partial Differential Equations (PDEs) often fail to generalize across geometric transformations because they depend heavily on specific coordinate systems.
arXiv AI
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Equivariant Sheaf Neural Networks: Learning Geometric Transport on Graphs
arXiv:2608.28853v1 Announce Type: cross Abstract: Equivariant graph neural networks provide a principled way to model geometric systems, but efficient first-order architectures remain limited in how...