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The principle of equivariance to symmetry transformations enables a theoretically grounded approach to neural network architecture design.
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Deep Residual Learning for Image Recognition
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Deep sets
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HexaConv
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Predicting molecular properties with covariant compositional networks
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Spherical CNNs on Unstructured Grids
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On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups
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An Equivariant Bayesian Convolutional Network predicts recombination hotspots and accurately resolves binding motifs
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Equivariance through parameter-sharing
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Multi-directional geodesic neural networks via equivariant convolution
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Parallel Transport Convolution: A New Tool for Convolutional Neural Networks on Manifolds
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Tensor Field Networks: Rotation- and Translation-Equivariant Neural Networks for 3D Point Clouds
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Rotation Equivariant CNNs for Digital Pathology
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3D G-CNNs for Pulmonary Nodule Detection
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Cubenet: Equivariance to 3d rotation and translation
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Deep Learning 3D Shapes using Alt-Az Anisotropic 2-Sphere Convolution
Liu, M., Yao, F., Choi, C., Ayan, S., and Karthik, R · 2019
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Invariant and Equivariant Graph Networks
Maron, H., Ben-Hamu, H., Shamir, N., and Lipman, Y · 2019
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