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We present Clifford-Steerable Convolutional Neural Networks (CS-CNNs), a novel class of $\mathrm{E}(p, q)$-equivariant CNNs.
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Multivector differential calculus
Hitzer, E. M · 2002
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Scale-space
Lindeberg, T · 2009
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Space-time algebra
Hestenes, D · 2015
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Adam: A Method for Stochastic Optimization
Kingma, D. P. and Ba, J · 2015
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Group Equivariant Convolutional Networks
Cohen, T. and Welling, M · 2016
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Gaussian Error Linear Units (GELUs)
Hendrycks, D. and Gimpel, K · 2016
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Steerable CNNs
Cohen, T. S. and Welling, M · 2017
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Deep-learning top taggers or the end of qcd?
Kasieczka, G., Plehn, T., Russell, M., and Schell, T · 2017
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Sgdr: Stochastic Gradient Descent with Warm Restarts
Loshchilov, I. and Hutter, F · 2017
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Roto-Translation Covariant Convolutional Networks for Medical Image Analysis
Bekkers, E. J., Lafarge, M. W., Veta, M., Eppenhof, K. A. J., Pluim, J. P. W., and Duits, R · 2018
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Scale equivariance in CNNs with vector fields
Marcos, D., Kellenberger, B., Lobry, S., and Tuia, D · 2018
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Group Normalization
Wu, Y. and He, K · 2018
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Scale Steerable Filters for Locally Scale-Invariant Convolutional Neural Networks
Ghosh, R. and Gupta, A · 2019
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General E(2)-Equivariant Steerable CNNs
Weiler, M. and Cesa, G · 2019
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Deep Scale-spaces: Equivariance Over Scale
Worrall, D. E. and Welling, M · 2019
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B-spline CNNs on Lie groups
Bekkers, E · 2020
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Lorentz group equivariant neural network for particle physics
Bogatskiy, A., Anderson, B. M., Offermann, J. T., Roussi, M., Miller, D. W., and Kondor, R · 2020
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Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data
Finzi, M., Stanton, S., Izmailov, P., and Wilson, A. G · 2020
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Euclidean neural networks: e3nn
Geiger, M., Smidt, T., Alby, M., Miller, B. K., Boomsma, W., Dice, B., Lapchevskyi, K., Weiler, M., Tyszkiewicz, M., Batzner, S., et al · 2020
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Learning to Control PDEs with Differentiable Physics
Holl, P., Thuerey, N., and Koltun, V · 2020
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Scale-Equivariant Steerable Networks
Sosnovik, I., Szmaja, M., and Smeulders, A. W. M · 2020
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Pycharge: an open-source python package for self-consistent electrodynamics simulations of lorentz oscillators and moving point charges
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An efficient lorentz equivariant graph neural network for jet tagging
Gong, S., Meng, Q., Zhang, J., Qu, H., Li, C., Qian, S., Du, W., Ma, Z.-M., and Liu, T.-Y · 2022
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Towards Multi-spatiotemporal-scale Generalized PDE Modeling
Gupta, J. K. and Brandstetter, J · 2022
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Steerable Partial Differential Operators for Equivariant Neural Networks
Jenner, E. and Weiler, M · 2022
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CKConv: Continuous Kernel Convolutions for Sequential Data
Romero, D. W., Kuzinna, A., Bekkers, E. J., Tomczak, J. M., and Hoogendoorn, M · 2022
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A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups
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Haan, P. d., Weiler, M., Cohen, T., and Welling, M · 2021
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Lang, L. and Weiler, M · 2021
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Fourier Neural Operator for Parametric Partial Differential Equations
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Extensions to the navier–stokes equations
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Similarity equivariant linear transformation of joint orientation-scale space representations
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Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters
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Clifford Neural Layers for PDE Modeling
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Geometric Algebra Transformer
Brehmer, J., Haan, P. d., Behrends, S., and Cohen, T. S · 2023
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Group Equivariant Fourier Neural Operators for Partial Differential Equations
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Wavelet networks: Scale-translation equivariant learning from raw time-series
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Equivariant and Coordinate Independent Convolutional Networks
Weiler, M., Forré, P., Verlinde, E., and Welling, M · 2023
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Implicit Convolutional Kernels for Steerable CNNs
Zhdanov, M., Hoffmann, N., and Cesa, G · 2023
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Does lorentz-symmetric design boost network performance in jet physics?
Li, C., Qu, H., Qian, S., Meng, Q., Gong, S., Zhang, J., Liu, T.-Y., and Li, Q · 2024
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