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Group equivariant and steerable convolutional neural networks (regular and steerable G-CNNs) have recently emerged as a very effective model class for learning from signal data such as 2D and 3D images, video, and other data where symmetries are present.
The theory of induced representations in field theory
Jose M Figueroa-O’Farrill · 1987
Earlier work this paper cites.
The design and use of steerable filters
W T Freeman and E H Adelson · 1991
Earlier work this paper cites.
Symmetries and Laplacians: Introduction to Harmonic Analysis, Group Representations and Applications
David Gurarie · 1992
Earlier work this paper cites.
Computing fourier transforms and convolutions on the 2-sphere
J R Driscoll and D M Healy · 1994
Earlier work this paper cites.
A Course in Abstract Harmonic Analysis
G B Folland · 1995
Earlier work this paper cites.
The neurogeometry of pinwheels as a sub-riemannian contact structure
Jean Petitot · 2003
Earlier work this paper cites.
Induced representations and mackey theory
T Ceccherini-Silberstein, A Machí, F Scarabotti, and F Tolli · 2009
Earlier work this paper cites.
Transforming auto-encoders
G E Hinton, A Krizhevsky, and S D Wang · 2011
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Learning the irreducible representations of commutative lie groups
T Cohen and M Welling · 2014
Cited alongside, same era.
Transformation properties of learned visual representations
T S Cohen and M Welling · 2015
Cited alongside, same era.
Group equivariant convolutional networks
Taco S Cohen and Max Welling · 2016
Cited alongside, same era.
Steerable CNNs
Taco S Cohen and Max Welling · 2017
Cited alongside, same era.
Harmonic networks: Deep translation and rotation equivariance
Daniel E Worrall, Stephan J Garbin, Daniyar Turmukhambetov, and Gabriel J Brostow · 2017
Cited alongside, same era.
Dynamic routing between capsules
Learning steerable filters for rotation equivariant CNNs
Maurice Weiler, Fred A Hamprecht, and Martin Storath · 2018
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Tensor field networks: Rotation- and Translation-Equivariant neural networks for 3D point clouds
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
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N-body networks: a covariant hierarchical neural network architecture for learning atomic potentials
Risi Kondor · 2018
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On the generalization of equivariance and convolution in neural networks to the action of compact groups
Risi Kondor and Shubhendu Trivedi · 2018
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Spherical CNNs
Taco S Cohen, Mario Geiger, Jonas Koehler, and Max Welling · 2018
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Covariant compositional networks for learning graphs
Risi Kondor, Hy Truong Son, Horace Pan, Brandon Anderson, and Shubhendu Trivedi · 2018
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Sara Sabour, Nicholas Frosst, and Geoffrey E Hinton · 2017
Cited alongside, same era.
Closest in time.
Matrix capsules with EM routing
Geoffrey Hinton, Nicholas Frosst, and Sara Sabour · 2018
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