Fetching the paper…
Reading the bibliography…
Recent works show that group equivariance as an inductive bias improves neural network performance for both classification and generation.
K. Iwasawa, “On some types of topological groups,” Annals of Mathematics , vol. 50, no. 3, pp. 507–558, Jul. 1949
1949
Earlier work this paper cites.
F. B. Fuchs, D. E. Worrall, V. Fischer, and M. Welling, “ SE ( 3 ) \mbox{SE}(3) -transformers: 3D roto-translation equivariant attention networks,” in Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS) , Dec. 2020, pp. 1970–1981
1981
Earlier work this paper cites.
L.-J. Lin, “Reinforcement learning for robots using neural networks,” Ph.D. dissertation, Carnegie Mellon University, Jan. 1993
1993
Earlier work this paper cites.
N. Qian, “On the momentum term in gradient descent learning algorithms,” Neural networks , vol. 12, no. 1, pp. 145–151, 1999
1999
Earlier work this paper cites.
J. Vermorel and M. Mohri, “Multi-armed bandit algorithms and empirical evaluation,” in Proceedings of the European Conference on Machine Learning , Oct. 2005, pp. 437–448
2005
Earlier work this paper cites.
B. Baumslag, “A simple way of proving the Jordan-Hölder-Schreier theorem,” The American Mathematical Monthly , vol. 113, no. 10, pp. 933–935, Dec. 2006
2006
Earlier work this paper cites.
H. Larochelle, D. Erhan, A. Courville, J. Bergstra, and Y. Bengio, “An empirical evaluation of deep architectures on problems with many factors of variation,” in Proceedings of the 24th international conference on Machine learning , 2007, pp. 473–480
2007
Earlier work this paper cites.
A. Krizhevsky, “Learning multiple layers of features from tiny images,” Master’s thesis, University of Toronto , 2009
2009
Earlier work this paper cites.
S. Adam, L. Busoniu, and R. Babuska, “Experience replay for real-time reinforcement learning control,” IEEE Transactions on Systems, Man, and Cybernetics, Part C , vol. 42, no. 2, pp. 201–212, Mar. 2011
2011
Earlier work this paper cites.
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng, “Reading digits in natural images with unsupervised feature learning,” in NIPS Workshop on Deep Learning and Unsupervised Feature Learning , Dec. 2011
2011
Earlier work this paper cites.
R. Gens and P. M. Domingos, “Deep symmetry networks,” in Proceedings of the 27th Conference on Neural Information Processing Systems , Dec. 2014, pp. 2537–2545
2014
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature , vol. 521, no. 7553, pp. 436–444, May 2015
2015
Earlier work this paper cites.
T. Cohen and M. Welling, “Group equivariant convolutional networks,” in Proceedings of the 33rd International Conference on Machine Learning (ICML) , Jun. 2016, pp. 2990–2999
2016
Earlier work this paper cites.
T. S. Cohen and M. Welling, “Steerable CNNs,” in Proceedings of the 5th International Conference on Learning Representations (ICLR) , Apr. 2017
2017
Earlier work this paper cites.
S. Ravanbakhsh, J. Schneider, and B. Póczos, “Equivariance through parameter-sharing,” in Proceedings of the 34th International Conference on Machine Learning (ICML) , Aug. 2017, pp. 2892–2901
2017
Earlier work this paper cites.
D. E. Worrall, S. J. Garbin, D. Turmukhambetov, and G. J. Brostow, “Harmonic networks: Deep translation and rotation equivariance,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , Jul. 2017, pp. 5028–5037
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Proceedings of the 30th Conference on Neural Information Processing Systems , Dec. 2017, pp. 5998–6008
2017
Earlier work this paper cites.
S. Sabour, N. Frosst, and G. E. Hinton, “Dynamic routing between capsules,” in Proceedings of the 31st International Conference on Neural Information Processing Systems , Dec. 2017, pp. 3859–3869
2017
Earlier work this paper cites.
B. Baker, O. Gupta, N. Naik, and R. Raskar, “Designing neural network architectures using reinforcement learning,” in Proceedings of the 5th International Conference on Learning Representations (ICLR) , Apr. 2017
2017
Earlier work this paper cites.
B. Zoph and Q. Le, “Neural architecture search with reinforcement learning,” in Proceedings of the 5th International Conference on Learning Representations (ICLR) , Apr. 2017
2017
Earlier work this paper cites.
G. Cohen, S. Afshar, J. Tapson, and A. Van Schaik, “Emnist: Extending mnist to handwritten letters,” in 2017 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2017, pp. 2921–2926
2017
Earlier work this paper cites.
tecperson, “Sign language mnist: Drop-in replacement for mnist for hand gesture recognition tasks,” 2017. [Online]. Available: https://www.kaggle.com/datamunge/sign-language-mnist
2017
Earlier work this paper cites.
T. S. Cohen, M. Geiger, J. Köhler, and M. Welling, “Spherical CNNs,” in Proceedings of the 6th International Conference on Learning Representations (ICLR) , May 2018
2018
Cited alongside, same era.
C. Esteves, C. Allen-Blanchette, A. Makadia, and K. Daniilidis, “Learning S O ( 3 ) {SO}(3) equivariant representations with spherical CNNs,” in Proceedings of the European Conference on Computer Vision (ECCV) , Sep. 2018, pp. 52–68
2018
Cited alongside, same era.
D. Kuzminykh, D. Polykovskiy, and A. Zhebrak, “Extracting invariant features from images using an equivariant autoencoder,” in Proceedings of the 10th Asian Conference on Machine Learning , Nov. 2018, pp. 438–453
2018
Cited alongside, same era.
L. Falorsi, P. de Haan, T. R. Davidson, N. De Cao, M. Weiler, P. Forré, and T. S. Cohen, “Explorations in homeomorphic variational auto-encoding,” in ICML Workshop on Theoretical Foundations and Applications of Deep Generative Models , Jul. 2018
2018
Cited alongside, same era.
E. J. Bekkers, “B-spline CNNs on Lie groups,” in Proceedings of the 8th International Conference on Learning Representations (ICLR) , Apr. 2020
2020
Later among the works it cites.
M. Finzi, S. Stanton, P. Izmailov, and A. G. Wilson, “Generalizing convolutional neural networks for equivariance to Lie groups on arbitrary continuous data,” in Proceedings of the 37th International Conference on Machine Learning (ICML) , Jul. 2020, pp. 3165–3176
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
R. Kondor and S. Trivedi, “On the generalization of equivariance and convolution in neural networks to the action of compact groups,” in Proceedings of the 35th International Conference on Machine Learning (ICML) , Jul. 2018, pp. 2747–2755
2018
Cited alongside, same era.
C. Esteves, C. Allen-Blanchette, X. Zhou, and K. Daniilidis, “Polar transformer networks,” in Proceedings of the 6th International Conference on Learning Representations (ICLR) , May 2018
2018
Cited alongside, same era.
J. E. Lenssen, M. Fey, and P. Libuschewski, “Group equivariant capsule networks,” in Proceedings of the 31st Conference on Neural Information Processing Systems , Dec. 2018, pp. 8844–8853
2018
Cited alongside, same era.
H. Pham, M. Guan, B. Zoph, Q. Le, and J. Dean, “Efficient neural architecture search via parameters sharing,” in Proceedings of the 35th International Conference on Machine Learning (ICML) , Jul. 2018, pp. 4095–4104
2018
Cited alongside, same era.
R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction . MIT Press, 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
D. Worrall and M. Welling, “Deep scale-spaces: Equivariance over scale,” in Proceedings of the 32nd Conference on Neural Information Processing Systems , Dec. 2019, pp. 7366–7378
2019
Cited alongside, same era.
M. Weiler and G. Cesa, “General E ( 2 ) \mbox{E}(2) -equivariant steerable CNNs,” in Proceedings of the 32nd Conference on Neural Information Processing Systems , Dec. 2019, pp. 14 334–14 345
2019
Cited alongside, same era.
D. W. Romero and M. Hoogendoorn, “Co-attentive equivariant neural networks: Focusing equivariance on transformations co-occurring in data,” in Proceedings of the 8th International Conference on Learning Representations (ICLR) , Apr. 2020
2020
Later among the works it cites.
D. W. Romero, E. J. Bekkers, J. M. Tomczak, and M. Hoogendoorn, “Attentive group equivariant convolutional networks,” in Proceedings of the 37th International Conference on Machine Learning (ICML) , Jul. 2020, pp. 8188–8199
2020
Later among the works it cites.
J. Gordon, D. Lopez-Paz, M. Baroni, and D. Bouchacourt, “Permutation equivariant models for compositional generalization in language,” in Proceedings of the 8th International Conference on Learning Representations (ICLR) , Apr. 2020
2020
Later among the works it cites.
C. Esteves, A. Makadia, and K. Daniilidis, “Spin-weighted spherical CNNs,” in Proceedings of the Conference on Neural Information Processing Systems (NeurIPS) , Dec. 2020, pp. 8614–8625
2020
Later among the works it cites.
A. Bogatskiy, B. Anderson, J. Offermann, M. Roussi, D. Miller, and R. Kondor, “Lorentz group equivariant neural network for particle physics,” in Proceedings of the 37th International Conference on Machine Learning (ICML) , Jul. 2020, pp. 992–1002
2020
Later among the works it cites.
E. Van der Pol, D. Worrall, H. Van Hoof, F. Oliehoek, and M. Welling, “MDP homomorphic networks: Group symmetries in reinforcement learning,” in Proceedings of the 33rd Conference on Advances in Neural Information Processing Systems (NeurIPS) , Dec. 2020, pp. 4199–4210
2020
Later among the works it cites.
J. Köhler, L. Klein, and F. Noé, “Equivariant flows: Exact likelihood generative learning for symmetric densities,” in Proceedings of the 37th International Conference on Machine Learning (ICML) , Jul. 2020, pp. 5361–5370
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
V. Kindratenko, D. Mu, Y. Zhan, J. Maloney, S. H. Hashemi, B. Rabe, K. Xu, R. Campbell, J. Peng, and W. Gropp, “HAL: Computer system for scalable deep learning,” in Practice and Experience in Advanced Research Computing (PEARC ’20) , Jul. 2020, pp. 41–48
2020
Later among the works it cites.
A. Zhou, T. Knowles, and C. Finn, “Meta-learning symmetries by reparameterization,” in Proceedings of the 9th International Conference on Learning Representations (ICLR) , May 2021
2021
Closest in time.
N. Dehmamy, Y. Liu, R. Walters, and R. Yu, “Lie algebra convolutional neural networks with automatic symmetry extraction,” 2021. [Online]. Available: https://openreview.net/forum?id=cTQnZPLIohy
2021
Closest in time.
P. de Haan, M. Weiler, T. Cohen, and M. Welling, “Gauge equivariant mesh CNNs: Anisotropic convolutions on geometric graphs,” in Proceedings of the 9th International Conference on Learning Representations (ICLR) , May 2021
2021
Closest in time.
M. Horie, N. Morita, T. Hishinuma, Y. Ihara, and N. Mitsume, “Isometric transformation invariant and equivariant graph convolutional networks,” in Proceedings of the 9th International Conference on Learning Representations (ICLR) , May 2021
2021
Closest in time.
N. Dey, A. Chen, and S. Ghafurian, “Group equivariant generative adversarial networks,” in Proceedings of the 9th International Conference on Learning Representations (ICLR) , May 2021
2021
Closest in time.
2021
Closest in time.
M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu, “Spatial transformer networks,” in Proceedings of the 28th Conference on Neural Information Processing Systems , Dec. 2015, pp. 2017–2025
2025
Closest in time.