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Group equivariance (e.g.
Foundations of differential geometry vol 1 (new york: Interscience) kobayashi s and nomizu k 1969
Kobayashi, S · 1963
Earlier work this paper cites.
A family of embedded runge-kutta formulae
Dormand, J. R. and Prince, P. J · 1980
Earlier work this paper cites.
The design and use of steerable filters
Freeman, W. T., Adelson, E. H., et al · 1991
Earlier work this paper cites.
Stochastic analysis on manifolds
Hsu, E. P · 2002
Earlier work this paper cites.
The rank of connection matrices and the dimension of graph algebras
Lovász, L · 2005
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Riemannian geometry and geometric analysis , volume 42005
Jost, J. and Jost, J · 2008
Earlier work this paper cites.
A kernel two-sample test
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Imagenet classification with deep convolutional neural networks
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Earlier work this paper cites.
Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling, 2013
Landrum, G · 2013
Earlier work this paper cites.
Introduction to mechanics and symmetry: a basic exposition of classical mechanical systems , volume 17
Marsden, J. E. and Ratiu, T. S · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Deep learning and the information bottleneck principle
Tishby, N. and Zaslavsky, N · 2015
Earlier work this paper cites.
Group equivariant convolutional networks
Cohen, T. and Welling, M · 2016
Earlier work this paper cites.
Exploiting cyclic symmetry in convolutional neural networks
Dieleman, S., De Fauw, J., and Kavukcuoglu, K · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Steerable CNNs
Cohen, T. S. and Welling, M · 2017
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2017
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, K., Kindermans, P.-J., Felix, H. E. S., Chmiela, S., Tkatchenko, A., and Müller, K.-R · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
Earlier work this paper cites.
Harmonic networks: Deep translation and rotation equivariance
Worrall, D. E., Garbin, S. J., Turmukhambetov, D., and Brostow, G. J · 2017
Earlier work this paper cites.
Deep sets
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J · 2017
Earlier work this paper cites.
Cohen, T. S., Geiger, M., Köhler, J., and Welling, M · 2018
Earlier work this paper cites.
Neural relational inference for interacting systems
Kipf, T., Fetaya, E., Wang, K.-C., Welling, M., and Zemel, R · 2018
Earlier work this paper cites.
On the generalization of equivariance and convolution in neural networks to the action of compact groups
Kondor, R. and Trivedi, S · 2018
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Clebsch–Gordan nets: a fully Fourier space spherical convolutional neural network
Kondor, R., Lin, Z., and Trivedi, S · 2018
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Invariant and equivariant graph networks
Maron, H., Ben-Hamu, H., Shamir, N., and Lipman, Y · 2018
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Schnet–a deep learning architecture for molecules and materials
Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., and Müller, K.-R · 2018
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Thomas, N., Smidt, T., Kearnes, S., Yang, L., Li, L., Kohlhoff, K., and Riley, P · 2018
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Message passing neural networks
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2020
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Directional message passing for molecular graphs
Klicpera, J., Groß, J., and Günnemann, S · 2020
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On second order behaviour in augmented neural odes
Norcliffe, A., Bodnar, C., Day, B., Simidjievski, N., and Liò, P · 2020
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Attentive group equivariant convolutional networks
Romero, D. W., Bekkers, E. J., Tomczak, J. M., and Hoogendoorn, M · 2020
Later among the works it cites.
Pdo-econvs: Partial differential operator based equivariant convolutions
Shen, Z., He, L., Lin, Z., and Ma, J · 2020
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Bekkers, E. J · 2019
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Cohen, T. S., Geiger, M., and Weiler, M · 2019
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Keriven, N. and Pe yré, G · 2019
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Equivariant flows: sampling configurations for multi-body systems with symmetric energies
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Shi, Y., Huang, Z., Wang, W., Zhong, H., Feng, S., and Sun, Y · 2020
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Song, Y. and Ermon, S · 2020
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Score-based generative modeling through stochastic differential equations
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Adaptive checkpoint adjoint method for gradient estimation in neural ode
Zhuang, J., Dvornek, N., Li, X., Tatikonda, S., Papademetris, X., and Duncan, J · 2020
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Se (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
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