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Based on the theory of homogeneous spaces we derive geometrically optimal edge attributes to be used within the flexible message-passing framework.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Quantum chemistry structures and properties of 134 kilo molecules, 2014
Rupp M.and Von Lilienfeld O.A. Ramakrishnan R., Dral P.O · 2014
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New approximation of a scale space kernel on se (3) and applications in neuroimaging
Jorg Portegies, Gonzalo Sanguinetti, Stephan Meesters, and Remco Duits · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Group equivariant convolutional networks
Taco Cohen and Max Welling · 2016
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Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
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Geometric deep learning on graphs and manifolds using mixture model cnns
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Roto-translation covariant convolutional networks for medical image analysis
Erik J Bekkers, Maxime W Lafarge, Mitko Veta, Koen AJ Eppenhof, Josien PW Pluim, and Remco Duits · 2018
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Splinecnn: Fast geometric deep learning with continuous b-spline kernels
Matthias Fey, Jan Eric Lenssen, Frank Weichert, and Heinrich Müller · 2018
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Design and processing of invertible orientation scores of 3d images
Michiel HJ Janssen, Augustus JEM Janssen, Erik J Bekkers, J Oliván Bescós, and Remco Duits · 2018
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Neural relational inference for interacting systems
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, and Richard Zemel · 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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Clebsch–gordan nets: a fully fourier space spherical convolutional neural network
Risi Kondor, Zhen Lin, and Shubhendu Trivedi · 2018
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Graphvae: Towards generation of small graphs using variational autoencoders
Martin Simonovsky and Nikos Komodakis · 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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Learning steerable filters for rotation equivariant cnns
Maurice Weiler, Fred A Hamprecht, and Martin Storath · 2018
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Cubenet: Equivariance to 3d rotation and translation
Daniel Worrall and Gabriel Brostow · 2018
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Cormorant: Covariant molecular neural networks
Brandon Anderson, Truong Son Hy, and Risi Kondor · 2019
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B-spline cnns on lie groups
Erik J Bekkers · 2019
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A general theory of equivariant cnns on homogeneous spaces
Taco S Cohen, Mario Geiger, and Maurice Weiler · 2019
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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Directional message passing for molecular graphs
Johannes Gasteiger, Janek Groß, and Stephan Günnemann · 2019
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Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Niklas Gebauer, Michael Gastegger, and Kristof Schütt · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Scale-equivariant steerable networks
Ivan Sosnovik, Michał Szmaja, and Arnold Smeulders · 2019
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Graph convolutional gaussian processes
Ian Walker and Ben Glocker · 2019
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General e (2)-equivariant steerable cnns
Maurice Weiler and Gabriele Cesa · 2019
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Ckconv: Continuous kernel convolution for sequential data
David W Romero, Anna Kuzina, Erik J Bekkers, Jakub Mikolaj Tomczak, and Mark Hoogendoorn · 2021
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E (n) equivariant graph neural networks
Vıctor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
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Symmetry-aware actor-critic for 3d molecular design
Gregor N. C. Simm, Robert Pinsler, Gábor Csányi, and José Miguel Hernández-Lobato · 2021
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Maximum likelihood training of score-based diffusion models
Yang Song, Conor Durkan, Iain Murray, and Stefano Ermon · 2021
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Scalars are universal: Equivariant machine learning, structured like classical physics
Soledad Villar, David W Hogg, Kate Storey-Fisher, Weichi Yao, and Ben Blum-Smith · 2021
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Superpixel image classification with graph attention networks
Pedro HC Avelar, Anderson R Tavares, Thiago LT da Silveira, Clíudio R Jung, and Luís C Lamb · 2020
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Experiment tracking with weights and biases, 2020
Lukas Biewald · 2020
Cited alongside, same era.
On the role of gradients for machine learning of molecular energies and forces
Anders S Christensen and O Anatole Von Lilienfeld · 2020
Cited alongside, same era.
Gauge equivariant mesh cnns: Anisotropic convolutions on geometric graphs
Pim De Haan, Maurice Weiler, Taco Cohen, and Max Welling · 2020
Cited alongside, same era.
On the universality of rotation equivariant point cloud networks
Nadav Dym and Haggai Maron · 2020
Cited alongside, same era.
Se (3)-transformers: 3d roto-translation equivariant attention networks
Fabian Fuchs, Daniel Worrall, Volker Fischer, and Max Welling · 2020
Cited alongside, same era.
Maurice Weiler, Patrick Forré, Erik Verlinde, and Max Welling · 2021
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E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E Smidt, and Boris Kozinsky · 2022
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e3nn: Euclidean neural networks, 2022
Mario Geiger and Tess Smidt · 2022
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Equivariant diffusion for molecule generation in 3d
Emiel Hoogeboom, Vıctor Garcia Satorras, Clément Vignac, and Max Welling · 2022
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Exploiting redundancy: Separable group convolutional networks on lie groups
David M Knigge, David W Romero, and Erik J Bekkers · 2022
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Informing geometric deep learning with electronic interactions to accelerate quantum chemistry
Zhuoran Qiao, Anders S Christensen, Matthew Welborn, Frederick R Manby, Anima Anandkumar, and Thomas F Miller III · 2022
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Visnet: a scalable and accurate geometric deep learning potential for molecular dynamics simulation
Yusong Wang, Shaoning Li, Xinheng He, Mingyu Li, Zun Wang, Nanning Zheng, Bin Shao, Tong Wang, and Tie-Yan Liu · 2022
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Geodiff: A geometric diffusion model for molecular conformation generation
Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang · 2022
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Mathematical Foundations of Equivariant Neural Networks
Jimmy Aronsson · 2023
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An exploration of conditioning methods in graph neural networks
Yeskendir Koishekenov and Erik J Bekkers · 2023
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Regular se (3) group convolutions for volumetric medical image analysis
Thijs P Kuipers and Erik J Bekkers · 2023
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Learning local equivariant representations for large-scale atomistic dynamics
Albert Musaelian, Simon Batzner, Anders Johansson, Lixin Sun, Cameron J Owen, Mordechai Kornbluth, and Boris Kozinsky · 2023
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Hyena hierarchy: Towards larger convolutional language models
Michael Poli, Stefano Massaroli, Eric Nguyen, Daniel Y Fu, Tri Dao, Stephen Baccus, Yoshua Bengio, Stefano Ermon, and Christopher Ré · 2023
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Clifford group equivariant neural networks
David Ruhe, Johannes Brandstetter, and Patrick Forré · 2023
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Schnetpack 2.0: A neural network toolbox for atomistic machine learning
Kristof T Schütt, Stefaan SP Hessmann, Niklas WA Gebauer, Jonas Lederer, and Michael Gastegger · 2023
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Midi: Mixed graph and 3d denoising diffusion for molecule generation
Clement Vignac, Nagham Osman, Laura Toni, and Pascal Frossard · 2023
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Equivariant and Coordinate Independent Convolutional Networks
Maurice Weiler, Patrick Forré, Erik Verlinde, and Max Welling · 2023
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