Documentation | Code | CHANGELOG | Colab
The aim of this library is to help the development of E(3) equivariant neural networks. It contains fundamental mathematical operations such as tensor products and spherical harmonics.
Important: install pytorch and only then run the command
pip install --upgrade pip
pip install --upgrade e3nn
For details and optional dependencies, see INSTALL.md
e3nn is under development. It is recommanded to install using pip. The main branch is considered as unstable. The second version number is incremented every time a breaking change is made to the code.
0.(increment when backwards incompatible release).(increment for backwards compatible release)
We are happy to help! The best way to get help on e3nn is to submit a Question or Bug Report.
If you want to get involved in and contribute to the development, improvement, and application of e3nn, introduce yourself in the discussions.
Our community abides by the Contributor Covenant Code of Conduct.
- Euclidean Neural Networks
@misc{thomas2018tensorfieldnetworksrotation,
title={Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds},
author={Nathaniel Thomas and Tess Smidt and Steven Kearnes and Lusann Yang and Li Li and Kai Kohlhoff and Patrick Riley},
year={2018},
eprint={1802.08219},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/1802.08219},
}
@misc{weiler20183dsteerablecnnslearning,
title={3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data},
author={Maurice Weiler and Mario Geiger and Max Welling and Wouter Boomsma and Taco Cohen},
year={2018},
eprint={1807.02547},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/1807.02547},
}
@misc{kondor2018clebschgordannetsfullyfourier,
title={Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network},
author={Risi Kondor and Zhen Lin and Shubhendu Trivedi},
year={2018},
eprint={1806.09231},
archivePrefix={arXiv},
primaryClass={stat.ML},
url={https://arxiv.org/abs/1806.09231},
}
- e3nn
@misc{e3nn_paper,
doi = {10.48550/ARXIV.2207.09453},
url = {https://arxiv.org/abs/2207.09453},
author = {Geiger, Mario and Smidt, Tess},
keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Neural and Evolutionary Computing (cs.NE), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {e3nn: Euclidean Neural Networks},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
@software{e3nn,
author = {Mario Geiger and
Tess Smidt and
Alby M. and
Benjamin Kurt Miller and
Wouter Boomsma and
Bradley Dice and
Kostiantyn Lapchevskyi and
Maurice Weiler and
Michał Tyszkiewicz and
Simon Batzner and
Dylan Madisetti and
Martin Uhrin and
Jes Frellsen and
Nuri Jung and
Sophia Sanborn and
Mingjian Wen and
Josh Rackers and
Marcel Rød and
Michael Bailey},
title = {Euclidean neural networks: e3nn},
month = apr,
year = 2022,
publisher = {Zenodo},
version = {0.5.0},
doi = {10.5281/zenodo.6459381},
url = {https://doi.org/10.5281/zenodo.6459381}
}
Euclidean neural networks (e3nn) Copyright (c) 2020, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy), Ecole Polytechnique Federale de Lausanne (EPFL), Free University of Berlin and Kostiantyn Lapchevskyi. All rights reserved.
If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Intellectual Property Office at [email protected].
NOTICE. This Software was developed under funding from the U.S. Department of Energy and the U.S. Government consequently retains certain rights. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform publicly and display publicly, and to permit others to do so.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent