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Incorporating equivariance as an inductive bias into deep learning architectures to take advantage of the data symmetry has been successful in multiple applications, such as chemistry and dynamical systems.
Augmentation for small object detection, 2019
Mate Kisantal, Zbigniew Wojna, Jakub Murawski, Jacek Naruniec, and Kyunghyun Cho · 1902
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Data augmentation for object detection via progressive and selective instance-switching, 2019
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Erratum: Spontaneous symmetry breaking in single and molecular quantum dots [phys. rev. lett. 82, 5325 (1999)]
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Carnegie mellon motion capture database
CMU · 2003
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Group equivariant convolutional networks
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Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
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Hiroshi Inoue · 2018
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Revisiting Data Augmentation for Rotational Invariance in Convolutional Neural Networks , page 127–141
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Minimal model of cellular symmetry breaking
Alexander Mietke, V. Jemseena, K. Vijay Kumar, Ivo F. Sbalzarini, and Frank Jülicher · 2019
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General E(2)-Equivariant Steerable CNNs
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A simple rotational equivariance loss for generic convolutional segmentation networks: preliminary results
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Se(3)-transformers: 3d roto-translation equivariant attention networks
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Directional message passing for molecular graphs
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E(n) equivariant graph neural networks
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Highly accurate protein structure prediction with alphafold
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Equivariant message passing for the prediction of tensorial properties and molecular spectra
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Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković · 2021
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Direct differentiable augmentation search
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Diverse data augmentation for learning image segmentation with cross-modality annotations
X Chen, C Lian, L Wang, H Deng, T Kuang, SH Fung, J Gateno, D Shen, JJ Xia, and PT Yap · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
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Residual pathway priors for soft equivariance constraints
Marc Finzi, Gregory Benton, and Andrew G Wilson · 2021
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Slaw: Scaled loss approximate weighting for efficient multi-task learning, 2021
Michael Crawshaw and Jana Košecká · 2021
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Rotationally equivariant 3d object detection
Hong-Xing Yu, Jiajun Wu, and Li Yi · 2022
Scale-rotation-equivariant lie group convolution neural networks (lie group-cnns), 2023
Wei-Dong Qiao, Yang Xu, and Hui Li · 2023
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Almost equivariance via lie algebra convolutions
Daniel McNeela · 2023
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Learning layer-wise equivariances automatically using gradients
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Equivariant graph hierarchy-based neural networks
Jiaqi Han, Wenbing Huang, Tingyang Xu, and Yu Rong · 2022
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Geometric and physical quantities improve e(3) equivariant message passing
Johannes Brandstetter, Rob Hesselink, Elise van der Pol, Erik J Bekkers, and Max Welling · 2022
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Equivariant transformers for neural network based molecular potentials
Philipp Thölke and Gianni De Fabritiis · 2022
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E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
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MACE: Higher order equivariant message passing neural networks for fast and accurate force fields
Ilyes Batatia, David Peter Kovacs, Gregor N. C. Simm, Christoph Ortner, and Gabor Csanyi · 2022
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Frame averaging for invariant and equivariant network design
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Sangnie Bhardwaj, Willie McClinton, Tongzhou Wang, Guillaume Lajoie, Chen Sun, Phillip Isola, and Dilip Krishnan · 2023
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Equi-tuning: Group equivariant fine-tuning of pretrained models
Sourya Basu, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Vijil Chenthamarakshan, Kush R. Varshney, Lav R. Varshney, and Payel Das · 2023
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Generative adversarial symmetry discovery
Jianke Yang, Robin Walters, Nima Dehmamy, and Rose Yu · 2023
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Pre-training via denoising for molecular property prediction
Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter Battaglia, Razvan Pascanu, and Jonathan Godwin · 2023
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Equivariant graph neural operator for modeling 3d dynamics
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An explicit frame construction for normalizing 3d point clouds
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