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Steerable convolutional neural networks (SCNNs) enhance task performance by modelling geometric symmetries through equivariance constraints on weights.
Die vollständigkeit der primitiven darstellungen einer geschlossenen kontinuierlichen gruppe
Peter, F. and Weyl, H · 1927
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On information and sufficiency
Kullback, S. and Leibler, R. A · 1951
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Axo-somatic and axo-dendritic synapses of the cerebral cortex: an electron microscope study
Gray, E. G · 1959
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Convolutional networks for images, speech, and time series
LeCun, Y., Bengio, Y., et al · 1995
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Salient features of synaptic organisation in the cerebral cortex
Somogyi, P., Tamas, G., Lujan, R., and Buhl, E. H · 1998
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Guidelines for management of small pulmonary nodules detected on ct scans: a statement from the fleischner society
MacMahon, H., Austin, J. H., Gamsu, G., Herold, C. J., Jett, J. R., Naidich, D. P., Patz Jr, E. F., and Swensen, S. J · 2005
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The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans
Armato III, S. G., McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., et al · 2011
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The gray area between synapse structure and function—gray’s synapse types i and ii revisited
Klemann, C. J. and Roubos, E. W · 2011
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The postsynaptic organization of synapses
Sheng, M. and Kim, E · 2011
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Evaluation of individuals with pulmonary nodules: When is it lung cancer?: Diagnosis and management of lung cancer: American college of chest physicians evidence-based clinical practice guidelines
Gould, M. K., Donington, J., Lynch, W. R., Mazzone, P. J., Midthun, D. E., Naidich, D. P., and Wiener, R. S · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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British thoracic society guidelines for the investigation and management of pulmonary nodules: accredited by nice
Callister, M., Baldwin, D., Akram, A., Barnard, S., Cane, P., Draffan, J., Franks, K., Gleeson, F., Graham, R., Malhotra, P., et al · 2015
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Lung nodules: size still matters
Larici, A. R., Farchione, A., Franchi, P., Ciliberto, M., Cicchetti, G., Calandriello, L., Del Ciello, A., and Bonomo, L · 2017
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Harmonic networks: Deep translation and rotation equivariance
Worrall, D. E., Garbin, S. J., Turmukhambetov, D., and Brostow, G. J · 2017
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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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Bekkers, E. J · 2019
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General E(2)-equivariant steerable CNNs
Weiler, M. and Cesa, G · 2019
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Efficient multiple organ localization in ct image using 3D region proposal network
A functional approach to rotation equivariant non-linearities for tensor field networks
Poulenard, A. and Guibas, L. J · 2021
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Weiler, M., Forré, P., Verlinde, E., and Welling, M · 2021
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A program to build E(n)-equivariant steerable CNNs
Cesa, G., Lang, L., and Weiler, M · 2022
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Learning partial equivariances from data
Romero, D. W. and Lohit, S · 2022
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Relaxing equivariance constraints with non-stationary continuous filters
van der Ouderaa, T., Romero, D. W., and van der Wilk, M · 2022
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Xu, X., Zhou, F., Liu, B., Fu, D., and Bai, X · 2019
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Generalizing convolutional neural networks for equivariance to lie groups on arbitrary continuous data
Finzi, M., Stanton, S., Izmailov, P., and Wilson, A. G · 2020
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phiflow: A differentiable pde solving framework for deep learning via physical simulations
Holl, P., Koltun, V., Um, K., and Thuerey, N · 2020
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A wigner-eckart theorem for group equivariant convolution kernels
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MDP homomorphic networks: Group symmetries in reinforcement learning
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Automatic symmetry discovery with lie algebra convolutional network
Dehmamy, N., Walters, R., Liu, Y., Wang, D., and Yu, R · 2021
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Bekkers, E. J., Vadgama, S., Hesselink, R. D., van der Linden, P. A., and Romero, D. W · 2023
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The liver tumor segmentation benchmark (lits)
Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., et al · 2023
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Regular SE(3) group convolutions for volumetric medical image analysis
Kuipers, T. P. and Bekkers, E. J · 2023
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Equivariance-aware architectural optimization of neural networks
Maile, K., Wilson, D. G., and Forré, P · 2023
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Approximation-generalization trade-offs under (approximate) group equivariance
Petrache, M. and Trivedi, S · 2023
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Learning layer-wise equivariances automatically using gradients
van der Ouderaa, T. F., Immer, A., and van der Wilk, M · 2023
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A general theory of correct, incorrect, and extrinsic equivariance
Wang, D., Zhu, X., Park, J. Y., Platt, R., and Walters, R · 2023
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Medmnist v2-a large-scale lightweight benchmark for 2D and 3D biomedical image classification
Yang, J., Shi, R., Wei, D., Liu, Z., Zhao, L., Ke, B., Pfister, H., and Ni, B · 2023
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