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We show that deep ensembles become equivariant for all inputs and at all training times by simply using data augmentation.
Dynamics of Deep Neural Networks and Neural Tangent Hierarchy
Huang, J. and Yau, H.-T · 1909
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Improved generalization bounds of group invariant / equivariant deep networks via quotient feature spaces
Sannai, A., Imaizumi, M., and Kawano, M · 1910
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General E(2)-Equivariant Steerable CNNs
Weiler, M. and Cesa, G · 1911
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Bayesian Learning for Neural Networks
Neal, R. M · 1996
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Feature Learning in Infinite-Width Neural Networks
Yang, G. and Hu, E. J · 2011
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Learning with Group Invariant Features: A Kernel Perspective
Mroueh, Y., Voinea, S., and Poggio, T. A · 2015
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
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Local Group Invariant Representations via Orbit Embeddings
Raj, A., Kumar, A., Mroueh, Y., Fletcher, T., and Schoelkopf, B · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Roto-Translation Covariant Convolutional Networks for Medical Image Analysis
Bekkers, E. J., Lafarge, M. W., Veta, M., Eppenhof, K. A. J., Pluim, J. P. W., and Duits, R · 2018
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Neural Tangent Kernel: Convergence and Generalization in Neural Networks
Jacot, A., Gabriel, F., and Hongler, C · 2018
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100,000 histological images of human colorectal cancer and healthy tissue
Kather, J. N., Halama, N., and Marx, A · 2018
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Deep Neural Networks as Gaussian Processes
Lee, J., Bahri, Y., Novak, R., Schoenholz, S. S., Pennington, J., and Sohl-Dickstein, J · 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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On Exact Computation with an Infinitely Wide Neural Net
Arora, S., Du, S. S., Hu, W., Li, Z., Salakhutdinov, R. R., and Wang, R · 2019
Cited alongside, same era.
A Kernel Theory of Modern Data Augmentation
Dao, T., Gu, A., Ratner, A., Smith, V., Sa, C. D., and Re, C · 2019
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Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
Du, S. S., Hou, K., Póczos, B., Salakhutdinov, R., Wang, R., and Xu, K · 2019
Cited alongside, same era.
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent
Lee, J., Xiao, L., Schoenholz, S., Bahri, Y., Novak, R., Sohl-Dickstein, J., and Pennington, J · 2019
Cited alongside, same era.
Enhanced Convolutional Neural Tangent Kernels
Li, Z., Wang, R., Yu, D., Du, S. S., Hu, W., Salakhutdinov, R., and Arora, S · 2019
Cited alongside, same era.
A program to build E(N)-equivariant steerable CNNs
Cesa, G., Lang, L., and Weiler, M · 2022
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Nonperturbative renormalization for the neural network-QFT correspondence
Erbin, H., Lahoche, V., and Samary, D. O · 2022
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A Neural Tangent Kernel Perspective of GANs
Franceschi, J.-Y., Bézenac, E. D., Ayed, I., Chen, M., Lamprier, S., and Gallinari, P · 2022
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Ensemble deep learning: A review
Ganaie, M. A., Hu, M., Malik, A. K., Tanveer, M., and Suganthan, P. N · 2022
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Equivariance versus Augmentation for Spherical Images
Gerken, J. E., Carlsson, O., Linander, H., Ohlsson, F., Petersson, C., and Persson, D · 2022
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Neural Tangent Kernel: A Survey
Golikov, E., Pokonechnyy, E., and Korviakov, V · 2022
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Novak, R., Xiao, L., Hron, J., Lee, J., Alemi, A. A., Sohl-Dickstein, J., and Schoenholz, S. S · 2020
Cited alongside, same era.
Saib, W., Sengeh, D., Dlamini, G., and Singh, E · 2020
Cited alongside, same era.
Non-Gaussian processes and neural networks at finite widths
Yaida, S · 2020
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Yang, G · 2020
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Provably Strict Generalisation Benefit for Equivariant Models
Elesedy, B. and Zaidi, S · 2021
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Neural Networks and Quantum Field Theory
Halverson, J., Maiti, A., and Stoner, K · 2021
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Learning with invariances in random features and kernel models
Mei, S., Misiakiewicz, T., and Montanari, A · 2021
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Few-shot Backdoor Attacks via Neural Tangent Kernels
Hayase, J. and Oh, S · 2022
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Optimization Dynamics of Equivariant and Augmented Neural Networks
Flinth, A. and Ohlsson, F · 2023
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Geometric deep learning and equivariant neural networks
Gerken, J. E., Aronsson, J., Carlsson, O., Linander, H., Ohlsson, F., Petersson, C., and Persson, D · 2023
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Equivariance with learned canonicalization functions
Kaba, S.-O., Mondal, A. K., Zhang, Y., Bengio, Y., and Ravanbakhsh, S · 2023
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Equivariant adaptation of large pre-trained models
Mondal, A. K., Panigrahi, S. S., Kaba, S.-O., Rajeswar, S., and Ravanbakhsh, S · 2023
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Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies
Ruffolo, J. A., Chu, L.-S., Mahajan, S. P., and Gray, J. J · 2023
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On the Neural Tangent Kernel Analysis of Randomly Pruned Neural Networks
Yang, H. and Wang, Z · 2023
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