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We study the properties of various over-parametrized convolutional neural architectures through their respective Gaussian process and neural tangent kernels.
Geometric applications of Fourier series and spherical harmonics
Helmut Groemer · 1996
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Regularization with dot-product kernels
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Kernel methods for deep learning
Youngmin Cho and Lawrence Saul · 2009
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Analytic combinatorics
Philippe Flajolet and Robert Sedgewick · 2009
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Analysis of spherical symmetries in Euclidean spaces
Claus Müller · 2012
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Multi-variable orthogonal polynomials
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Julien Mairal, Piotr Koniusz, Zaid Harchaoui, and Cordelia Schmid · 2014
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Eigenvalues of dot-product kernels on the sphere
Douglas Azevedo and Valdir A Menegatto · 2015
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Karen Simonyan and Andrew Zisserman · 2015
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Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee · 2016
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Understanding the effective receptive field in deep convolutional neural networks
Wenjie Luo, Yujia Li, Raquel Urtasun, and Richard Zemel · 2016
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End-to-end kernel learning with supervised convolutional kernel networks
Julien Mairal · 2016
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Breaking the curse of dimensionality with convex neural networks
Francis Bach · 2017
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Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2017
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Why and when can deep-but not shallow-networks avoid the curse of dimensionality: a review
Tomaso Poggio, Hrushikesh Mhaskar, Lorenzo Rosasco, Brando Miranda, and Qianli Liao · 2017
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Denoising and regularization via exploiting the structural bias of convolutional generators
Reinhard Heckel and Mahdi Soltanolkotabi · 2019
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Multivariate bell polynomials and derivatives of composed functions
Aidan Schumann · 2019
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Frequency bias in neural networks for input of non-uniform density
Ronen Basri, Meirav Galun, Amnon Geifman, David Jacobs, Yoni Kasten, and Shira Kritchman · 2020
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Deep equals shallow for relu networks in kernel regimes
Alberto Bietti and Francis Bach · 2020
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Deep neural tangent kernel and laplace kernel have the same rkhs
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Arthur Jacot, Franck Gabriel, and Clement Hongler · 2018
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Bayesian deep convolutional networks with many channels are gaussian processes
Roman Novak, Lechao Xiao, Jaehoon Lee, Yasaman Bahri, Greg Yang, Jiri Hron, Daniel A Abolafia, Jeffrey Pennington, and Jascha Sohl-Dickstein · 2018
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Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2018
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On exact computation with an infinitely wide neural net
Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov, and Ruosong Wang · 2019
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The convergence rate of neural networks for learned functions of different frequencies
Ronen Basri, David Jacobs, Yoni Kasten, and Shira Kritchman · 2019
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On the inductive bias of neural tangent kernels
Alberto Bietti and Julien Mairal · 2019
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Lin Chen and Sheng Xu · 2020
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Harmonic decompositions of convolutional networks
Meyer Scetbon and Zaid Harchaoui · 2020
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Approximation and learning with deep convolutional models: a kernel perspective
Alberto Bietti · 2021
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On the sample complexity of learning with geometric stability
Alberto Bietti, Luca Venturi, and Joan Bruna · 2021
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Locality defeats the curse of dimensionality in convolutional teacher-student scenarios
Alessandro Favero, Francesco Cagnetta, and Matthieu Wyart · 2021
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Learning with invariances in random features and kernel models
Song Mei, Theodor Misiakiewicz, and Andrea Montanari · 2021
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Learning with convolution and pooling operations in kernel methods
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A spectral analysis of dot-product kernels
Meyer Scetbon and Zaid Harchaoui · 2021
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The neural tangent link between cnn denoisers and non-local filters
Julian Tachella, Junqi Tang, and Mike Davies · 2021
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