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Recent works have revealed that infinitely-wide feed-forward or recurrent neural networks of any architecture correspond to Gaussian processes referred to as Neural Network Gaussian Processes (NNGPs).
Scale mixtures of normal distributions
D. F. Andrews and C. L. Mallows · 1974
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Priors for infinite networks
R. M. Neal · 1996
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Variational learning of inducing variables in sparse Gaussian processes
M. Titsias · 2009
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Student-t processes as alternatives to Gaussian processes
A. Shah, A. Wilson, and Z. Ghahramani · 2014
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Kullback-leibler divergence for the normal-gamma distribution
Joram Soch and Carsten Allefeld · 2016
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Sample-then-optimize posterior sampling for Bayesian linear models
A. G. de G. Matthews, J. Hron, R. E. Turner, and Z. Ghahramani · 2017
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JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
Earlier work this paper cites.
Neural tangent kernel: convergence and generalization in neural networks
A. Jacot, F. Gabriel, and C. Hongler · 2018
Cited alongside, same era.
Deep neural networks as Gaussian processes
Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S Schoenholz, Jeffrey Pennington, and Jascha Sohl-Dickstein · 2018
Cited alongside, same era.
Gaussian process behaviour in wide deep neural networks
Alexander G de G Matthews, Mark Rowland, Jiri Hron, Richard E Turner, and Zoubin Ghahramani · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Deep convolutional networks as shallow Gaussian processes
Adrià Garriga-Alonso, Carl Edward Rasmussen, and Laurence Aitchison · 2019
Cited alongside, same era.
Wide neural networks of any depth evolve as linear models under gradient descent
Greg Yang · 2019
Later among the works it cites.
Exploring the uncertainty properties of neural networks’ implicit priors in the infinite-width limit
Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington, and Jasper Snoek · 2020
Later among the works it cites.
Stable behaviour of infinitely wide deep neural networks
S. Favaro, S. Fortini, and S. Peluchetti · 2020
Later among the works it cites.
Infinite attention: NNGP and NTK for deep attention networks
Jiri Hron, Yasaman Bahri, Jascha Sohl-Dickstein, and Roman Novak · 2020
Later among the works it cites.
Neural tangents: Fast and easy infinite neural networks in python
Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, and Samuel S. Schoenholz · 2020
Later among the works it cites.
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J. Lee, L. Xiao, S. S. Schoenholz, Y. Bahri, R. Novak, J. Sohl-Dickstein, and J. Pennington · 2019
Cited alongside, same era.
Infinite-channel deep stable convolutional neural networks
D. Bracale, S. Favaro, S. Fortini, and S. Peluchetti · 2021
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