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Recent work has shown that the prior over functions induced by a deep Bayesian neural network (BNN) behaves as a Gaussian process (GP) as the width of all layers becomes large.
Yang, G · 1902
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Central limit theorems for interchangeable processes
Blum, J. R., Chernoff, H., Rosenblatt, M., and Teicher, H · 1958
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Probability and Measure
Billingsley, P · 1986
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Bayesian Learning for Neural Networks
Neal, R. M · 1996
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Gaussian processes for machine learning , volume 1
Rasmussen, C. E. and Williams, C. K · 2006
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Theory of statistics
Schervish, M. J · 2012
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Jacot, A., Gabriel, F., and Hongler, C · 2018
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Deep neural networks as Gaussian processes
Lee, J., Sohl-dickstein, J., Pennington, J., Novak, R., Schoenholz, S., and Bahri, Y · 2018
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Gaussian process behaviour in wide deep neural networks
Matthews, A. G., Rowland, M., Hron, J., Turner, R. E., and Ghahramani, Z · 2018
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Garriga-Alonso, A., Rasmussen, C. E., and Aitchison, L · 2019
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Wide neural networks of any depth evolve as linear models under gradient descent
Lee, J., Xiao, L., Schoenholz, S. S., Bahri, Y., Novak, R., Sohl-Dickstein, J., and Pennington, J · 2019
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Bayesian deep convolutional networks with many channels are Gaussian processes
Novak, R., Xiao, L., Bahri, Y., Lee, J., Yang, G., Hron, J., Abolafia, D. A., Pennington, J., and Sohl-Dickstein, J · 2019
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Infinite attention: NNGP and NTK for deep attention networks
Hron, J., Bahri, Y., Sohl-Dickstein, J., and Novak, R · 2020
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On the infinite width limit of neural networks with a standard parameterization
Sohl-Dickstein, J., Novak, R., Schoenholz, S. S., and Lee, J · 2020
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Yang, G
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