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We consider fully connected feed-forward deep neural networks (NNs) where weights and biases are independent and identically distributed as symmetric centered stable distributions.
Traditional and heavy-tailed self regularization in neural network models
Martin, C. H. and Mahoney, M. W. (2019) · 1901
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A tail-index analysis of stochastic gradient noise in deep neural networks
Simsekli, U., Sagun, L., and Gurbuzbalaban, M. (2019) · 1901
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Yang, G. (2019) · 1902
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Bayesian Learning for Neural Networks
Neal, R. M. (1995) · 1995
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On the chambers-mallows-stuck method for simulating skewed stable random variables
Weron, R. (1996) · 1996
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Convergence of Probability Measures
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Beyond gaussian processes: On the distributions of infinite networks
Der, R. and Lee, D. D. (2006) · 2006
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An overview of multivariate stable distributions
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Correction to: ”on the chambers–mallows–stuck method for simulating skewed stable random variables”
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Jacot, A., Gabriel, F., and Hongler, C. (2018) · 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) · 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., and Wang, R. (2019) · 2019
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Deep convolutional networks as shallow gaussian processes
Garriga-Alonso, A., Rasmussen, C. E., and Aitchison, L. (2019) · 2019
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On the impact of the activation function on deep neural networks training
Hayou, S., Doucet, A., and Rousseau, J. (2019) · 2019
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Wide neural networks of any depth evolve as linear models under gradient descent
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Deep information propagation
Schoenholz, S. S., Gilmer, J., Ganguli, S., and Sohl-Dickstein, J. (2017) · 2017
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Gaussian process behaviour in wide deep neural networks
Matthews, A. G. d. G., Hron, J., Rowland, M., Turner, R. E., and Ghahramani, Z. (2018a)
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Gaussian process behaviour in wide deep neural networks
Matthews, A. G. d. G., Rowland, M., Hron, J., Turner, R. E., and Ghahramani, Z. (2018b)
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Lee, J., Xiao, L., Schoenholz, S. S., Bahri, Y., Sohl-Dickstein, J., and Pennington, J. (2019) · 2019
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