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Lee, J., Xiao, L., Schoenholz, S. S., Bahri, Y., Novak, R., Sohl-Dickstein, J., & Pennington, J. (n.d.). wide neural networks of any depth evolve as linear models under gradient descent*This article is an updated version of a paper presented at 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada.. Journal of statistical mechanics, , . http://access.bl.uk/ark:/81055/vdc_100117431966.0x00004c