Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective

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2008

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2012

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2014

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2018

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2018

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Jaehoon Lee, Jascha Sohl-Dickstein, Jeffrey Pennington, Roman Novak, Sam Schoenholz, and Yasaman Bahri, Deep neural networks as gaussian processes, in International Conference on Learning Representations (2018)

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2018

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Nasim Rahaman, Devansh Arpit, Aristide Baratin, Felix Dräxler, Min Lin, Fred A. Hamprecht, Yoshua Bengio, and Aaron C. Courville, On the spectral bias of deep neural networks, arXiv e-prints (2018) , arXiv:1806.08734

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2018

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2018

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Roman Novak, Lechao Xiao, Jaehoon Lee, Yasaman Bahri, Greg Yang, Daniel A. Abolafia, Jeffrey Pennington, and Jascha Sohl-Dickstein, Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes, arXiv e-prints (2018), arXiv:1810.05148 [stat.ML]

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2018

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2019

Closest in time.

Lenka Zdeborová, Understanding deep learning is also a job for physicists, Nature Physics 16

2020

Closest in time.

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Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, and Samuel S. Schoenholz, Neural tangents: Fast and easy infinite neural networks in python, in International Conference on Learning Representations (2020)

2020

Closest in time.