Fetching the paper…
Reading the bibliography…
Neural networks in the lazy training regime converge to kernel machines.
Effect of batch learning in multilayer neural networks
Kenji Fukumizu · 1998
Earlier work this paper cites.
Learning with kernels : support vector machines, regularization, optimization, and beyond
Bernhard Schölkopf and Alexander J. Smola · 2002
Earlier work this paper cites.
Gaussian processes for machine learning
Carl Edward Rasmussen and Christopher K. I. Williams · 2006
Earlier work this paper cites.
The peano-baker series
Michael Baake and Ulrike Schlaegel · 2011
Earlier work this paper cites.
Algorithms for learning kernels based on centered alignment
Corinna Cortes, Mehryar Mohri, and Afshin Rostamizadeh · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Exact solutions to the nonlinear dynamics of learning in deep linear neural network
Andrew M. Saxe, James L. McClelland, and Surya Ganguli · 2014
Earlier work this paper cites.
Finite dimensional linear systems
Roger W Brockett · 2015
Earlier work this paper cites.
Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 2016
Earlier work this paper cites.
Wide residual networks, 2017
Sergey Zagoruyko and Nikos Komodakis · 2017
Earlier work this paper cites.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
Earlier work this paper cites.
On the optimization of deep networks: Implicit acceleration by overparameterization
Sanjeev Arora, Nadav Cohen, and Elad Hazan · 2018
Earlier work this paper cites.
To understand deep learning we need to understand kernel learning
Mikhail Belkin, Siyuan Ma, and Soumik Mandal · 2018
Earlier work this paper cites.
Algorithmic regularization in learning deep homogeneous models: Layers are automatically balanced
Simon Shaolei Du, Wei Hu, and J. Lee · 2018
Earlier work this paper cites.
Neural tangent kernel: convergence and generalization in neural networks (invited paper)
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
Cited alongside, same era.
Just interpolate: Kernel ”ridgeless” regression can generalize
Tengyuan Liang and Alexander Rakhlin · 2018
Cited alongside, same era.
On exact computation with an infinitely wide neural net
Sanjeev Arora, Simon Shaolei Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov, and Ruosong Wang · 2019
Cited alongside, same era.
On lazy training in differentiable programming
Lénaïc Chizat, Edouard Oyallon, and Francis R. Bach · 2019
Cited alongside, same era.
Wide neural networks of any depth evolve as linear models under gradient descent
Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, and Jascha Sohl-Dickstein · 2019
Cited alongside, same era.
Finite versus infinite neural networks: an empirical study, 2020
Jaehoon Lee, Samuel S. Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, and Jascha Sohl-Dickstein · 2020
Later among the works it cites.
On the linearity of large non-linear models: when and why the tangent kernel is constant
Chaoyue Liu, Libin Zhu, and Misha Belkin · 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.
Kernel and rich regimes in overparametrized models
Blake Woodworth, Suriya Gunasekar, Jason D. Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, and Nathan Srebro · 2020
Later among the works it cites.
A unifying view on implicit bias in training linear neural networks
Chulhee Yun, Shankar Krishnan, and Hossein Mobahi · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
On the asymptotics of wide networks with polynomial activations
Kyle Aitken and Guy Gur-Ari · 2020
Cited alongside, same era.
Asymptotics of wide convolutional neural networks
Anders Johan Andreassen and Ethan Dyer · 2020
Cited alongside, same era.
Taylorized training: Towards better approximation of neural network training at finite width, 2020
Yu Bai, Ben Krause, Huan Wang, Caiming Xiong, and Richard Socher · 2020
Cited alongside, same era.
Spectrum dependent learning curves in kernel regression and wide neural networks
Blake Bordelon, Abdulkadir Canatar, and Cengiz Pehlevan · 2020
Cited alongside, same era.
Label-aware neural tangent kernel: Toward better generalization and local elasticity, 2020
Shuxiao Chen, Hangfeng He, and Weijie J. Su · 2020
Cited alongside, same era.
Separability and geometry of object manifolds in deep neural networks
Uri Cohen, SueYeon Chung, Daniel D Lee, and Haim Sompolinsky · 2020
Cited alongside, same era.
Every model learned by gradient descent is approximately a kernel machine
Pedro Domingos · 2020
Cited alongside, same era.
Implicit regularization via neural feature alignment
Aristide Baratin, Thomas George, César Laurent, R. Devon Hjelm, Guillaume Lajoie, Pascal Vincent, and Simon Lacoste-Julien · 2021
Closest in time.
Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks
Abdulkadir Canatar, Blake Bordelon, and Cengiz Pehlevan · 2021
Closest in time.
Rich and lazy learning of task representations in brains and neural networks
Timo Flesch, Keno Juechems, Tsvetomira Dumbalska, Andrew Saxe, and Christopher Summerfield · 2021
Closest in time.
Landscape and training regimes in deep learning
Mario Geiger, Leonardo Petrini, and Matthieu Wyart · 2021
Closest in time.
Deep linear networks dynamics: Low-rank biases induced by initialization scale and l2 regularization
Arthur Jacot, François Ged, Franck Gabriel, Berfin Şimşek, and Clément Hongler · 2021
Closest in time.
Bruno Loureiro, Cédric Gerbelot, Hugo Cui, Sebastian Goldt, Florent Krzakala, Marc Mézard, and Lenka Zdeborová · 2021
Closest in time.
Rapid feature evolution accelerates learning in neural networks, 2021
Haozhe Shan and Blake Bordelon · 2021
Closest in time.
Dominik Stöger and Mahdi Soltanolkotabi · 2021
Closest in time.