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There has been a large amount of interest, both in the past and particularly recently, into the power of different families of universal approximators, e.g.
Rational approximation to | x | |x|
Donald J Newman et al · 1964
Earlier work this paper cites.
Constructive approximation , volume 303
Ronald A DeVore and George G Lorentz · 1993
Earlier work this paper cites.
A fast iterative shrinkage-thresholding algorithm for linear inverse problems
Amir Beck and Marc Teboulle · 2009
Earlier work this paper cites.
Training and testing low-degree polynomial data mappings via linear svm
Yin-Wen Chang, Cho-Jui Hsieh, Kai-Wei Chang, Michael Ringgaard, and Chih-Jen Lin · 2010
Earlier work this paper cites.
Bounded independence fools degree-2 threshold functions
Ilias Diakonikolas, Daniel M Kane, and Jelani Nelson · 2010
Earlier work this paper cites.
Learning kernel-based halfspaces with the 0-1 loss
Shai Shalev-Shwartz, Ohad Shamir, and Karthik Sridharan · 2011
Earlier work this paper cites.
On the computational efficiency of training neural networks
Roi Livni, Shai Shalev-Shwartz, and Ohad Shamir · 2014
Earlier work this paper cites.
The power of depth for feedforward neural networks
Ronen Eldan and Ohad Shamir · 2016
Cited alongside, same era.
Train faster, generalize better: Stability of stochastic gradient descent
Moritz Hardt, Ben Recht, and Yoram Singer · 2016
Cited alongside, same era.
Benefits of depth in neural networks
Matus Telgarsky · 2016
Cited alongside, same era.
Spectrally-normalized margin bounds for neural networks
Peter L Bartlett, Dylan J Foster, and Matus J Telgarsky · 2017
Cited alongside, same era.
Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review
Tomaso Poggio, Hrushikesh Mhaskar, Lorenzo Rosasco, Brando Miranda, and Qianli Liao · 2017
Cited alongside, same era.
High-dimensional statistics
Phillippe Rigollet · 2017
Cited alongside, same era.
Depth-width tradeoffs in approximating natural functions with neural networks
Itay Safran and Ohad Shamir · 2017
Later among the works it cites.
Neural networks and rational functions
Matus Telgarsky · 2017
Later among the works it cites.
Error bounds for approximations with deep relu networks
Dmitry Yarotsky · 2017
Later among the works it cites.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
Later among the works it cites.
Algorithmic aspects of machine learning
Ankur Moitra · 2018
Closest in time.
High-Dimensional Probability
Roman Vershynin · 2018
Closest in time.
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