Fetching the paper…
Reading the bibliography…
In 1988, Eric B.
On the capabilities of multilayer perceptrons
Eric B Baum · 1988
Earlier work this paper cites.
Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
Earlier work this paper cites.
Universal approximation bounds for superpositions of a sigmoidal function
Andrew R. Barron · 1993
Earlier work this paper cites.
Multilayer feedforward networks with a nonpolynomial activation function can approximate any function
Moshe Leshno, Vladimir Ya Lin, Allan Pinkus, and Shimon Schocken · 1993
Earlier work this paper cites.
Reconstructing a neural net from its output
Charles Fefferman · 1994
Earlier work this paper cites.
Gaussian Hilbert spaces , volume 129
Svante Janson · 1997
Earlier work this paper cites.
The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network
Peter L Bartlett · 1998
Earlier work this paper cites.
Convex neural networks
Yoshua Bengio, Nicolas L Roux, Pascal Vincent, Olivier Delalleau, and Patrice Marcotte · 2006
Earlier work this paper cites.
Agnostically learning halfspaces
Adam Tauman Kalai, Adam R Klivans, Yishay Mansour, and Rocco A Servedio · 2008
Earlier work this paper cites.
Learning polynomials with neural networks
Alexandr Andoni, Rina Panigrahy, Gregory Valiant, and Li Zhang · 2014
Earlier work this paper cites.
Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
Earlier work this paper cites.
Norm-based capacity control in neural networks
Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro · 2015
Earlier work this paper cites.
Breaking the curse of dimensionality with convex neural networks
Francis Bach · 2017
Earlier work this paper cites.
Spectrally-normalized margin bounds for neural networks
Peter L Bartlett, Dylan J Foster, and Matus J Telgarsky · 2017
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
Cited alongside, same era.
On the global convergence of gradient descent for over-parameterized models using optimal transport
Lenaic Chizat and Francis Bach · 2018
Cited alongside, same era.
Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
Cited alongside, same era.
Learning overparameterized neural networks via stochastic gradient descent on structured data
Yuanzhi Li and Yingyu Liang · 2018
Cited alongside, same era.
A mean field view of the landscape of two-layer neural networks
Limitations of lazy training of two-layers neural network
Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, and Andrea Montanari · 2019
Later among the works it cites.
Gradient descent finds global minima for generalizable deep neural networks of practical sizes
Kenji Kawaguchi and Jiaoyang Huang · 2019
Later among the works it cites.
Samet Oymak and Mahdi Soltanolkotabi · 2019
Later among the works it cites.
How do infinite width bounded norm networks look in function space?
Pedro Savarese, Itay Evron, Daniel Soudry, and Nathan Srebro · 2019
Later among the works it cites.
Quadratic suffices for over-parametrization via matrix chernoff bound
Zhao Song and Xin Yang · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Song Mei, Andrea Montanari, and Phan-Minh Nguyen · 2018
Cited alongside, same era.
Theoretical insights into the optimization landscape of over-parameterized shallow neural networks
Mahdi Soltanolkotabi, Adel Javanmard, and Jason D Lee · 2018
Cited alongside, same era.
A convergence theory for deep learning via over-parameterization
Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song · 2019
Cited alongside, same era.
Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks
Sanjeev Arora, Simon Du, Wei Hu, Zhiyuan Li, and Ruosong Wang · 2019
Cited alongside, same era.
On lazy training in differentiable programming
Lenaic Chizat, Edouard Oyallon, and Francis Bach · 2019
Cited alongside, same era.
Neural networks learning and memorization with (almost) no over-parameterization
Amit Daniely · 2019
Cited alongside, same era.
Gradient descent finds global minima of deep neural networks
Simon Du, Jason Lee, Haochuan Li, Liwei Wang, and Xiyu Zhai · 2019
Cited alongside, same era.
On the power and limitations of random features for understanding neural networks
Gilad Yehudai and Ohad Shamir · 2019
Later among the works it cites.
Small relu networks are powerful memorizers: a tight analysis of memorization capacity
Chulhee Yun, Suvrit Sra, and Ali Jadbabaie · 2019
Later among the works it cites.
A corrective view of neural networks: Representation, memorization and learning
Guy Bresler and Dheeraj Nagaraj · 2020
Closest in time.
Memorizing gaussians with no over-parameterizaion via gradient decent on neural networks
Amit Daniely · 2020
Closest in time.
Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks
Ziwei Ji and Matus Telgarsky · 2020
Closest in time.
Neural tangent kernels, transportation mappings, and universal approximation
Ziwei Ji, Matus Telgarsky, and Ruicheng Xian · 2020
Closest in time.
A function space view of bounded norm infinite width relu nets: The multivariate case
Greg Ongie, Rebecca Willett, Daniel Soudry, and Nathan Srebro · 2020
Closest in time.