Learning Lipschitz Functions by GD-trained Shallow Overparameterized ReLU Neural Networks

28 December 2022

Papers citing "Learning Lipschitz Functions by GD-trained Shallow Overparameterized ReLU Neural Networks"

32 / 32 papers shown

Title
Improved Convergence Guarantees for Shallow Neural Networks A. Razborov ODL 51 1 0 05 Dec 2022
Learning Single-Index Models with Shallow Neural Networks A. Bietti Joan Bruna Clayton Sanford M. Song 179 68 0 27 Oct 2022
Neural Networks can Learn Representations with Gradient Descent Alexandru Damian Jason D. Lee Mahdi Soltanolkotabi SSL MLT 74 115 0 30 Jun 2022
Deep Network Approximation in Terms of Intrinsic Parameters Zuowei Shen Haizhao Yang Shijun Zhang 38 9 0 15 Nov 2021
Subquadratic Overparameterization for Shallow Neural Networks Chaehwan Song Ali Ramezani-Kebrya Thomas Pethick Armin Eftekhari Volkan Cevher 56 31 0 02 Nov 2021
A spectral-based analysis of the separation between two-layer neural networks and linear methods Lei Wu Jihao Long 99 8 0 10 Aug 2021
Deep learning: a statistical viewpoint Peter L. Bartlett Andrea Montanari Alexander Rakhlin 27 272 0 16 Mar 2021
Online nonparametric regression with Sobolev kernels O. Zadorozhnyi Pierre Gaillard Sébastien Gerchinovitz Alessandro Rudi 26 4 0 06 Feb 2021
On the Proof of Global Convergence of Gradient Descent for Deep ReLU Networks with Linear Widths Quynh N. Nguyen 60 48 0 24 Jan 2021
Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks Quynh N. Nguyen Marco Mondelli Guido Montúfar 40 82 0 21 Dec 2020
Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network Tianyang Hu Wei Cao Cong Lin Guang Cheng 100 52 0 06 Jul 2020
Analyzing the discrepancy principle for kernelized spectral filter learning algorithms Alain Celisse Martin Wahl 30 18 0 17 Apr 2020
Over-parametrized deep neural networks do not generalize well Michael Kohler A. Krzyżak 28 12 0 09 Dec 2019
Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks Ziwei Ji Matus Telgarsky 50 177 0 26 Sep 2019
On the Inductive Bias of Neural Tangent Kernels A. Bietti Julien Mairal 49 255 0 29 May 2019
Towards moderate overparameterization: global convergence guarantees for training shallow neural networks Samet Oymak Mahdi Soltanolkotabi 43 320 0 12 Feb 2019
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruosong Wang MLT 108 966 0 24 Jan 2019
Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon Alexander Rakhlin Xiyu Zhai 95 79 0 28 Dec 2018
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers Zeyuan Allen-Zhu Yuanzhi Li Yingyu Liang MLT 106 769 0 12 Nov 2018
A Convergence Theory for Deep Learning via Over-Parameterization Zeyuan Allen-Zhu Yuanzhi Li Zhao Song AI4CE ODL 170 1,457 0 09 Nov 2018
Gradient Descent Provably Optimizes Over-parameterized Neural Networks S. Du Xiyu Zhai Barnabás Póczós Aarti Singh MLT ODL 130 1,261 0 04 Oct 2018
Does data interpolation contradict statistical optimality? M. Belkin Alexander Rakhlin Alexandre B. Tsybakov 60 218 0 25 Jun 2018
Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot Franck Gabriel Clément Hongler 159 3,160 0 20 Jun 2018
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes Loucas Pillaud-Vivien Alessandro Rudi Francis R. Bach 71 100 0 25 May 2018
Size-Independent Sample Complexity of Neural Networks Noah Golowich Alexander Rakhlin Ohad Shamir 79 547 0 18 Dec 2017
Spectrally-normalized margin bounds for neural networks Peter L. Bartlett Dylan J. Foster Matus Telgarsky ODL 120 1,208 0 26 Jun 2017
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity Amit Daniely Roy Frostig Y. Singer 77 343 0 18 Feb 2016
Generalization Properties of Learning with Random Features Alessandro Rudi Lorenzo Rosasco MLT 58 329 0 14 Feb 2016
Breaking the Curse of Dimensionality with Convex Neural Networks Francis R. Bach 102 701 0 30 Dec 2014
Non-parametric Stochastic Approximation with Large Step sizes Aymeric Dieuleveut Francis R. Bach 39 169 0 02 Aug 2014
Early stopping and non-parametric regression: An optimal data-dependent stopping rule Garvesh Raskutti Martin J. Wainwright Bin Yu 57 299 0 15 Jun 2013
Optimistic Rates for Learning with a Smooth Loss Nathan Srebro Karthik Sridharan Ambuj Tewari 124 281 0 20 Sep 2010