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Global Convergence of Adaptive Gradient Methods for An
  Over-parameterized Neural Network

Global Convergence of Adaptive Gradient Methods for An Over-parameterized Neural Network

19 February 2019
Xiaoxia Wu
S. Du
Rachel A. Ward
ArXivPDFHTML

Papers citing "Global Convergence of Adaptive Gradient Methods for An Over-parameterized Neural Network"

16 / 16 papers shown
Title
Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks
Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks
Matteo Tucat
Anirbit Mukherjee
Procheta Sen
Mingfei Sun
Omar Rivasplata
MLT
39
1
0
12 Apr 2024
Efficient Federated Learning via Local Adaptive Amended Optimizer with
  Linear Speedup
Efficient Federated Learning via Local Adaptive Amended Optimizer with Linear Speedup
Yan Sun
Li Shen
Hao Sun
Liang Ding
Dacheng Tao
FedML
29
17
0
30 Jul 2023
Global Convergence of SGD On Two Layer Neural Nets
Global Convergence of SGD On Two Layer Neural Nets
Pulkit Gopalani
Anirbit Mukherjee
26
5
0
20 Oct 2022
Provable Regret Bounds for Deep Online Learning and Control
Provable Regret Bounds for Deep Online Learning and Control
Xinyi Chen
Edgar Minasyan
Jason D. Lee
Elad Hazan
36
6
0
15 Oct 2021
Deformed semicircle law and concentration of nonlinear random matrices
  for ultra-wide neural networks
Deformed semicircle law and concentration of nonlinear random matrices for ultra-wide neural networks
Zhichao Wang
Yizhe Zhu
37
18
0
20 Sep 2021
The Low-Rank Simplicity Bias in Deep Networks
The Low-Rank Simplicity Bias in Deep Networks
Minyoung Huh
H. Mobahi
Richard Y. Zhang
Brian Cheung
Pulkit Agrawal
Phillip Isola
30
110
0
18 Mar 2021
A Convergence Theory Towards Practical Over-parameterized Deep Neural
  Networks
A Convergence Theory Towards Practical Over-parameterized Deep Neural Networks
Asaf Noy
Yi Tian Xu
Y. Aflalo
Lihi Zelnik-Manor
Rong Jin
41
3
0
12 Jan 2021
Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for
  Deep ReLU Networks
Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks
Quynh N. Nguyen
Marco Mondelli
Guido Montúfar
25
81
0
21 Dec 2020
Training (Overparametrized) Neural Networks in Near-Linear Time
Training (Overparametrized) Neural Networks in Near-Linear Time
Jan van den Brand
Binghui Peng
Zhao Song
Omri Weinstein
ODL
29
82
0
20 Jun 2020
Provable Training of a ReLU Gate with an Iterative Non-Gradient
  Algorithm
Provable Training of a ReLU Gate with an Iterative Non-Gradient Algorithm
Sayar Karmakar
Anirbit Mukherjee
14
7
0
08 May 2020
Adaptive Federated Optimization
Adaptive Federated Optimization
Sashank J. Reddi
Zachary B. Charles
Manzil Zaheer
Zachary Garrett
Keith Rush
Jakub Konecný
Sanjiv Kumar
H. B. McMahan
FedML
58
1,395
0
29 Feb 2020
Convergence of End-to-End Training in Deep Unsupervised Contrastive
  Learning
Convergence of End-to-End Training in Deep Unsupervised Contrastive Learning
Zixin Wen
SSL
21
2
0
17 Feb 2020
Towards Understanding the Spectral Bias of Deep Learning
Towards Understanding the Spectral Bias of Deep Learning
Yuan Cao
Zhiying Fang
Yue Wu
Ding-Xuan Zhou
Quanquan Gu
41
215
0
03 Dec 2019
Overparameterized Neural Networks Implement Associative Memory
Overparameterized Neural Networks Implement Associative Memory
Adityanarayanan Radhakrishnan
M. Belkin
Caroline Uhler
BDL
35
71
0
26 Sep 2019
Fast Convergence of Natural Gradient Descent for Overparameterized
  Neural Networks
Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks
Guodong Zhang
James Martens
Roger C. Grosse
ODL
22
124
0
27 May 2019
Gradient Descent can Learn Less Over-parameterized Two-layer Neural
  Networks on Classification Problems
Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems
Atsushi Nitanda
Geoffrey Chinot
Taiji Suzuki
MLT
16
33
0
23 May 2019
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