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Convergence of stochastic gradient descent under a local Lojasiewicz
  condition for deep neural networks
v1v2 (latest)

Convergence of stochastic gradient descent under a local Lojasiewicz condition for deep neural networks

18 April 2023
Jing An
Jianfeng Lu
ArXiv (abs)PDFHTML

Papers citing "Convergence of stochastic gradient descent under a local Lojasiewicz condition for deep neural networks"

5 / 5 papers shown
Title
ODE approximation for the Adam algorithm: General and overparametrized setting
ODE approximation for the Adam algorithm: General and overparametrized setting
Steffen Dereich
Arnulf Jentzen
Sebastian Kassing
61
0
0
06 Nov 2025
Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks
Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks
Bangti Jin
Longjun Wu
98
1
0
29 Aug 2025
From Sublinear to Linear: Fast Convergence in Deep Networks via Locally Polyak-Lojasiewicz Regions
From Sublinear to Linear: Fast Convergence in Deep Networks via Locally Polyak-Lojasiewicz Regions
Agnideep Aich
Ashit Aich
Bruce Wade
120
1
0
29 Jul 2025
Convergence of continuous-time stochastic gradient descent with applications to deep neural networks
Convergence of continuous-time stochastic gradient descent with applications to deep neural networks
Gabor Lugosi
Eulalia Nualart
233
1
0
11 Sep 2024
Generalization Performance of Empirical Risk Minimization on
  Over-parameterized Deep ReLU Nets
Generalization Performance of Empirical Risk Minimization on Over-parameterized Deep ReLU NetsIEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2021
Shao-Bo Lin
Yao Wang
Ding-Xuan Zhou
ODL
292
5
0
28 Nov 2021
1