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Convergence of gradient descent for learning linear neural networks

Convergence of gradient descent for learning linear neural networks

4 August 2021
Gabin Maxime Nguegnang
Holger Rauhut
Ulrich Terstiege
    MLT
ArXivPDFHTML

Papers citing "Convergence of gradient descent for learning linear neural networks"

14 / 14 papers shown
Title
Gradient Descent Converges Linearly to Flatter Minima than Gradient Flow in Shallow Linear Networks
Gradient Descent Converges Linearly to Flatter Minima than Gradient Flow in Shallow Linear Networks
Pierfrancesco Beneventano
Blake Woodworth
MLT
34
1
0
15 Jan 2025
Convergence of continuous-time stochastic gradient descent with
  applications to linear deep neural networks
Convergence of continuous-time stochastic gradient descent with applications to linear deep neural networks
Gabor Lugosi
Eulalia Nualart
13
0
0
11 Sep 2024
Lecture Notes on Linear Neural Networks: A Tale of Optimization and
  Generalization in Deep Learning
Lecture Notes on Linear Neural Networks: A Tale of Optimization and Generalization in Deep Learning
Nadav Cohen
Noam Razin
31
0
0
25 Aug 2024
How do Transformers perform In-Context Autoregressive Learning?
How do Transformers perform In-Context Autoregressive Learning?
Michael E. Sander
Raja Giryes
Taiji Suzuki
Mathieu Blondel
Gabriel Peyré
32
7
0
08 Feb 2024
Geometry of Linear Neural Networks: Equivariance and Invariance under Permutation Groups
Geometry of Linear Neural Networks: Equivariance and Invariance under Permutation Groups
Kathlén Kohn
Anna-Laura Sattelberger
V. Shahverdi
25
3
0
24 Sep 2023
Asymmetric matrix sensing by gradient descent with small random
  initialization
Asymmetric matrix sensing by gradient descent with small random initialization
J. S. Wind
38
1
0
04 Sep 2023
Robust Implicit Regularization via Weight Normalization
Robust Implicit Regularization via Weight Normalization
H. Chou
Holger Rauhut
Rachel A. Ward
28
7
0
09 May 2023
Function Space and Critical Points of Linear Convolutional Networks
Function Space and Critical Points of Linear Convolutional Networks
Kathlén Kohn
Guido Montúfar
V. Shahverdi
Matthew Trager
16
11
0
12 Apr 2023
Critical Points and Convergence Analysis of Generative Deep Linear
  Networks Trained with Bures-Wasserstein Loss
Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss
Pierre Bréchet
Katerina Papagiannouli
Jing An
Guido Montúfar
23
3
0
06 Mar 2023
Side Effects of Learning from Low-dimensional Data Embedded in a
  Euclidean Space
Side Effects of Learning from Low-dimensional Data Embedded in a Euclidean Space
Juncai He
R. Tsai
Rachel A. Ward
36
8
0
01 Mar 2022
Continuous vs. Discrete Optimization of Deep Neural Networks
Continuous vs. Discrete Optimization of Deep Neural Networks
Omer Elkabetz
Nadav Cohen
62
44
0
14 Jul 2021
Global optimality conditions for deep neural networks
Global optimality conditions for deep neural networks
Chulhee Yun
S. Sra
Ali Jadbabaie
121
117
0
08 Jul 2017
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp
  Minima
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
N. Keskar
Dheevatsa Mudigere
J. Nocedal
M. Smelyanskiy
P. T. P. Tang
ODL
281
2,888
0
15 Sep 2016
Linear Convergence of Gradient and Proximal-Gradient Methods Under the
  Polyak-Łojasiewicz Condition
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition
Hamed Karimi
J. Nutini
Mark W. Schmidt
133
1,198
0
16 Aug 2016
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