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Stochastic gradient descent with noise of machine learning type. Part
  II: Continuous time analysis

Stochastic gradient descent with noise of machine learning type. Part II: Continuous time analysis

4 June 2021
Stephan Wojtowytsch
ArXivPDFHTML

Papers citing "Stochastic gradient descent with noise of machine learning type. Part II: Continuous time analysis"

14 / 14 papers shown
Title
Sharpness-Aware Minimization Efficiently Selects Flatter Minima Late in Training
Sharpness-Aware Minimization Efficiently Selects Flatter Minima Late in Training
Zhanpeng Zhou
Mingze Wang
Yuchen Mao
Bingrui Li
Junchi Yan
AAML
62
0
0
14 Oct 2024
Stochastic Modified Flows for Riemannian Stochastic Gradient Descent
Stochastic Modified Flows for Riemannian Stochastic Gradient Descent
Benjamin Gess
Sebastian Kassing
Nimit Rana
40
0
0
02 Feb 2024
Correlated Noise in Epoch-Based Stochastic Gradient Descent:
  Implications for Weight Variances
Correlated Noise in Epoch-Based Stochastic Gradient Descent: Implications for Weight Variances
Marcel Kühn
B. Rosenow
19
3
0
08 Jun 2023
mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization
mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization
Kayhan Behdin
Qingquan Song
Aman Gupta
S. Keerthi
Ayan Acharya
Borja Ocejo
Gregory Dexter
Rajiv Khanna
D. Durfee
Rahul Mazumder
AAML
18
7
0
19 Feb 2023
Stochastic Modified Flows, Mean-Field Limits and Dynamics of Stochastic
  Gradient Descent
Stochastic Modified Flows, Mean-Field Limits and Dynamics of Stochastic Gradient Descent
Benjamin Gess
Sebastian Kassing
Vitalii Konarovskyi
DiffM
32
6
0
14 Feb 2023
Toward Equation of Motion for Deep Neural Networks: Continuous-time
  Gradient Descent and Discretization Error Analysis
Toward Equation of Motion for Deep Neural Networks: Continuous-time Gradient Descent and Discretization Error Analysis
Taiki Miyagawa
50
9
0
28 Oct 2022
From Gradient Flow on Population Loss to Learning with Stochastic
  Gradient Descent
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent
Satyen Kale
Jason D. Lee
Chris De Sa
Ayush Sekhari
Karthik Sridharan
27
4
0
13 Oct 2022
SGD with Large Step Sizes Learns Sparse Features
SGD with Large Step Sizes Learns Sparse Features
Maksym Andriushchenko
Aditya Varre
Loucas Pillaud-Vivien
Nicolas Flammarion
45
56
0
11 Oct 2022
Identical Image Retrieval using Deep Learning
Identical Image Retrieval using Deep Learning
Sayan Nath
Nikhil Nayak
VLM
34
1
0
10 May 2022
A Continuous-time Stochastic Gradient Descent Method for Continuous Data
A Continuous-time Stochastic Gradient Descent Method for Continuous Data
Kexin Jin
J. Latz
Chenguang Liu
Carola-Bibiane Schönlieb
23
9
0
07 Dec 2021
What Happens after SGD Reaches Zero Loss? --A Mathematical Framework
What Happens after SGD Reaches Zero Loss? --A Mathematical Framework
Zhiyuan Li
Tianhao Wang
Sanjeev Arora
MLT
90
98
0
13 Oct 2021
Stochastic gradient descent with noise of machine learning type. Part I:
  Discrete time analysis
Stochastic gradient descent with noise of machine learning type. Part I: Discrete time analysis
Stephan Wojtowytsch
25
50
0
04 May 2021
Convergence of stochastic gradient descent schemes for
  Lojasiewicz-landscapes
Convergence of stochastic gradient descent schemes for Lojasiewicz-landscapes
Steffen Dereich
Sebastian Kassing
34
27
0
16 Feb 2021
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
308
2,890
0
15 Sep 2016
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