ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2006.02409
  4. Cited By
On the Promise of the Stochastic Generalized Gauss-Newton Method for
  Training DNNs
v1v2v3v4 (latest)

On the Promise of the Stochastic Generalized Gauss-Newton Method for Training DNNs

3 June 2020
Matilde Gargiani
Andrea Zanelli
Moritz Diehl
Katharina Eggensperger
    ODL
ArXiv (abs)PDFHTML

Papers citing "On the Promise of the Stochastic Generalized Gauss-Newton Method for Training DNNs"

12 / 12 papers shown
Incremental Gauss-Newton Descent for Machine Learning
Incremental Gauss-Newton Descent for Machine Learning
Mikalai Korbit
Mario Zanon
ODL
229
1
0
10 Aug 2024
Exact Gauss-Newton Optimization for Training Deep Neural Networks
Exact Gauss-Newton Optimization for Training Deep Neural Networks
Mikalai Korbit
Adeyemi Damilare Adeoye
Alberto Bemporad
Mario Zanon
ODL
391
8
0
23 May 2024
Thermodynamic Natural Gradient Descent
Thermodynamic Natural Gradient Descent
Kaelan Donatella
Samuel Duffield
Maxwell Aifer
Denis Melanson
Gavin Crooks
Patrick J. Coles
194
5
0
22 May 2024
Dynamic Anisotropic Smoothing for Noisy Derivative-Free Optimization
Dynamic Anisotropic Smoothing for Noisy Derivative-Free OptimizationInternational Conference on Machine Learning (ICML), 2024
S. Reifenstein
T. Leleu
Yoshihisa Yamamoto
274
3
0
02 May 2024
A Selective Review on Statistical Methods for Massive Data Computation:
  Distributed Computing, Subsampling, and Minibatch Techniques
A Selective Review on Statistical Methods for Massive Data Computation: Distributed Computing, Subsampling, and Minibatch Techniques
Xuetong Li
Yuan Gao
Hong Chang
Danyang Huang
Yingying Ma
...
Ke Xu
Jing Zhou
Xuening Zhu
Yingqiu Zhu
Hansheng Wang
220
18
0
17 Mar 2024
Dual Gauss-Newton Directions for Deep Learning
Dual Gauss-Newton Directions for Deep Learning
Vincent Roulet
Mathieu Blondel
ODL
200
0
0
17 Aug 2023
Sophia: A Scalable Stochastic Second-order Optimizer for Language Model
  Pre-training
Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-trainingInternational Conference on Learning Representations (ICLR), 2023
Hong Liu
Zhiyuan Li
David Leo Wright Hall
Abigail Z. Jacobs
Tengyu Ma
VLM
697
257
0
23 May 2023
Achieving High Accuracy with PINNs via Energy Natural Gradients
Achieving High Accuracy with PINNs via Energy Natural GradientsInternational Conference on Machine Learning (ICML), 2023
Johannes Müller
Marius Zeinhofer
381
12
0
25 Feb 2023
Efficient first-order predictor-corrector multiple objective
  optimization for fair misinformation detection
Efficient first-order predictor-corrector multiple objective optimization for fair misinformation detection
Eric Enouen
Katja Mathesius
Sean Wang
Arielle K. Carr
Sihong Xie
115
2
0
15 Sep 2022
PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method
  with Probabilistic Gradient Estimation
PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient EstimationInternational Conference on Machine Learning (ICML), 2022
Matilde Gargiani
Andrea Zanelli
Andrea Martinelli
Tyler H. Summers
John Lygeros
180
17
0
01 Feb 2022
Inexact bilevel stochastic gradient methods for constrained and
  unconstrained lower-level problems
Inexact bilevel stochastic gradient methods for constrained and unconstrained lower-level problems
Tommaso Giovannelli
G. Kent
Luis Nunes Vicente
374
16
0
01 Oct 2021
ViViT: Curvature access through the generalized Gauss-Newton's low-rank
  structure
ViViT: Curvature access through the generalized Gauss-Newton's low-rank structure
Felix Dangel
Lukas Tatzel
Philipp Hennig
233
14
0
04 Jun 2021
1
Page 1 of 1