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2006.05924
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Sketchy Empirical Natural Gradient Methods for Deep Learning
10 June 2020
Minghan Yang
Dong Xu
Zaiwen Wen
Mengyun Chen
Pengxiang Xu
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Papers citing
"Sketchy Empirical Natural Gradient Methods for Deep Learning"
10 / 10 papers shown
A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms
Gil Goldshlager
Jiang Hu
Lin Lin
161
0
0
28 Aug 2025
Beyond the Mean: Fisher-Orthogonal Projection for Natural Gradient Descent in Large Batch Training
Yishun Lu
Wesley Armour
ODL
550
2
0
19 Aug 2025
MKOR: Momentum-Enabled Kronecker-Factor-Based Optimizer Using Rank-1 Updates
Neural Information Processing Systems (NeurIPS), 2023
Mohammad Mozaffari
Sikan Li
Zhao Zhang
M. Dehnavi
275
6
0
02 Jun 2023
ASDL: A Unified Interface for Gradient Preconditioning in PyTorch
Kazuki Osawa
Satoki Ishikawa
Rio Yokota
Shigang Li
Torsten Hoefler
ODL
212
20
0
08 May 2023
Brand New K-FACs: Speeding up K-FAC with Online Decomposition Updates
C. Puiu
239
2
0
16 Oct 2022
Riemannian Natural Gradient Methods
Jiang Hu
Ruicheng Ao
Anthony Man-Cho So
Minghan Yang
Zaiwen Wen
263
15
0
15 Jul 2022
Randomized K-FACs: Speeding up K-FAC with Randomized Numerical Linear Algebra
Ideal (IDEAL), 2022
C. Puiu
315
3
0
30 Jun 2022
Rethinking Exponential Averaging of the Fisher
C. Puiu
256
3
0
10 Apr 2022
NG+ : A Multi-Step Matrix-Product Natural Gradient Method for Deep Learning
Minghan Yang
Dong Xu
Qiwen Cui
Zaiwen Wen
Pengxiang Xu
192
4
0
14 Jun 2021
Eigenvalue-corrected Natural Gradient Based on a New Approximation
Kai-Xin Gao
Xiaolei Liu
Zheng-Hai Huang
Min Wang
Shuangling Wang
Zidong Wang
Dachuan Xu
F. Yu
ODL
183
7
0
27 Nov 2020
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