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Scalable Second Order Optimization for Deep Learning
v1v2 (latest)

Scalable Second Order Optimization for Deep Learning

20 February 2020
Rohan Anil
Vineet Gupta
Tomer Koren
Kevin Regan
Y. Singer
    ODL
ArXiv (abs)PDFHTML

Papers citing "Scalable Second Order Optimization for Deep Learning"

21 / 21 papers shown
Fundamentals of Regression
Fundamentals of Regression
Miguel A. Mendez
AI4CE
85
0
0
27 Nov 2025
FUSE: First-Order and Second-Order Unified SynthEsis in Stochastic Optimization
FUSE: First-Order and Second-Order Unified SynthEsis in Stochastic OptimizationConference on Algebraic Informatics (AI), 2025
Zhanhong Jiang
Md Zahid Hasan
Aditya Balu
Joshua R. Waite
Genyi Huang
Soumik Sarkar
285
0
0
06 Mar 2025
PETScML: Second-order solvers for training regression problems in
  Scientific Machine Learning
PETScML: Second-order solvers for training regression problems in Scientific Machine LearningPlatform for Advanced Scientific Computing Conference (PASC), 2024
Stefano Zampini
Umberto Zerbinati
George Turkyyiah
David E. Keyes
257
6
0
18 Mar 2024
Scalable Stochastic Gradient Riemannian Langevin Dynamics in
  Non-Diagonal Metrics
Scalable Stochastic Gradient Riemannian Langevin Dynamics in Non-Diagonal Metrics
Hanlin Yu
M. Hartmann
Bernardo Williams
Arto Klami
BDL
475
9
0
09 Mar 2023
VeLO: Training Versatile Learned Optimizers by Scaling Up
VeLO: Training Versatile Learned Optimizers by Scaling Up
Luke Metz
James Harrison
C. Freeman
Amil Merchant
Lucas Beyer
...
Naman Agrawal
Ben Poole
Igor Mordatch
Adam Roberts
Jascha Narain Sohl-Dickstein
362
78
0
17 Nov 2022
Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities
Compute-Efficient Deep Learning: Algorithmic Trends and OpportunitiesJournal of machine learning research (JMLR), 2022
Brian Bartoldson
B. Kailkhura
Davis W. Blalock
448
68
0
13 Oct 2022
On the Factory Floor: ML Engineering for Industrial-Scale Ads
  Recommendation Models
On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models
Rohan Anil
S. Gadanho
Danya Huang
Nijith Jacob
Zhuoshu Li
...
Cristina Pop
Kevin Regan
G. Shamir
Rakesh Shivanna
Qiqi Yan
3DV
345
51
0
12 Sep 2022
Hessian-Free Second-Order Adversarial Examples for Adversarial Learning
Hessian-Free Second-Order Adversarial Examples for Adversarial Learning
Yaguan Qian
Yu-qun Wang
Bin Wang
Zhaoquan Gu
Yu-Shuang Guo
Wassim Swaileh
AAML
336
4
0
04 Jul 2022
Practical tradeoffs between memory, compute, and performance in learned
  optimizers
Practical tradeoffs between memory, compute, and performance in learned optimizers
Luke Metz
C. Freeman
James Harrison
Niru Maheswaranathan
Jascha Narain Sohl-Dickstein
517
39
0
22 Mar 2022
Local Quadratic Convergence of Stochastic Gradient Descent with Adaptive
  Step Size
Local Quadratic Convergence of Stochastic Gradient Descent with Adaptive Step Size
Adityanarayanan Radhakrishnan
M. Belkin
Caroline Uhler
ODL
123
0
0
30 Dec 2021
Real-time Neural Radiance Caching for Path Tracing
Real-time Neural Radiance Caching for Path Tracing
Thomas Müller
Fabrice Rousselle
Jan Novák
A. Keller
3DHAI4CE
371
215
0
23 Jun 2021
A Generalizable Approach to Learning Optimizers
A Generalizable Approach to Learning Optimizers
Diogo Almeida
Clemens Winter
Jie Tang
Wojciech Zaremba
AI4CE
393
35
0
02 Jun 2021
AsymptoticNG: A regularized natural gradient optimization algorithm with
  look-ahead strategy
AsymptoticNG: A regularized natural gradient optimization algorithm with look-ahead strategy
Zedong Tang
Fenlong Jiang
Junke Song
Maoguo Gong
Hao Li
F. Yu
Zidong Wang
Min Wang
ODL
174
1
0
24 Dec 2020
Second-order Neural Network Training Using Complex-step Directional
  Derivative
Second-order Neural Network Training Using Complex-step Directional Derivative
Siyuan Shen
Tianjia Shao
Kun Zhou
Jian Ren
Feng Luo
Yin Yang
ODL
163
4
0
15 Sep 2020
Descending through a Crowded Valley - Benchmarking Deep Learning
  Optimizers
Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers
Robin M. Schmidt
Frank Schneider
Philipp Hennig
ODL
916
195
0
03 Jul 2020
Training (Overparametrized) Neural Networks in Near-Linear Time
Training (Overparametrized) Neural Networks in Near-Linear Time
Jan van den Brand
Binghui Peng
Zhao Song
Omri Weinstein
ODL
356
84
0
20 Jun 2020
Daydream: Accurately Estimating the Efficacy of Optimizations for DNN
  Training
Daydream: Accurately Estimating the Efficacy of Optimizations for DNN Training
Hongyu Zhu
Amar Phanishayee
Gennady Pekhimenko
384
66
0
05 Jun 2020
PyHessian: Neural Networks Through the Lens of the Hessian
PyHessian: Neural Networks Through the Lens of the Hessian
Z. Yao
A. Gholami
Kurt Keutzer
Michael W. Mahoney
ODL
489
367
0
16 Dec 2019
Zap Q-Learning With Nonlinear Function Approximation
Zap Q-Learning With Nonlinear Function ApproximationNeural Information Processing Systems (NeurIPS), 2019
Shuhang Chen
Adithya M. Devraj
Fan Lu
Ana Bušić
Sean P. Meyn
253
24
0
11 Oct 2019
An Adaptive Remote Stochastic Gradient Method for Training Neural
  Networks
An Adaptive Remote Stochastic Gradient Method for Training Neural Networks
Yushu Chen
Hao Jing
Wenlai Zhao
Zhiqiang Liu
Haohuan Fu
Lián Qiao
Wei Xue
Guangwen Yang
ODL
625
2
0
04 May 2019
OverSketched Newton: Fast Convex Optimization for Serverless Systems
OverSketched Newton: Fast Convex Optimization for Serverless Systems
Vipul Gupta
S. Kadhe
T. Courtade
Michael W. Mahoney
Kannan Ramchandran
286
35
0
21 Mar 2019
1
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