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Optimization Methods for Large-Scale Machine Learning
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Optimization Methods for Large-Scale Machine Learning

Léon Bottou
Frank E. Curtis
J. Nocedal
ArXiv (abs)PDFHTML

Papers citing "Optimization Methods for Large-Scale Machine Learning"

16 / 866 papers shown
Title
Stochastic Newton and Quasi-Newton Methods for Large Linear Least-squares Problems
Stochastic Newton and Quasi-Newton Methods for Large Linear Least-squares Problems
Julianne Chung
Matthias Chung
J. T. Slagel
L. Tenorio
56
11
0
23 Feb 2017
On SGD's Failure in Practice: Characterizing and Overcoming Stalling
On SGD's Failure in Practice: Characterizing and Overcoming Stalling
V. Patel
46
1
0
01 Feb 2017
Stochastic Subsampling for Factorizing Huge Matrices
Stochastic Subsampling for Factorizing Huge Matrices
A. Mensch
Julien Mairal
Bertrand Thirion
Gaël Varoquaux
72
30
0
19 Jan 2017
Towards Principled Methods for Training Generative Adversarial Networks
Towards Principled Methods for Training Generative Adversarial Networks
Martín Arjovsky
M. Nault
GAN
87
2,112
0
17 Jan 2017
Stochastic Generative Hashing
Stochastic Generative Hashing
Bo Dai
Ruiqi Guo
Sanjiv Kumar
Niao He
Le Song
TPM
103
107
0
11 Jan 2017
Coupling Adaptive Batch Sizes with Learning Rates
Coupling Adaptive Batch Sizes with Learning Rates
Lukas Balles
Javier Romero
Philipp Hennig
ODL
159
110
0
15 Dec 2016
Federated Optimization: Distributed Machine Learning for On-Device Intelligence
Federated Optimization: Distributed Machine Learning for On-Device Intelligence
Jakub Konecný
H. B. McMahan
Daniel Ramage
Peter Richtárik
FedML
184
1,914
0
08 Oct 2016
Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite-Sum Structure
Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite-Sum Structure
A. Bietti
Julien Mairal
209
36
0
04 Oct 2016
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
559
2,948
0
15 Sep 2016
Benchmarking State-of-the-Art Deep Learning Software Tools
Benchmarking State-of-the-Art Deep Learning Software Tools
Shaoshuai Shi
Qiang-qiang Wang
Pengfei Xu
Xiaowen Chu
BDL
135
330
0
25 Aug 2016
DOOMED: Direct Online Optimization of Modeling Errors in Dynamics
DOOMED: Direct Online Optimization of Modeling Errors in Dynamics
Nathan D. Ratliff
Franziska Meier
Daniel Kappler
S. Schaal
85
17
0
01 Aug 2016
Tradeoffs between Convergence Speed and Reconstruction Accuracy in Inverse Problems
Tradeoffs between Convergence Speed and Reconstruction Accuracy in Inverse Problems
Raja Giryes
Yonina C. Eldar
A. Bronstein
Guillermo Sapiro
72
85
0
30 May 2016
FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods
FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods
Xiang Cheng
Farbod Roosta-Khorasani
Stefan Palombo
Peter L. Bartlett
Michael W. Mahoney
ODL
42
0
0
26 May 2016
A Multi-Batch L-BFGS Method for Machine Learning
A Multi-Batch L-BFGS Method for Machine Learning
A. Berahas
J. Nocedal
Martin Takáč
ODL
112
112
0
19 May 2016
The Proximal Robbins-Monro Method
The Proximal Robbins-Monro Method
Panos Toulis
Thibaut Horel
E. Airoldi
59
30
0
04 Oct 2015
Automatic differentiation in machine learning: a survey
Automatic differentiation in machine learning: a survey
A. G. Baydin
Barak A. Pearlmutter
Alexey Radul
J. Siskind
PINNAI4CEODL
196
2,839
0
20 Feb 2015