Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales
Search
Open menu
Home
Papers
1606.04838
Cited By
v1
v2
v3 (latest)
Optimization Methods for Large-Scale Machine Learning
15 June 2016
Léon Bottou
Frank E. Curtis
J. Nocedal
Re-assign community
ArXiv (abs)
PDF
HTML
Papers citing
"Optimization Methods for Large-Scale Machine Learning"
50 / 1,491 papers shown
Unified Optimal Analysis of the (Stochastic) Gradient Method
Sebastian U. Stich
246
127
0
09 Jul 2019
Learning joint lesion and tissue segmentation from task-specific hetero-modal datasets
International Conference on Medical Imaging with Deep Learning (MIDL), 2019
Reuben Dorent
Wenqi Li
J. Ekanayake
Sebastien Ourselin
Tom Vercauteren
142
4
0
07 Jul 2019
ReLU Networks as Surrogate Models in Mixed-Integer Linear Programs
Computers and Chemical Engineering (Comput. Chem. Eng.), 2019
B. Grimstad
H. Andersson
221
152
0
06 Jul 2019
Precision annealing Monte Carlo methods for statistical data assimilation and machine learning
Physical Review Research (PRR), 2019
Zheng Fang
Adrian S. Wong
Kangbo Hao
Alexander J. A. Ty
H. Abarbanel
122
1
0
06 Jul 2019
Variance Reduction for Matrix Games
Y. Carmon
Yujia Jin
Aaron Sidford
Kevin Tian
284
74
0
03 Jul 2019
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses
Ulysse Marteau-Ferey
Francis R. Bach
Alessandro Rudi
206
39
0
03 Jul 2019
The Role of Memory in Stochastic Optimization
Conference on Uncertainty in Artificial Intelligence (UAI), 2019
Antonio Orvieto
Jonas Köhler
Aurelien Lucchi
202
32
0
02 Jul 2019
Network-accelerated Distributed Machine Learning Using MLFabric
Raajay Viswanathan
Aditya Akella
AI4CE
114
4
0
30 Jun 2019
Combining Stochastic Adaptive Cubic Regularization with Negative Curvature for Nonconvex Optimization
Journal of Optimization Theory and Applications (JOTA), 2019
Seonho Park
Seung Hyun Jung
P. Pardalos
ODL
145
17
0
27 Jun 2019
A Review on Deep Learning in Medical Image Reconstruction
Journal of the Operations Research Society of China (JORSC), 2019
Hai-Miao Zhang
Bin Dong
MedIm
386
150
0
23 Jun 2019
A Unifying Framework for Variance Reduction Algorithms for Finding Zeroes of Monotone Operators
Xun Zhang
W. Haskell
Z. Ye
184
3
0
22 Jun 2019
Fully Decoupled Neural Network Learning Using Delayed Gradients
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2019
Huiping Zhuang
Yi Wang
Qinglai Liu
Shuai Zhang
Zhiping Lin
FedML
173
33
0
21 Jun 2019
Accelerating Mini-batch SARAH by Step Size Rules
Information Sciences (Inf. Sci.), 2019
Zhuang Yang
Zengping Chen
Cheng-Yu Wang
243
15
0
20 Jun 2019
A Survey of Optimization Methods from a Machine Learning Perspective
IEEE Transactions on Cybernetics (IEEE Trans. Cybern.), 2019
Shiliang Sun
Zehui Cao
Han Zhu
Jing Zhao
226
631
0
17 Jun 2019
Optimizing Pipelined Computation and Communication for Latency-Constrained Edge Learning
IEEE Communications Letters (IEEE Commun. Lett.), 2019
N. Skatchkovsky
Osvaldo Simeone
132
18
0
11 Jun 2019
Stochastic In-Face Frank-Wolfe Methods for Non-Convex Optimization and Sparse Neural Network Training
Paul Grigas
Alfonso Lobos
Nathan Vermeersch
187
5
0
09 Jun 2019
Practical Deep Learning with Bayesian Principles
Neural Information Processing Systems (NeurIPS), 2019
Kazuki Osawa
S. Swaroop
Anirudh Jain
Runa Eschenhagen
Richard Turner
Rio Yokota
Mohammad Emtiyaz Khan
BDL
UQCV
447
267
0
06 Jun 2019
Efficient Subsampled Gauss-Newton and Natural Gradient Methods for Training Neural Networks
Yi Ren
Shiqian Ma
146
40
0
05 Jun 2019
On the Convergence of SARAH and Beyond
International Conference on Artificial Intelligence and Statistics (AISTATS), 2019
Bingcong Li
Meng Ma
G. Giannakis
220
31
0
05 Jun 2019
Approximate Inference Turns Deep Networks into Gaussian Processes
Neural Information Processing Systems (NeurIPS), 2019
Mohammad Emtiyaz Khan
Alexander Immer
Ehsan Abedi
M. Korzepa
UQCV
BDL
393
130
0
05 Jun 2019
The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis
Cynthia Rudin
David Carlson
HAI
178
39
0
04 Jun 2019
A Generic Acceleration Framework for Stochastic Composite Optimization
Neural Information Processing Systems (NeurIPS), 2019
A. Kulunchakov
Julien Mairal
375
46
0
03 Jun 2019
Scaling Up Quasi-Newton Algorithms: Communication Efficient Distributed SR1
International Conference on Machine Learning, Optimization, and Data Science (MOD), 2019
Majid Jahani
M. Nazari
S. Rusakov
A. Berahas
Martin Takávc
275
16
0
30 May 2019
Limitations of the Empirical Fisher Approximation for Natural Gradient Descent
Neural Information Processing Systems (NeurIPS), 2019
Frederik Kunstner
Lukas Balles
Philipp Hennig
493
249
0
29 May 2019
An Inertial Newton Algorithm for Deep Learning
Journal of machine learning research (JMLR), 2019
Camille Castera
Jérôme Bolte
Cédric Févotte
Edouard Pauwels
PINN
ODL
281
70
0
29 May 2019
Where is the Information in a Deep Neural Network?
Alessandro Achille
Giovanni Paolini
Stefano Soatto
396
91
0
29 May 2019
Sample Complexity of Sample Average Approximation for Conditional Stochastic Optimization
SIAM Journal on Optimization (SIOPT), 2019
Yifan Hu
Xin Chen
Niao He
261
39
0
28 May 2019
Recursive Estimation for Sparse Gaussian Process Regression
Manuel Schürch
Dario Azzimonti
A. Benavoli
Marco Zaffalon
175
39
0
28 May 2019
Finite-Sample Analysis of Nonlinear Stochastic Approximation with Applications in Reinforcement Learning
Zaiwei Chen
Sheng Zhang
Thinh T. Doan
John-Paul Clarke
S. T. Maguluri
361
67
0
27 May 2019
Robustness of accelerated first-order algorithms for strongly convex optimization problems
IEEE Transactions on Automatic Control (IEEE TAC), 2019
Hesameddin Mohammadi
Meisam Razaviyayn
M. Jovanović
223
47
0
27 May 2019
Decentralized Bayesian Learning over Graphs
Anusha Lalitha
Xinghan Wang
O. Kilinc
Y. Lu
T. Javidi
F. Koushanfar
FedML
203
27
0
24 May 2019
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models: Extension
Neural Information Processing Systems (NeurIPS), 2019
Yunfei Teng
Wenbo Gao
F. Chalus
A. Choromańska
Shiqian Ma
Adrian Weller
499
14
0
24 May 2019
Blockwise Adaptivity: Faster Training and Better Generalization in Deep Learning
Shuai Zheng
James T. Kwok
ODL
167
5
0
23 May 2019
MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Sampling
International Conference on Intelligent Cloud Computing (ICICC), 2019
Jianyu Wang
Anit Kumar Sahu
Zhouyi Yang
Gauri Joshi
S. Kar
358
176
0
23 May 2019
Adaptive norms for deep learning with regularized Newton methods
Jonas Köhler
Leonard Adolphs
Aurelien Lucchi
ODL
169
12
0
22 May 2019
LAGC: Lazily Aggregated Gradient Coding for Straggler-Tolerant and Communication-Efficient Distributed Learning
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2019
Jingjing Zhang
Osvaldo Simeone
216
36
0
22 May 2019
Sequential training algorithm for neural networks
Jongrae Kim
37
1
0
17 May 2019
Efficient Optimization of Loops and Limits with Randomized Telescoping Sums
International Conference on Machine Learning (ICML), 2019
Alex Beatson
Ryan P. Adams
157
21
0
16 May 2019
Client-Edge-Cloud Hierarchical Federated Learning
Lumin Liu
Jun Zhang
S. H. Song
Khaled B. Letaief
FedML
349
918
0
16 May 2019
Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition
Knowledge Discovery and Data Mining (KDD), 2019
Anil R. Yelundur
Vineet Chaoji
Bamdev Mishra
189
7
0
15 May 2019
A Stochastic Gradient Method with Biased Estimation for Faster Nonconvex Optimization
Pacific Rim International Conference on Artificial Intelligence (PRICAI), 2019
Jia Bi
S. Gunn
177
4
0
13 May 2019
Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints
International Conference on Learning Representations (ICLR), 2019
Mengtian Li
Ersin Yumer
Deva Ramanan
258
54
0
12 May 2019
On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization
International Conference on Machine Learning (ICML), 2019
Hao Yu
Rong Jin
212
52
0
10 May 2019
The sharp, the flat and the shallow: Can weakly interacting agents learn to escape bad minima?
N. Kantas
P. Parpas
G. Pavliotis
ODL
116
8
0
10 May 2019
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks
Neural Information Processing Systems (NeurIPS), 2019
Jiong Zhang
Hsiang-Fu Yu
Inderjit S. Dhillon
189
29
0
08 May 2019
Sparse multiresolution representations with adaptive kernels
IEEE Transactions on Signal Processing (IEEE Trans. Signal Process.), 2019
Maria Peifer
Luiz F. O. Chamon
Santiago Paternain
Alejandro Ribeiro
152
4
0
07 May 2019
Estimate Sequences for Variance-Reduced Stochastic Composite Optimization
International Conference on Machine Learning (ICML), 2019
A. Kulunchakov
Julien Mairal
192
27
0
07 May 2019
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
524
2
0
04 May 2019
New optimization algorithms for neural network training using operator splitting techniques
Neural Networks (NN), 2019
C. Alecsa
Titus Pinta
Imre Boros
173
9
0
29 Apr 2019
Target-Based Temporal Difference Learning
Donghwan Lee
Niao He
OOD
163
33
0
24 Apr 2019
Previous
1
2
3
...
24
25
26
...
28
29
30
Next
Page 25 of 30
Page
of 30
Go