Optimization Methods for Large-Scale Machine Learning

15 June 2016

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

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Title
LAGC: Lazily Aggregated Gradient Coding for Straggler-Tolerant and Communication-Efficient Distributed Learning Jingjing Zhang Osvaldo Simeone 18 31 0 22 May 2019
Sequential training algorithm for neural networks Jongrae Kim 22 1 0 17 May 2019
Efficient Optimization of Loops and Limits with Randomized Telescoping Sums Alex Beatson Ryan P. Adams 17 21 0 16 May 2019
Client-Edge-Cloud Hierarchical Federated Learning Lumin Liu Jun Zhang S. H. Song Khaled B. Letaief FedML 42 729 0 16 May 2019
Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition Anil R. Yelundur Vineet Chaoji Bamdev Mishra 11 7 0 15 May 2019
A Stochastic Gradient Method with Biased Estimation for Faster Nonconvex Optimization Jia Bi S. Gunn 25 3 0 13 May 2019
Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints Mengtian Li Ersin Yumer Deva Ramanan 14 46 0 12 May 2019
On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization Hao Yu Rong Jin 23 50 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 21 8 0 10 May 2019
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks Jiong Zhang Hsiang-Fu Yu Inderjit S. Dhillon 24 26 0 08 May 2019
Sparse multiresolution representations with adaptive kernels Maria Peifer Luiz F. O. Chamon Santiago Paternain Alejandro Ribeiro 11 4 0 07 May 2019
Estimate Sequences for Variance-Reduced Stochastic Composite Optimization A. Kulunchakov Julien Mairal 8 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 27 2 0 04 May 2019
New optimization algorithms for neural network training using operator splitting techniques C. Alecsa Titus Pinta Imre Boros 17 8 0 29 Apr 2019
Target-Based Temporal Difference Learning Donghwan Lee Niao He OOD 16 31 0 24 Apr 2019
Least Squares Auto-Tuning Shane T. Barratt Stephen P. Boyd MoMe 19 23 0 10 Apr 2019
Generalizing from a Few Examples: A Survey on Few-Shot Learning Yaqing Wang Quanming Yao James T. Kwok L. Ni 39 1,793 0 10 Apr 2019
On the approximation of the solution of partial differential equations by artificial neural networks trained by a multilevel Levenberg-Marquardt method H. Calandra Serge Gratton E. Riccietti X. Vasseur 16 7 0 09 Apr 2019
Convergence rates for the stochastic gradient descent method for non-convex objective functions Benjamin J. Fehrman Benjamin Gess Arnulf Jentzen 19 101 0 02 Apr 2019
Convergence rates for optimised adaptive importance samplers Ömer Deniz Akyildiz Joaquín Míguez 28 30 0 28 Mar 2019
OverSketched Newton: Fast Convex Optimization for Serverless Systems Vipul Gupta S. Kadhe T. Courtade Michael W. Mahoney Kannan Ramchandran 19 33 0 21 Mar 2019
Noisy Accelerated Power Method for Eigenproblems with Applications Vien V. Mai M. Johansson 9 3 0 20 Mar 2019
TATi-Thermodynamic Analytics ToolkIt: TensorFlow-based software for posterior sampling in machine learning applications Frederik Heber Zofia Trstanova B. Leimkuhler 22 0 0 20 Mar 2019
Combining Model and Parameter Uncertainty in Bayesian Neural Networks A. Hubin G. Storvik UQCV BDL 12 11 0 18 Mar 2019
A Distributed Hierarchical SGD Algorithm with Sparse Global Reduction Fan Zhou Guojing Cong 19 8 0 12 Mar 2019
Recovery Bounds on Class-Based Optimal Transport: A Sum-of-Norms Regularization Framework Arman Rahbar Ashkan Panahi M. Chehreghani Devdatt Dubhashi Hamid Krim 43 0 0 09 Mar 2019
SGD without Replacement: Sharper Rates for General Smooth Convex Functions Prateek Jain Dheeraj M. Nagaraj Praneeth Netrapalli 19 87 0 04 Mar 2019
Time-Delay Momentum: A Regularization Perspective on the Convergence and Generalization of Stochastic Momentum for Deep Learning Ziming Zhang Wenju Xu Alan Sullivan 43 1 0 02 Mar 2019
An Empirical Study of Large-Batch Stochastic Gradient Descent with Structured Covariance Noise Yeming Wen Kevin Luk Maxime Gazeau Guodong Zhang Harris Chan Jimmy Ba ODL 20 22 0 21 Feb 2019
Global Convergence of Adaptive Gradient Methods for An Over-parameterized Neural Network Xiaoxia Wu S. Du Rachel A. Ward 23 66 0 19 Feb 2019
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization Nhan H. Pham Lam M. Nguyen Dzung Phan Quoc Tran-Dinh 16 139 0 15 Feb 2019
Forward-backward-forward methods with variance reduction for stochastic variational inequalities R. Boț P. Mertikopoulos Mathias Staudigl P. Vuong 11 22 0 09 Feb 2019
Predict Globally, Correct Locally: Parallel-in-Time Optimal Control of Neural Networks P. Parpas Corey Muir OOD 13 12 0 07 Feb 2019
Negative eigenvalues of the Hessian in deep neural networks Guillaume Alain Nicolas Le Roux Pierre-Antoine Manzagol 19 42 0 06 Feb 2019
Riemannian adaptive stochastic gradient algorithms on matrix manifolds Hiroyuki Kasai Pratik Jawanpuria Bamdev Mishra 26 3 0 04 Feb 2019
Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions Adrien B. Taylor Francis R. Bach 14 60 0 03 Feb 2019
Stochastic Gradient Descent for Nonconvex Learning without Bounded Gradient Assumptions Yunwen Lei Ting Hu Guiying Li K. Tang MLT 21 115 0 03 Feb 2019
Non-asymptotic Analysis of Biased Stochastic Approximation Scheme Belhal Karimi B. Miasojedow Eric Moulines Hoi-To Wai 10 90 0 02 Feb 2019
Multilevel Monte Carlo Variational Inference Masahiro Fujisawa Issei Sato 19 10 0 01 Feb 2019
MgNet: A Unified Framework of Multigrid and Convolutional Neural Network Juncai He Jinchao Xu 16 51 0 29 Jan 2019
Variational Characterizations of Local Entropy and Heat Regularization in Deep Learning Nicolas García Trillos Zachary T. Kaplan D. Sanz-Alonso ODL 20 3 0 29 Jan 2019
Quasi-Newton Methods for Machine Learning: Forget the Past, Just Sample A. Berahas Majid Jahani Peter Richtárik Martin Takávc 24 40 0 28 Jan 2019
SGD: General Analysis and Improved Rates Robert Mansel Gower Nicolas Loizou Xun Qian Alibek Sailanbayev Egor Shulgin Peter Richtárik 34 376 0 27 Jan 2019
Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise A. Kulunchakov Julien Mairal 32 44 0 25 Jan 2019
Provable Smoothness Guarantees for Black-Box Variational Inference Justin Domke 11 34 0 24 Jan 2019
To Relieve Your Headache of Training an MRF, Take AdVIL Chongxuan Li Chao Du Kun Xu Max Welling Jun Zhu Bo Zhang 9 9 0 24 Jan 2019
Large-Batch Training for LSTM and Beyond Yang You Jonathan Hseu Chris Ying J. Demmel Kurt Keutzer Cho-Jui Hsieh 23 89 0 24 Jan 2019
Trajectory Normalized Gradients for Distributed Optimization Jianqiao Wangni Ke Li Jianbo Shi Jitendra Malik 19 2 0 24 Jan 2019
Decoupled Greedy Learning of CNNs Eugene Belilovsky Michael Eickenberg Edouard Oyallon 8 114 0 23 Jan 2019
Finite-Sum Smooth Optimization with SARAH Lam M. Nguyen Marten van Dijk Dzung Phan Phuong Ha Nguyen Tsui-Wei Weng Jayant Kalagnanam 28 22 0 22 Jan 2019