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Optimization Methods for Large-Scale Machine Learning

15 June 2016

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

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Title
First-Order Preconditioning via Hypergradient Descent Theodore H. Moskovitz Rui Wang Janice Lan Sanyam Kapoor Thomas Miconi J. Yosinski Aditya Rawal AI4CE 79 8 0 18 Oct 2019
Improving the convergence of SGD through adaptive batch sizes Scott Sievert Zachary B. Charles ODL 74 8 0 18 Oct 2019
Error Lower Bounds of Constant Step-size Stochastic Gradient Descent Zhiyan Ding Yiding Chen Qin Li Xiaojin Zhu 54 4 0 18 Oct 2019
Adaptive Step Sizes in Variance Reduction via Regularization Bingcong Li G. Giannakis 50 5 0 15 Oct 2019
Predicting dynamical system evolution with residual neural networks Artem Chashchin M. Botchev Ivan Oseledets G. Ovchinnikov AI4TS AI4CE 45 3 0 11 Oct 2019
Straggler-Agnostic and Communication-Efficient Distributed Primal-Dual Algorithm for High-Dimensional Data Mining Zhouyuan Huo Heng-Chiao Huang FedML 47 5 0 09 Oct 2019
The Complexity of Finding Stationary Points with Stochastic Gradient Descent Yoel Drori Shigehito Shimizu 94 64 0 04 Oct 2019
Partial differential equation regularization for supervised machine learning Jillian R. Fisher 56 2 0 03 Oct 2019
SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum Jianyu Wang Vinayak Tantia Nicolas Ballas Michael G. Rabbat 99 201 0 01 Oct 2019
Conservative set valued fields, automatic differentiation, stochastic gradient method and deep learning Jérôme Bolte Edouard Pauwels 104 129 0 23 Sep 2019
PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs Xuhui Meng Zhen Li Dongkun Zhang George Karniadakis PINN AI4CE 90 457 0 23 Sep 2019
Human Position Detection & Tracking with On-robot Time-of-Flight Laser Ranging Sensors Sarthak Arora Shitij Kumar F. Sahin 14 2 0 21 Sep 2019
Empirical study towards understanding line search approximations for training neural networks Younghwan Chae D. Wilke 110 11 0 15 Sep 2019
Ouroboros: On Accelerating Training of Transformer-Based Language Models Qian Yang Zhouyuan Huo Wenlin Wang Heng-Chiao Huang Lawrence Carin 57 9 0 14 Sep 2019
Shapley Interpretation and Activation in Neural Networks Yadong Li Xin Cui TDI FAtt LLMSV 50 3 0 13 Sep 2019
The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication Sebastian U. Stich Sai Praneeth Karimireddy FedML 80 20 0 11 Sep 2019
Efficient Continual Learning in Neural Networks with Embedding Regularization Jary Pomponi Simone Scardapane Vincenzo Lomonaco A. Uncini CLL 87 42 0 09 Sep 2019
Distributed Deep Learning with Event-Triggered Communication Jemin George Prudhvi K. Gurram 56 16 0 08 Sep 2019
Distributed Training of Embeddings using Graph Analytics G. Gill Roshan Dathathri Saeed Maleki Madan Musuvathi Todd Mytkowicz Olli Saarikivi The University of Texas at Austin GNN 26 1 0 08 Sep 2019
Stochastic quasi-Newton with line-search regularization A. Wills Thomas B. Schon ODL 68 22 0 03 Sep 2019
Linear Convergence of Adaptive Stochastic Gradient Descent Yuege Xie Xiaoxia Wu Rachel A. Ward 74 45 0 28 Aug 2019
Tackling Algorithmic Bias in Neural-Network Classifiers using Wasserstein-2 Regularization Laurent Risser Alberto González Sanz Quentin Vincenot Jean-Michel Loubes 97 21 0 15 Aug 2019
Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Chen Wang Qin Yu Kai Zhou D. Hui Xiaofeng Gong Ruisen Luo 141 22 0 09 Aug 2019
Bias of Homotopic Gradient Descent for the Hinge Loss Denali Molitor Deanna Needell Rachel A. Ward 39 6 0 26 Jul 2019
Learning the Tangent Space of Dynamical Instabilities from Data Antoine Blanchard T. Sapsis 134 8 0 24 Jul 2019
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions Matthew Faw Rajat Sen Karthikeyan Shanmugam Constantine Caramanis Sanjay Shakkottai 80 3 0 23 Jul 2019
Bilevel Optimization, Deep Learning and Fractional Laplacian Regularization with Applications in Tomography Harbir Antil Z. Di R. Khatri 58 51 0 22 Jul 2019
Speeding Up Iterative Closest Point Using Stochastic Gradient Descent F. A. Maken F. Ramos Lionel Ott 3DPC 37 13 0 22 Jul 2019
Adaptive Weight Decay for Deep Neural Networks Kensuke Nakamura Byung-Woo Hong 63 43 0 21 Jul 2019
Techniques for Automated Machine Learning Yi-Wei Chen Qingquan Song Helen Zhou 65 50 0 21 Jul 2019
An Evolutionary Algorithm of Linear complexity: Application to Training of Deep Neural Networks S. I. Valdez A. R. Domínguez ODL 29 1 0 12 Jul 2019
Adaptive Deep Learning for High-Dimensional Hamilton-Jacobi-Bellman Equations Tenavi Nakamura-Zimmerer Q. Gong W. Kang 134 134 0 11 Jul 2019
The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning Suyun Liu Luis Nunes Vicente 167 75 0 10 Jul 2019
Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization Kenji Kawaguchi Haihao Lu ODL 100 64 0 09 Jul 2019
Unified Optimal Analysis of the (Stochastic) Gradient Method Sebastian U. Stich 91 113 0 09 Jul 2019
Learning joint lesion and tissue segmentation from task-specific hetero-modal datasets Reuben Dorent Wenqi Li J. Ekanayake Sebastien Ourselin Tom Vercauteren 55 4 0 07 Jul 2019
ReLU Networks as Surrogate Models in Mixed-Integer Linear Programs B. Grimstad H. Andersson 64 144 0 06 Jul 2019
Precision annealing Monte Carlo methods for statistical data assimilation and machine learning Zheng Fang Adrian S. Wong Kangbo Hao Alexander J. A. Ty H. Abarbanel 26 1 0 06 Jul 2019
Variance Reduction for Matrix Games Y. Carmon Yujia Jin Aaron Sidford Kevin Tian 94 67 0 03 Jul 2019
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses Ulysse Marteau-Ferey Francis R. Bach Alessandro Rudi 56 36 0 03 Jul 2019
The Role of Memory in Stochastic Optimization Antonio Orvieto Jonas Köhler Aurelien Lucchi 94 31 0 02 Jul 2019
Network-accelerated Distributed Machine Learning Using MLFabric Raajay Viswanathan Aditya Akella AI4CE 42 4 0 30 Jun 2019
Combining Stochastic Adaptive Cubic Regularization with Negative Curvature for Nonconvex Optimization Seonho Park Seung Hyun Jung P. Pardalos ODL 72 15 0 27 Jun 2019
A Review on Deep Learning in Medical Image Reconstruction Hai-Miao Zhang Bin Dong MedIm 128 128 0 23 Jun 2019
A Unifying Framework for Variance Reduction Algorithms for Finding Zeroes of Monotone Operators Xun Zhang W. Haskell Z. Ye 51 3 0 22 Jun 2019
Fully Decoupled Neural Network Learning Using Delayed Gradients Huiping Zhuang Yi Wang Qinglai Liu Shuai Zhang Zhiping Lin FedML 83 31 0 21 Jun 2019
A Survey of Optimization Methods from a Machine Learning Perspective Shiliang Sun Zehui Cao Han Zhu Jing Zhao 88 566 0 17 Jun 2019
Optimizing Pipelined Computation and Communication for Latency-Constrained Edge Learning N. Skatchkovsky Osvaldo Simeone 52 17 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 86 5 0 09 Jun 2019
Practical Deep Learning with Bayesian Principles Kazuki Osawa S. Swaroop Anirudh Jain Runa Eschenhagen Richard Turner Rio Yokota Mohammad Emtiyaz Khan BDL UQCV 167 247 0 06 Jun 2019