Optimization Methods for Large-Scale Machine Learning

15 June 2016

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

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Title
A mathematical model for automatic differentiation in machine learning Jérôme Bolte Edouard Pauwels 23 67 0 03 Jun 2020
Finite Difference Neural Networks: Fast Prediction of Partial Differential Equations Zheng Shi Nur Sila Gulgec A. Berahas S. Pakzad Martin Takáč 9 10 0 02 Jun 2020
Carathéodory Sampling for Stochastic Gradient Descent Francesco Cosentino Harald Oberhauser Alessandro Abate 18 1 0 02 Jun 2020
Improved SVRG for quadratic functions N. Kahalé 33 0 0 01 Jun 2020
Artificial neural networks for neuroscientists: A primer G. R. Yang Xiao-Jing Wang 42 243 0 01 Jun 2020
Data-Driven Methods to Monitor, Model, Forecast and Control Covid-19 Pandemic: Leveraging Data Science, Epidemiology and Control Theory Teodoro Alamo Daniel Gutiérrez-Reina P. Millán 14 27 0 01 Jun 2020
Pruning via Iterative Ranking of Sensitivity Statistics Stijn Verdenius M. Stol Patrick Forré AAML 24 37 0 01 Jun 2020
Better scalability under potentially heavy-tailed gradients Matthew J. Holland 13 1 0 01 Jun 2020
ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning Z. Yao A. Gholami Sheng Shen Mustafa Mustafa Kurt Keutzer Michael W. Mahoney ODL 39 276 0 01 Jun 2020
A New Accelerated Stochastic Gradient Method with Momentum Liang Liu Xiaopeng Luo ODL 15 3 0 31 May 2020
Complex Sequential Understanding through the Awareness of Spatial and Temporal Concepts Bo Pang Kaiwen Zha Hanwen Cao Jiajun Tang Minghui Yu Cewu Lu 20 25 0 30 May 2020
CoolMomentum: A Method for Stochastic Optimization by Langevin Dynamics with Simulated Annealing O. Borysenko M. Byshkin ODL 27 14 0 29 May 2020
HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism Jay H. Park Gyeongchan Yun Chang Yi N. T. Nguyen Seungmin Lee Jaesik Choi S. Noh Young-ri Choi MoE 27 130 0 28 May 2020
Convergence Analysis of Riemannian Stochastic Approximation Schemes Alain Durmus P. Jiménez Eric Moulines Salem Said Hoi-To Wai 19 10 0 27 May 2020
Scalable Privacy-Preserving Distributed Learning D. Froelicher J. Troncoso-Pastoriza Apostolos Pyrgelis Sinem Sav João Sá Sousa Jean-Philippe Bossuat Jean-Pierre Hubaux FedML 22 69 0 19 May 2020
PatchGuard: A Provably Robust Defense against Adversarial Patches via Small Receptive Fields and Masking Chong Xiang A. Bhagoji Vikash Sehwag Prateek Mittal AAML 30 29 0 17 May 2020
S-ADDOPT: Decentralized stochastic first-order optimization over directed graphs Muhammad I. Qureshi Ran Xin S. Kar U. Khan 23 33 0 15 May 2020
Interpreting Rate-Distortion of Variational Autoencoder and Using Model Uncertainty for Anomaly Detection Seonho Park George Adosoglou P. Pardalos DRL UQCV 36 16 0 05 May 2020
Distributed Stochastic Non-Convex Optimization: Momentum-Based Variance Reduction Prashant Khanduri Pranay Sharma Swatantra Kafle Saikiran Bulusu K. Rajawat P. Varshney 27 6 0 01 May 2020
Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging Shigang Li Tal Ben-Nun Giorgi Nadiradze Salvatore Di Girolamo Nikoli Dryden Dan Alistarh Torsten Hoefler 29 15 0 30 Apr 2020
Dynamic backup workers for parallel machine learning Chuan Xu Giovanni Neglia Nicola Sebastianelli 17 11 0 30 Apr 2020
The Impact of the Mini-batch Size on the Variance of Gradients in Stochastic Gradient Descent Xin-Yao Qian Diego Klabjan ODL 31 35 0 27 Apr 2020
Correct Me If You Can: Learning from Error Corrections and Markings Julia Kreutzer Nathaniel Berger Stefan Riezler 14 18 0 23 Apr 2020
Heterogeneous CPU+GPU Stochastic Gradient Descent Algorithms Yujing Ma Florin Rusu 6 3 0 19 Apr 2020
Transfer learning in large-scale ocean bottom seismic wavefield reconstruction Mi Zhang Ali Siahkoohi Felix J. Herrmann 27 2 0 15 Apr 2020
On Learning Rates and Schrödinger Operators Bin Shi Weijie J. Su Michael I. Jordan 34 60 0 15 Apr 2020
Stochastic batch size for adaptive regularization in deep network optimization Kensuke Nakamura Stefano Soatto Byung-Woo Hong ODL 27 6 0 14 Apr 2020
Estimating a Brain Network Predictive of Stress and Genotype with Supervised Autoencoders Austin Talbot David B. Dunson K. Dzirasa David Carlson 9 3 0 10 Apr 2020
Straggler-aware Distributed Learning: Communication Computation Latency Trade-off Emre Ozfatura S. Ulukus Deniz Gunduz 20 42 0 10 Apr 2020
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration Wenlong Mou C. J. Li Martin J. Wainwright Peter L. Bartlett Michael I. Jordan 33 75 0 09 Apr 2020
Deep Neural Network Learning with Second-Order Optimizers -- a Practical Study with a Stochastic Quasi-Gauss-Newton Method C. Thiele Mauricio Araya-Polo D. Hohl ODL 11 2 0 06 Apr 2020
Understanding Learning Dynamics for Neural Machine Translation Conghui Zhu Guanlin Li Lemao Liu Tiejun Zhao Shuming Shi 31 3 0 05 Apr 2020
Stopping Criteria for, and Strong Convergence of, Stochastic Gradient Descent on Bottou-Curtis-Nocedal Functions V. Patel 23 23 0 01 Apr 2020
Concentrated Differentially Private and Utility Preserving Federated Learning Rui Hu Yuanxiong Guo Yanmin Gong FedML 41 12 0 30 Mar 2020
Differentially Private Federated Learning for Resource-Constrained Internet of Things Rui Hu Yuanxiong Guo E. Ratazzi Yanmin Gong FedML 33 17 0 28 Mar 2020
A Hybrid-Order Distributed SGD Method for Non-Convex Optimization to Balance Communication Overhead, Computational Complexity, and Convergence Rate Naeimeh Omidvar M. Maddah-ali Hamed Mahdavi ODL 27 3 0 27 Mar 2020
Convergence of Recursive Stochastic Algorithms using Wasserstein Divergence Abhishek Gupta W. Haskell 11 4 0 25 Mar 2020
Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness Thinh T. Doan Lam M. Nguyen Nhan H. Pham Justin Romberg 23 21 0 24 Mar 2020
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates Anastasia Koloskova Nicolas Loizou Sadra Boreiri Martin Jaggi Sebastian U. Stich FedML 46 493 0 23 Mar 2020
A termination criterion for stochastic gradient descent for binary classification Sina Baghal Courtney Paquette S. Vavasis 13 0 0 23 Mar 2020
Resilience in Collaborative Optimization: Redundant and Independent Cost Functions Nirupam Gupta Nitin H. Vaidya 30 18 0 21 Mar 2020
A Hybrid Model-based and Data-driven Approach to Spectrum Sharing in mmWave Cellular Networks H. S. Ghadikolaei H. Ghauch Gábor Fodor Mikael Skoglund Carlo Fischione 14 14 0 19 Mar 2020
Block Layer Decomposition schemes for training Deep Neural Networks L. Palagi R. Seccia 39 5 0 18 Mar 2020
The Implicit Regularization of Stochastic Gradient Flow for Least Squares Alnur Ali Yan Sun Robert Tibshirani 30 76 0 17 Mar 2020
Dynamic transformation of prior knowledge into Bayesian models for data streams Tran Xuan Bach N. Anh Ngo Van Linh Khoat Than 19 8 0 13 Mar 2020
Truncated Inference for Latent Variable Optimization Problems: Application to Robust Estimation and Learning Christopher Zach Huu Le 36 4 0 12 Mar 2020
Machine Learning on Volatile Instances Xiaoxi Zhang Jianyu Wang Gauri Joshi Carlee Joe-Wong 25 25 0 12 Mar 2020
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization Sudeep Salgia Qing Zhao Sattar Vakili 48 2 0 11 Mar 2020
Communication-efficient Variance-reduced Stochastic Gradient Descent H. S. Ghadikolaei Sindri Magnússon 22 3 0 10 Mar 2020
Communication-Efficient Distributed Deep Learning: A Comprehensive Survey Zhenheng Tang Shaoshuai Shi Wei Wang Yue Liu Xiaowen Chu 31 48 0 10 Mar 2020