Optimization Methods for Large-Scale Machine Learning

15 June 2016

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

50 / 1,410 papers shown

Title
A Machine Learning Framework for Solving High-Dimensional Mean Field Game and Mean Field Control Problems Lars Ruthotto Stanley Osher Wuchen Li L. Nurbekyan Samy Wu Fung AI4CE 41 215 0 04 Dec 2019
Federated Learning with Personalization Layers Manoj Ghuhan Arivazhagan V. Aggarwal Aaditya Kumar Singh Sunav Choudhary FedML 44 822 0 02 Dec 2019
Scalable Extreme Deconvolution James A. Ritchie Iain Murray 20 1 0 26 Nov 2019
Automatic Differentiable Monte Carlo: Theory and Application Shi-Xin Zhang Z. Wan H. Yao 21 17 0 20 Nov 2019
Layer-wise Adaptive Gradient Sparsification for Distributed Deep Learning with Convergence Guarantees Shaoshuai Shi Zhenheng Tang Qiang-qiang Wang Kaiyong Zhao Xiaowen Chu 24 22 0 20 Nov 2019
On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning Aritra Dutta El Houcine Bergou A. Abdelmoniem Chen-Yu Ho Atal Narayan Sahu Marco Canini Panos Kalnis 38 77 0 19 Nov 2019
Convergence Analysis of a Momentum Algorithm with Adaptive Step Size for Non Convex Optimization Anas Barakat Pascal Bianchi 23 12 0 18 Nov 2019
Optimal Mini-Batch Size Selection for Fast Gradient Descent M. Perrone Haidar Khan Changhoan Kim Anastasios Kyrillidis Jerry Quinn V. Salapura 26 9 0 15 Nov 2019
Convergence to minima for the continuous version of Backtracking Gradient Descent T. Truong 11 18 0 11 Nov 2019
Asynchronous Online Federated Learning for Edge Devices with Non-IID Data Yujing Chen Yue Ning Martin Slawski Huzefa Rangwala FedML 26 56 0 05 Nov 2019
A Rule for Gradient Estimator Selection, with an Application to Variational Inference Tomas Geffner Justin Domke 53 6 0 05 Nov 2019
Persistency of Excitation for Robustness of Neural Networks Kamil Nar S. Shankar Sastry AAML 19 10 0 04 Nov 2019
On the Convergence of Local Descent Methods in Federated Learning Farzin Haddadpour M. Mahdavi FedML 26 267 0 31 Oct 2019
Lsh-sampling Breaks the Computation Chicken-and-egg Loop in Adaptive Stochastic Gradient Estimation Beidi Chen Yingchen Xu Anshumali Shrivastava 23 16 0 30 Oct 2019
Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization Farzin Haddadpour Mohammad Mahdi Kamani M. Mahdavi V. Cadambe FedML 33 199 0 30 Oct 2019
LeanConvNets: Low-cost Yet Effective Convolutional Neural Networks Jonathan Ephrath Moshe Eliasof Lars Ruthotto E. Haber Eran Treister 36 16 0 29 Oct 2019
ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels Angus Dempster Franccois Petitjean Geoffrey I. Webb AI4TS 35 760 0 29 Oct 2019
Non-Gaussianity of Stochastic Gradient Noise A. Panigrahi Raghav Somani Navin Goyal Praneeth Netrapalli 31 52 0 21 Oct 2019
Sparsification as a Remedy for Staleness in Distributed Asynchronous SGD Rosa Candela Giulio Franzese Maurizio Filippone Pietro Michiardi 24 1 0 21 Oct 2019
A Stochastic Extra-Step Quasi-Newton Method for Nonsmooth Nonconvex Optimization Minghan Yang Andre Milzarek Zaiwen Wen Tong Zhang ODL 19 36 0 21 Oct 2019
Communication-Efficient Local Decentralized SGD Methods Xiang Li Wenhao Yang Shusen Wang Zhihua Zhang 30 53 0 21 Oct 2019
A Fast Saddle-Point Dynamical System Approach to Robust Deep Learning Yasaman Esfandiari Aditya Balu K. Ebrahimi Umesh Vaidya N. Elia Soumik Sarkar OOD 28 3 0 18 Oct 2019
Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic Matteo Sordello Niccolò Dalmasso Hangfeng He Weijie Su 24 7 0 18 Oct 2019
First-Order Preconditioning via Hypergradient Descent Theodore H. Moskovitz Rui Wang Janice Lan Sanyam Kapoor Thomas Miconi J. Yosinski Aditya Rawal AI4CE 39 8 0 18 Oct 2019
Improving the convergence of SGD through adaptive batch sizes Scott Sievert Zachary B. Charles ODL 33 8 0 18 Oct 2019
Error Lower Bounds of Constant Step-size Stochastic Gradient Descent Zhiyan Ding Yiding Chen Qin Li Xiaojin Zhu 22 4 0 18 Oct 2019
Adaptive Step Sizes in Variance Reduction via Regularization Bingcong Li G. Giannakis 14 5 0 15 Oct 2019
Predicting dynamical system evolution with residual neural networks Artem Chashchin M. Botchev Ivan Oseledets G. Ovchinnikov AI4TS AI4CE 9 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 19 5 0 09 Oct 2019
The Complexity of Finding Stationary Points with Stochastic Gradient Descent Yoel Drori Shigehito Shimizu 31 64 0 04 Oct 2019
Partial differential equation regularization for supervised machine learning Jillian R. Fisher 35 2 0 03 Oct 2019
SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum Jianyu Wang Vinayak Tantia Nicolas Ballas Michael G. Rabbat 25 200 0 01 Oct 2019
Conservative set valued fields, automatic differentiation, stochastic gradient method and deep learning Jérôme Bolte Edouard Pauwels 13 126 0 23 Sep 2019
PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs Xuhui Meng Zhen Li Dongkun Zhang George Karniadakis PINN AI4CE 22 445 0 23 Sep 2019
Human Position Detection & Tracking with On-robot Time-of-Flight Laser Ranging Sensors Sarthak Arora Shitij Kumar F. Sahin 11 2 0 21 Sep 2019
From Server-Based to Client-Based Machine Learning: A Comprehensive Survey Renjie Gu Chaoyue Niu Fan Wu Guihai Chen Chun Hu Chengfei Lyu Zhihua Wu 33 25 0 18 Sep 2019
Empirical study towards understanding line search approximations for training neural networks Younghwan Chae D. Wilke 27 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 33 9 0 14 Sep 2019
Shapley Interpretation and Activation in Neural Networks Yadong Li Xin Cui TDI FAtt LLMSV 25 3 0 13 Sep 2019
Lightweight Task Offloading Exploiting MPI Wait Times for Parallel Adaptive Mesh Refinement Philipp Samfass T. Weinzierl D. E. Charrier M. Bader 18 2 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 30 20 0 11 Sep 2019
Efficient Continual Learning in Neural Networks with Embedding Regularization Jary Pomponi Simone Scardapane Vincenzo Lomonaco A. Uncini CLL 36 41 0 09 Sep 2019
Communication-Censored Distributed Stochastic Gradient Descent Weiyu Li Tianyi Chen Liping Li Zhaoxian Wu Qing Ling 25 17 0 09 Sep 2019
Distributed Deep Learning with Event-Triggered Communication Jemin George Prudhvi K. Gurram 16 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 15 1 0 08 Sep 2019
Decentralized Stochastic Gradient Tracking for Non-convex Empirical Risk Minimization Jiaqi Zhang Keyou You 17 18 0 06 Sep 2019
Deep Convolutional Networks in System Identification Carl R. Andersson Antônio H. Ribeiro K. Tiels Niklas Wahlström Thomas B. Schon 27 48 0 04 Sep 2019
LCA: Loss Change Allocation for Neural Network Training Janice Lan Rosanne Liu Hattie Zhou J. Yosinski 21 24 0 03 Sep 2019
Stochastic quasi-Newton with line-search regularization A. Wills Thomas B. Schon ODL 31 21 0 03 Sep 2019
Linear Convergence of Adaptive Stochastic Gradient Descent Yuege Xie Xiaoxia Wu Rachel A. Ward 16 44 0 28 Aug 2019