Optimization Methods for Large-Scale Machine Learning

15 June 2016

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

50 / 1,407 papers shown

Title
Decoupled Parallel Backpropagation with Convergence Guarantee Zhouyuan Huo Bin Gu Qian Yang Heng-Chiao Huang 15 97 0 27 Apr 2018
Revisiting Small Batch Training for Deep Neural Networks Dominic Masters Carlo Luschi ODL 37 659 0 20 Apr 2018
Constant Step Size Stochastic Gradient Descent for Probabilistic Modeling Dmitry Babichev Francis R. Bach 25 9 0 16 Apr 2018
E-commerce Anomaly Detection: A Bayesian Semi-Supervised Tensor Decomposition Approach using Natural Gradients Anil R. Yelundur Srinivasan H. Sengamedu Bamdev Mishra 13 1 0 11 Apr 2018
Sequence Training of DNN Acoustic Models With Natural Gradient Adnan Haider P. Woodland 26 7 0 06 Apr 2018
Probabilistic Contraction Analysis of Iterated Random Operators Abhishek Gupta Rahul Jain Peter Glynn 6 9 0 04 Apr 2018
A Constant Step Stochastic Douglas-Rachford Algorithm with Application to Non Separable Regularizations Adil Salim Pascal Bianchi W. Hachem 15 2 0 03 Apr 2018
Training Tips for the Transformer Model Martin Popel Ondrej Bojar 12 306 0 01 Apr 2018
A Common Framework for Natural Gradient and Taylor based Optimisation using Manifold Theory Adnan Haider 8 2 0 26 Mar 2018
Lower error bounds for the stochastic gradient descent optimization algorithm: Sharp convergence rates for slowly and fast decaying learning rates Arnulf Jentzen Philippe von Wurstemberger 75 31 0 22 Mar 2018
Group Normalization Yuxin Wu Kaiming He 45 3,596 0 22 Mar 2018
Efficient FPGA Implementation of Conjugate Gradient Methods for Laplacian System using HLS Sahithi Rampalli N. Sehgal Ishita Bindlish Tanya Tyagi Pawan Kumar 13 4 0 10 Mar 2018
A Stochastic Semismooth Newton Method for Nonsmooth Nonconvex Optimization Andre Milzarek X. Xiao Shicong Cen Zaiwen Wen M. Ulbrich 15 36 0 09 Mar 2018
WNGrad: Learn the Learning Rate in Gradient Descent Xiaoxia Wu Rachel A. Ward Léon Bottou 22 86 0 07 Mar 2018
Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning Yao Zhang Andrew M. Saxe Madhu S. Advani A. Lee 18 59 0 05 Mar 2018
DAGs with NO TEARS: Continuous Optimization for Structure Learning Xun Zheng Bryon Aragam Pradeep Ravikumar Eric Xing NoLa CML OffRL 19 914 0 04 Mar 2018
Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD Sanghamitra Dutta Gauri Joshi Soumyadip Ghosh Parijat Dube P. Nagpurkar 31 193 0 03 Mar 2018
Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis Tal Ben-Nun Torsten Hoefler GNN 33 702 0 26 Feb 2018
GPU Accelerated Sub-Sampled Newton's Method Sudhir B. Kylasa Farbod Roosta-Khorasani Michael W. Mahoney A. Grama ODL 26 8 0 26 Feb 2018
Complex-valued Neural Networks with Non-parametric Activation Functions Simone Scardapane S. Van Vaerenbergh Amir Hussain A. Uncini 23 81 0 22 Feb 2018
Spurious Valleys in Two-layer Neural Network Optimization Landscapes Luca Venturi Afonso S. Bandeira Joan Bruna 32 74 0 18 Feb 2018
Convergence of Online Mirror Descent Yunwen Lei Ding-Xuan Zhou 23 20 0 18 Feb 2018
Stochastic quasi-Newton with adaptive step lengths for large-scale problems A. Wills Thomas B. Schon 24 9 0 12 Feb 2018
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption Lam M. Nguyen Phuong Ha Nguyen Marten van Dijk Peter Richtárik K. Scheinberg Martin Takáč 24 226 0 11 Feb 2018
Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data Susan Athey David M. Blei Rob Donnelly Francisco J. R. Ruiz Tobias Schmidt 14 66 0 22 Jan 2018
Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms Junhong Lin V. Cevher 14 34 0 22 Jan 2018
Rover Descent: Learning to optimize by learning to navigate on prototypical loss surfaces Louis Faury Flavian Vasile 15 2 0 22 Jan 2018
When Does Stochastic Gradient Algorithm Work Well? Lam M. Nguyen Nam H. Nguyen Dzung Phan Jayant Kalagnanam K. Scheinberg 24 15 0 18 Jan 2018
MXNET-MPI: Embedding MPI parallelism in Parameter Server Task Model for scaling Deep Learning Amith R. Mamidala Georgios Kollias C. Ward F. Artico 23 20 0 11 Jan 2018
Gradient-based Optimization for Regression in the Functional Tensor-Train Format Alex A. Gorodetsky J. Jakeman 19 32 0 03 Jan 2018
A Stochastic Trust Region Algorithm Based on Careful Step Normalization Frank E. Curtis K. Scheinberg R. Shi 27 45 0 29 Dec 2017
Geometrical Insights for Implicit Generative Modeling Léon Bottou Martín Arjovsky David Lopez-Paz Maxime Oquab 32 49 0 21 Dec 2017
Snake: a Stochastic Proximal Gradient Algorithm for Regularized Problems over Large Graphs Adil Salim Pascal Bianchi W. Hachem 25 17 0 19 Dec 2017
Parallel Complexity of Forward and Backward Propagation Maxim Naumov 16 8 0 18 Dec 2017
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning Siyuan Ma Raef Bassily M. Belkin 24 287 0 18 Dec 2017
Neumann Optimizer: A Practical Optimization Algorithm for Deep Neural Networks Shankar Krishnan Ying Xiao Rif A. Saurous ODL 8 19 0 08 Dec 2017
AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks Aditya Devarakonda Maxim Naumov M. Garland ODL 24 136 0 06 Dec 2017
A two-dimensional decomposition approach for matrix completion through gossip Mukul Bhutani Bamdev Mishra 16 0 0 21 Nov 2017
Convergent Block Coordinate Descent for Training Tikhonov Regularized Deep Neural Networks Ziming Zhang M. Brand 26 70 0 20 Nov 2017
BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning Ziming Zhang Yuanwei Wu Guanghui Wang ODL 32 28 0 19 Nov 2017
Accelerated Method for Stochastic Composition Optimization with Nonsmooth Regularization Zhouyuan Huo Bin Gu Ji Liu Heng-Chiao Huang 29 50 0 10 Nov 2017
SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements Francisco J. R. Ruiz Susan Athey David M. Blei 24 85 0 09 Nov 2017
Analysis of Biased Stochastic Gradient Descent Using Sequential Semidefinite Programs Bin Hu Peter M. Seiler Laurent Lessard 18 39 0 03 Nov 2017
Don't Decay the Learning Rate, Increase the Batch Size Samuel L. Smith Pieter-Jan Kindermans Chris Ying Quoc V. Le ODL 13 978 0 01 Nov 2017
Adaptive Sampling Strategies for Stochastic Optimization Raghu Bollapragada R. Byrd J. Nocedal 11 115 0 30 Oct 2017
On the role of synaptic stochasticity in training low-precision neural networks Carlo Baldassi Federica Gerace H. Kappen C. Lucibello Luca Saglietti Enzo Tartaglione R. Zecchina 9 23 0 26 Oct 2017
Avoiding Communication in Proximal Methods for Convex Optimization Problems Saeed Soori Aditya Devarakonda J. Demmel Mert Gurbuzbalaban M. Dehnavi 27 7 0 24 Oct 2017
Smart "Predict, then Optimize" Adam N. Elmachtoub Paul Grigas 22 578 0 22 Oct 2017
Convergence diagnostics for stochastic gradient descent with constant step size Jerry Chee Panos Toulis 8 11 0 17 Oct 2017
AdaDNNs: Adaptive Ensemble of Deep Neural Networks for Scene Text Recognition Chun Yang Xu-Cheng Yin Zejun Li Jianwei Wu Chunchao Guo Hongfa Wang Lei Xiao 24 10 0 10 Oct 2017