Distributed Newton Methods for Deep Neural Networks

DOI10.1162/neco_a_01088zbMath1472.68176arXiv1802.00130OpenAlexW2963419707WikidataQ52590057 ScholiaQ52590057MaRDI QIDQ5157199

Chien-Chih Wang, Kent Loong Tan, Chih-Jen Lin, Yu-Hsiang Lin, Dhruv Mahajan, Unnamed Author, Chun-Ting Chen, S. Sathiya Keerthi

Publication date: 12 October 2021

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1802.00130

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Numerical mathematical programming methods (65K05) Nonconvex programming, global optimization (90C26) Methods of successive quadratic programming type (90C55)

Related Items (1)

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Uses Software

• UCI-ml
• LIBSVM
• MNIST
• ImageNet
• Pylearn2
• AlexNet

Cites Work

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• On the Use of Stochastic Hessian Information in Optimization Methods for Machine Learning
• Fast Curvature Matrix-Vector Products for Second-Order Gradient Descent
• Large-Scale Machine Learning with Stochastic Gradient Descent
• Subsampled Hessian Newton Methods for Supervised Learning
• Some methods of speeding up the convergence of iteration methods

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