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Convergence Rates of Training Deep Neural Networks via Alternating Minimization Methods
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Convergence Rates of Training Deep Neural Networks via Alternating Minimization Methods

Jintao Xu
Chenglong Bao
W. Xing
ArXiv (abs)PDFHTML

Papers citing "Convergence Rates of Training Deep Neural Networks via Alternating Minimization Methods"

7 / 7 papers shown
Title
Fenchel Lifted Networks: A Lagrange Relaxation of Neural Network Training
Fenchel Lifted Networks: A Lagrange Relaxation of Neural Network Training
Fangda Gu
Armin Askari
L. Ghaoui
75
39
0
20 Nov 2018
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Liang-Chieh Chen
Yukun Zhu
George Papandreou
Florian Schroff
Hartwig Adam
SSeg
490
13,194
0
07 Feb 2018
Convergent Block Coordinate Descent for Training Tikhonov Regularized Deep Neural Networks
Convergent Block Coordinate Descent for Training Tikhonov Regularized Deep Neural Networks
Ziming Zhang
M. Brand
54
71
0
20 Nov 2017
Wide & Deep Learning for Recommender Systems
Wide & Deep Learning for Recommender Systems
Heng-Tze Cheng
L. Koc
Jeremiah Harmsen
T. Shaked
Tushar Chandra
...
Zakaria Haque
Lichan Hong
Vihan Jain
Xiaobing Liu
Hemal Shah
HAIVLM
198
3,673
0
24 Jun 2016
Training Neural Networks Without Gradients: A Scalable ADMM Approach
Training Neural Networks Without Gradients: A Scalable ADMM Approach
Gavin Taylor
R. Burmeister
Zheng Xu
Bharat Singh
Ankit B. Patel
Tom Goldstein
ODL
79
276
0
06 May 2016
Calculus of the exponent of Kurdyka-Łojasiewicz inequality and its applications to linear convergence of first-order methods
Calculus of the exponent of Kurdyka-Łojasiewicz inequality and its applications to linear convergence of first-order methods
Guoyin Li
Ting Kei Pong
166
296
0
09 Feb 2016
Sequence to Sequence Learning with Neural Networks
Sequence to Sequence Learning with Neural Networks
Ilya Sutskever
Oriol Vinyals
Quoc V. Le
AIMat
450
20,606
0
10 Sep 2014