Weight decay and resolution effects in feedforward artificial neural networks

1991 ◽  
Vol 2 (1) ◽  
pp. 168-170 ◽  
Author(s):  
D.B. Mundie ◽  
L.W. Massengill
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Weight Decay ◽  
Artificial Neural

scholarly journals Noise injection for training artificial neural networks: A comparison with weight decay and early stopping

2009 ◽  
Vol 36 (10) ◽  
pp. 4810-4818 ◽  
Author(s):  
Richard M. Zur ◽  
Yulei Jiang ◽  
Lorenzo L. Pesce ◽  
Karen Drukker
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Early Stopping ◽  
Weight Decay ◽  
Noise Injection ◽  
Artificial Neural

Toward principled regularization of deep networks—From weight decay to feature contraction

2019 ◽  
Vol 4 (30) ◽  
pp. eaaw1329 ◽  
Author(s):  
Atsuto Maki
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Classification Problems ◽  
Weight Decay ◽  
Target Error ◽  
Deep Networks ◽  
Artificial Neural

Training deep artificial neural networks for classification problems may benefit from exploiting intrinsic class similarities by way of network regularization that compensates for a drawback in the commonly used target error.

FUTURE OF ARTIFICIAL NEURAL NETWORKS IN SPACE EXPLORATION AND SPACE ENGINEERING

2018 ◽  
pp. 6-11
Author(s):  
Kobiljon Kh. Zoidov ◽  
◽  
Svetlana V. Ponomareva ◽  
Daniel I. Serebryansky ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Space Exploration ◽  
Artificial Neural
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