An Encrypted Neural Network Learning to Build Safe Trained Model

2018 ◽  
Vol 06 (01) ◽  
pp. 32-36
Author(s):  
S. S. Sayyad ◽  
D. B. Kulkarni
Keyword(s):  
Neural Network ◽  
Neural Network Learning ◽  
Network Learning

Speed up Neural Network Learning by GPGPU

2011 ◽  
Vol 131 (11) ◽  
pp. 1889-1894
Author(s):  
Yuta Tsuchida ◽  
Michifumi Yoshioka
Keyword(s):  
Neural Network ◽  
Neural Network Learning ◽  
Network Learning ◽  
Speed Up

scholarly journals Fully Decoupled Neural Network Learning Using Delayed Gradients

2021 ◽  
pp. 1-8
Author(s):  
Huiping Zhuang ◽  
Yi Wang ◽  
Qinglai Liu ◽  
Zhiping Lin
Keyword(s):  
Neural Network ◽  
Neural Network Learning ◽  
Network Learning

scholarly journals A Geometric Perspective on Information Plane Analysis

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 711
Author(s):  
Mina Basirat ◽  
Bernhard C. Geiger ◽  
Peter M. Roth
Keyword(s):  
Neural Network ◽  
Mutual Information ◽  
Geometric Interpretation ◽  
Neural Network Training ◽  
Neural Network Learning ◽  
Network Learning ◽  
Plane Analysis ◽  
Network Training ◽  
Hidden Layer ◽  
The Impact

Information plane analysis, describing the mutual information between the input and a hidden layer and between a hidden layer and the target over time, has recently been proposed to analyze the training of neural networks. Since the activations of a hidden layer are typically continuous-valued, this mutual information cannot be computed analytically and must thus be estimated, resulting in apparently inconsistent or even contradicting results in the literature. The goal of this paper is to demonstrate how information plane analysis can still be a valuable tool for analyzing neural network training. To this end, we complement the prevailing binning estimator for mutual information with a geometric interpretation. With this geometric interpretation in mind, we evaluate the impact of regularization and interpret phenomena such as underfitting and overfitting. In addition, we investigate neural network learning in the presence of noisy data and noisy labels.

LOCALLY IMPLEMENTABLE MATRIX FLOWS AND THEIR APPLICATION TO NEURAL NETWORK LEARNING

1994 ◽  
Vol 04 (01) ◽  
pp. 23-51 ◽  
Author(s):  
JEROEN DEHAENE ◽  
JOOS VANDEWALLE
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Network Structure ◽  
Adaptive Signal Processing ◽  
External Signal ◽  
Neural Network Learning ◽  
Network Learning ◽  
Adaptive Signal

A number of matrix flows, based on isospectral and isodirectional flows, is studied and modified for the purpose of local implementability on a network structure. The flows converge to matrices with a predefined spectrum and eigenvectors which are determined by an external signal. The flows can be useful for adaptive signal processing applications and are applied to neural network learning.

Generalization of the backpropagation neural network learning algorithm to permit complex weights

1990 ◽  
Vol 29 (11) ◽  
pp. 1591 ◽  
Author(s):  
Gordon R. Little ◽  
Steven C. Gustafson ◽  
Robert A. Senn
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Backpropagation Neural Network ◽  
Neural Network Learning ◽  
Network Learning
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