Analysis of Neural Network Training and Cost Functions Impact on the Accuracy of IDS and SIEM Systems

2019 ◽  
pp. 433-451 ◽  
Author(s):  
Said El Hajji ◽  
Nabil Moukafih ◽  
Ghizlane Orhanou
Keyword(s):  
Neural Network ◽  
Cost Functions ◽  
Neural Network Training ◽  
Network Training

A comparative case study of neural network training by using frame-level cost functions for automatic speech recognition purposes in Spanish

2020 ◽  
Vol 79 (27-28) ◽  
pp. 19669-19715
Author(s):  
Aldonso Becerra ◽  
J. Ismael de la Rosa ◽  
Efrén González ◽  
A. David Pedroza ◽  
N. Iracemi Escalante ◽  
...  
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Cost Functions ◽  
Comparative Case Study ◽  
Neural Network Training ◽  
Network Training

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.

scholarly journals An Adaptive Memory Multi-Batch L-BFGS Algorithm for Neural Network Training

2020 ◽  
Vol 53 (2) ◽  
pp. 8199-8204
Author(s):  
Federico Zocco ◽  
Seán McLoone
Keyword(s):  
Neural Network ◽  
Adaptive Memory ◽  
Neural Network Training ◽  
Network Training

scholarly journals Optimal uncertainty-guided neural network training

2020 ◽  
pp. 106878
Author(s):  
H. M. Dipu Kabir ◽  
Abbas Khosravi ◽  
Abdollah Kavousi-Fard ◽  
Saeid Nahavandi ◽  
Dipti Srinivasan
Keyword(s):  
Neural Network ◽  
Neural Network Training ◽  
Network Training
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