Department or Administrative Unit
Computer Science
Document Type
Article
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Author Copyright
© 2021 by the authors.
Publication Date
9-16-2021
Journal
Entropy
Abstract
Recently, there is a growing interest in applying Transfer Entropy (TE) in quantifying the effective connectivity between artificial neurons. In a feedforward network, the TE can be used to quantify the relationships between neuron output pairs located in different layers. Our focus is on how to include the TE in the learning mechanisms of a Convolutional Neural Network (CNN) architecture. We introduce a novel training mechanism for CNN architectures which integrates the TE feedback connections. Adding the TE feedback parameter accelerates the training process, as fewer epochs are needed. On the flip side, it adds computational overhead to each epoch. According to our experiments on CNN classifiers, to achieve a reasonable computational overhead–accuracy trade-off, it is efficient to consider only the inter-neural information transfer of the neuron pairs between the last two fully connected layers. The TE acts as a smoothing factor, generating stability and becoming active only periodically, not after processing each input sample. Therefore, we can consider the TE is in our model a slowly changing meta-parameter.
Recommended Citation
Moldovan, A., Caţaron, A., & Andonie, R. (2021). Learning in Convolutional Neural Networks Accelerated by Transfer Entropy. Entropy, 23(9), 1218. https://doi.org/10.3390/e23091218
Comments
This article was originally published Open Access in Entropy. The full-text article from the publisher can be found here.