Learning in Convolutional Neural Networks Accelerated by Transfer Entropy

arXiv:2404.02943v1 Announce Type: new
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. …

abstract artificial arxiv connectivity convolutional neural networks cs.ai cs.it cs.lg entropy focus math.it network networks neural networks neuron neurons relationships transfer type