3 papers
cs.LG2024
Transfer Entropy in Graph Convolutional Neural Networks
Adrian Moldovan, Angel CaÅ£aron, RÄzvan Andonie
Graph Convolutional Networks (GCN) are Graph Neural Networks where the convolutions are applied over a graph. In contrast to Convolutional Neural Networks, GCN's are designed to pe…
cs.LG2024
Learning in Convolutional Neural Networks Accelerated by Transfer Entropy
Adrian Moldovan, Angel CaÅ£aron, RÄzvan Andonie
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 b…
cs.LG2024
Information Plane Analysis Visualization in Deep Learning via Transfer Entropy
Adrian Moldovan, Angel Cataron, Razvan Andonie
In a feedforward network, Transfer Entropy (TE) can be used to measure the influence that one layer has on another by quantifying the information transfer between them during train…