5 citations · 9 across the 3 of their papers we have counts for
4 papers
Transfer Entropy in Graph Convolutional Neural Networks
Adrian Moldovan, Angel Caţaron, Răzvan 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…
Learning in Convolutional Neural Networks Accelerated by Transfer Entropy
Adrian Moldovan, Angel Caţaron, Răzvan 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…
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…
Learning in Feedforward Neural Networks Accelerated by Transfer Entropy
Adrian Moldovan, Angel Caţaron, Răzvan Andonie
Current neural networks architectures are many times harder to train because of the increasing size and complexity of the used datasets. Our objective is to design more efficient t…