most citedLearning in Convolutional Neural Networks Accelerated by Transfer Entropy

5 citations · 9 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2024

Concept Drift Visualization of SVM with Shifting Window

Honorius Galmeanu, Razvan Andonie

In machine learning, concept drift is an evolution of information that invalidates the current data model. It happens when the statistical properties of the input data change over…

cs.SD2024

Emotion Manipulation Through Music -- A Deep Learning Interactive Visual Approach

Adel N. Abdalla, Jared Osborne, Razvan Andonie

Music evokes emotion in many people. We introduce a novel way to manipulate the emotional content of a song using AI tools. Our goal is to achieve the desired emotion while leaving…

cs.LG20242 cited

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…

cs.LG20245 cited

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…

cs.LG20242 cited

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…