Abstract: One of the long-unsolved open problems in machine learning is imbuing machine learning algorithms with human-like cognitive reasoning capabilities. An essential aspect of cognitive reasoning ...

Introduction: Motor imagery electroencephalographic (MI-EEG) signal recognition is used in various brain–computer interface (BCI) systems. In most existing BCI systems, this identification relies on ...

Inductive reasoning is a critical skill that enables individuals to make sound decisions by drawing general conclusions from specific observations. Whether you’re working on a high-stakes business ...

Abstract: Identifying gene regulatory networks (GRNs) from gene expression data has been a critical focus in systems biology. This paper proposes an efficient inductive learning framework based on a ...

Furthermore, domain adaptation (DA) has been the most common TL method in general, whereas inductive transfer learning (ITL) has been rare. To the best of our knowledge, DA and ITL have never been ...

Machine learning research aims to learn representations that enable effective downstream task performance. A growing subfield seeks to interpret these representations’ roles in model behaviors or ...

Welcome to the latest installment in our series on blended learning. In our previous posts, we’ve explored what blended learning is and delved into its strengths and weaknesses. We’ve also examined ...

Graph Transformers (GTs) have successfully achieved state-of-the-art performance on various platforms. GTs can capture long-range information from nodes that are at large distances, unlike the local ...