* Hierarchical Bayesian models: a framework for learning to learn, transfer learning, and multitask learning.
He focuses on transfer learning and multitask learning for natural language processing.
Detailed information on specific transfer learning solutions are presented in "Homogeneous transfer learning" "Heterogeneous transfer learning" and "Negative transfer" sections.
"Homogeneous transfer learning" "Heterogeneous transfer learning" and "Negative transfer" sections cover homogeneous transfer learning solutions, heterogeneous transfer learning solutions, and solutions addressing negative transfer, respectively.
"Homogeneous transfer learning" and "Heterogeneous transfer learning" sections provide solutions on homogeneous and heterogeneous transfer learning, "Negative transfer" section provides information on negative transfer as it pertains to transfer learning.
Based on different situations between the source and target domains and tasks, transfer learning is categorized into three subsettings: inductive transfer learning, transductive transfer learning, and unsupervised transfer learning [51].
Unlike previous surveys related to transfer learning, we focus distinctly on heterogeneous transfer learning and its related challenges.
Her research interests include rare category analysis, active learning, semisupervised learning, transfer learning and spam filtering.
This survey paper aims to provide a researcher interested in transfer learning with an overview of related works, examples of applications that are addressed by transfer learning, and issues and solutions that are relevant to the field of transfer learning.
This section explains various ongoing applications in field of transfer learning and deep learning.
