Learning Based Approach
Learning-based approaches leverage machine learning and deep learning to solve complex problems across diverse scientific and engineering domains. Current research focuses on developing and refining models such as deep reinforcement learning, convolutional neural networks, recurrent neural networks, and graph neural networks, often incorporating techniques like active learning and transfer learning to improve efficiency and generalization. These methods are proving valuable for applications ranging from materials discovery and robotics to resource optimization and medical image analysis, offering improved accuracy, efficiency, and interpretability compared to traditional methods. The impact is significant, enabling data-driven solutions to previously intractable problems and accelerating progress in various fields.
Papers
Impact of Initialization on Intra-subject Pediatric Brain MR Image Registration: A Comparative Analysis between SyN ANTs and Deep Learning-Based Approaches
Andjela Dimitrijevic, Vincent Noblet, Benjamin De Leener
Systematic Literature Review on Application of Learning-based Approaches in Continuous Integration
Ali Kazemi Arani, Triet Huynh Minh Le, Mansooreh Zahedi, M. Ali Babar
A Learning-based Declarative Privacy-Preserving Framework for Federated Data Management
Hong Guan, Summer Gautier, Rajan Hari Ambrish, Yancheng Wang, Chaowei Xiao, Yingzhen Yang, Jia Zou
Admission Prediction in Undergraduate Applications: an Interpretable Deep Learning Approach
Amisha Priyadarshini, Barbara Martinez-Neda, Sergio Gago-Masague