Narrative Review
Narrative reviews synthesize existing research to provide a comprehensive overview of a specific topic, aiming to identify key trends, gaps, and future research directions. Current research focuses on applying narrative reviews across diverse fields, employing various model architectures like graph neural networks, large language models, and diffusion models to analyze complex data and improve model interpretability and efficiency. This approach is crucial for advancing scientific understanding and informing the development of practical applications in areas such as medicine, engineering, and manufacturing.
Papers
Digital staining in optical microscopy using deep learning -- a review
Lucas Kreiss, Shaowei Jiang, Xiang Li, Shiqi Xu, Kevin C. Zhou, Alexander Mühlberg, Kyung Chul Lee, Kanghyun Kim, Amey Chaware, Michael Ando, Laura Barisoni, Seung Ah Lee, Guoan Zheng, Kyle Lafata, Oliver Friedrich, Roarke Horstmeyer
Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review
Hanxun Jin, Enrui Zhang, Horacio D. Espinosa
Label-efficient Time Series Representation Learning: A Review
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, Xiaoli Li
Review of Deep Reinforcement Learning for Autonomous Driving
B. Udugama
Review on Efficient Strategies for Coordinated Motion and Tracking in Swarm Robotics
B. Udugama