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
Generative Modeling: A Review
Nick Polson, Vadim Sokolov
A Review of Latent Representation Models in Neuroimaging
C. Vázquez-García, F. J. Martínez-Murcia, F. Segovia Román, Juan M. Górriz
Machine Learning and Deep Learning Techniques used in Cybersecurity and Digital Forensics: a Review
Jaouhar Fattahi
Accelerating process control and optimization via machine learning: A review
Ilias Mitrai, Prodromos Daoutidis
A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future
Shilin Sun, Wenbin An, Feng Tian, Fang Nan, Qidong Liu, Jun Liu, Nazaraf Shah, Ping Chen
Are LLMs Good Literature Review Writers? Evaluating the Literature Review Writing Ability of Large Language Models
Xuemei Tang, Xufeng Duan, Zhenguang G. Cai
Toward an Insider Threat Education Platform: A Theoretical Literature Review
Haywood Gelman, John D. Hastings, David Kenley, Eleanor Loiacono