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
Estimating causal effects with optimization-based methods: A review and empirical comparison
Martin Cousineau, Vedat Verter, Susan A. Murphy, Joelle Pineau
Hyperbolic Graph Neural Networks: A Review of Methods and Applications
Menglin Yang, Min Zhou, Zhihao Li, Jiahong Liu, Lujia Pan, Hui Xiong, Irwin King
Review of research on fireworks algorithm
Zhao Zhigang, Li Zhimei, Mo Haimiao, Zeng Min
Matching Papers and Reviewers at Large Conferences
Kevin Leyton-Brown, Mausam, Yatin Nandwani, Hedayat Zarkoob, Chris Cameron, Neil Newman, Dinesh Raghu
Transformers in Medical Image Analysis: A Review
Kelei He, Chen Gan, Zhuoyuan Li, Islem Rekik, Zihao Yin, Wen Ji, Yang Gao, Qian Wang, Junfeng Zhang, Dinggang Shen