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
Deep Learning based Systems for Crater Detection: A Review
Atal Tewari, K Prateek, Amrita Singh, Nitin Khanna
Review of Machine Learning Methods for Additive Manufacturing of Functionally Graded Materials
Mohammad Karimzadeh, Aleksandar Vakanski, Fei Xu, Xinchang Zhang
A Primer on Bayesian Neural Networks: Review and Debates
Julyan Arbel, Konstantinos Pitas, Mariia Vladimirova, Vincent Fortuin
Deep Learning Techniques in Extreme Weather Events: A Review
Shikha Verma, Kuldeep Srivastava, Akhilesh Tiwari, Shekhar Verma
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery
Sam Khallaghi, J. Ronald Eastman, Lyndon D. Estes