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
Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges
Arvind Srivastav, Soumyajit Mandal
Enhanced Sampling with Machine Learning: A Review
Shams Mehdi, Zachary Smith, Lukas Herron, Ziyue Zou, Pratyush Tiwary
Towards Interpretability in Audio and Visual Affective Machine Learning: A Review
David S. Johnson, Olya Hakobyan, Hanna Drimalla