High Uncertainty Anticipation
High uncertainty anticipation focuses on developing methods to accurately quantify and manage uncertainty in model predictions across diverse fields, aiming to improve the reliability and trustworthiness of AI systems. Current research emphasizes integrating uncertainty estimation into various model architectures, including neural networks, diffusion models, and graph neural networks, often employing techniques like Bayesian methods, conformal prediction, and ensemble methods. This work is crucial for deploying AI in high-stakes applications like healthcare, autonomous driving, and finance, where reliable uncertainty quantification is paramount for safe and effective decision-making.
Papers
Expert Uncertainty and Severity Aware Chest X-Ray Classification by Multi-Relationship Graph Learning
Mengliang Zhang, Xinyue Hu, Lin Gu, Liangchen Liu, Kazuma Kobayashi, Tatsuya Harada, Ronald M. Summers, Yingying Zhu
Risk-reducing design and operations toolkit: 90 strategies for managing risk and uncertainty in decision problems
Alexander Gutfraind
Uncertainty and Explainable Analysis of Machine Learning Model for Reconstruction of Sonic Slowness Logs
Hua Wang, Yuqiong Wu, Yushun Zhang, Fuqiang Lai, Zhou Feng, Bing Xie, Ailin Zhao
Modeling Uncertainty and Using Post-fusion as Fallback Improves Retrieval Augmented Generation with LLMs
Ye Liu, Semih Yavuz, Rui Meng, Meghana Moorthy, Shafiq Joty, Caiming Xiong, Yingbo Zhou
Uncertainty in Natural Language Generation: From Theory to Applications
Joris Baan, Nico Daheim, Evgenia Ilia, Dennis Ulmer, Haau-Sing Li, Raquel Fernández, Barbara Plank, Rico Sennrich, Chrysoula Zerva, Wilker Aziz
A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond
Junyu Chen, Yihao Liu, Shuwen Wei, Zhangxing Bian, Shalini Subramanian, Aaron Carass, Jerry L. Prince, Yong Du