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
Decentralized Multi-Robot Line-of-Sight Connectivity Maintenance under Uncertainty
Yupeng Yang, Yiwei Lyu, Yanze Zhang, Sha Yi, Wenhao Luo
UBENCH: Benchmarking Uncertainty in Large Language Models with Multiple Choice Questions
Xunzhi Wang, Zhuowei Zhang, Qiongyu Li, Gaonan Chen, Mengting Hu, Zhiyu li, Bitong Luo, Hang Gao, Zhixin Han, Haotian Wang
GW-MoE: Resolving Uncertainty in MoE Router with Global Workspace Theory
Haoze Wu, Zihan Qiu, Zili Wang, Hang Zhao, Jie Fu
Uncertainty modeling for fine-tuned implicit functions
Anna Susmelj, Mael Macuglia, Nataša Tagasovska, Reto Sutter, Sebastiano Caprara, Jean-Philippe Thiran, Ender Konukoglu
Physics-Constrained Learning for PDE Systems with Uncertainty Quantified Port-Hamiltonian Models
Kaiyuan Tan, Peilun Li, Thomas Beckers