Probabilistic Model
Probabilistic models are mathematical frameworks used to represent and reason under uncertainty, aiming to quantify the likelihood of different outcomes. Current research focuses on improving the efficiency and accuracy of these models across diverse applications, including generative AI (e.g., diffusion models, sum-product networks), uncertainty quantification in large language models, and robust inference in Bayesian networks. This work is significant because it enhances the reliability and interpretability of AI systems, leading to improved decision-making in various fields such as healthcare, finance, and scientific discovery.
Papers
Mixture of Efficient Diffusion Experts Through Automatic Interval and Sub-Network Selection
Alireza Ganjdanesh, Yan Kang, Yuchen Liu, Richard Zhang, Zhe Lin, Heng Huang
Probabilistically Aligned View-unaligned Clustering with Adaptive Template Selection
Wenhua Dong, Xiao-Jun Wu, Zhenhua Feng, Sara Atito, Muhammad Awais, Josef Kittler
The diameter of a stochastic matrix: A new measure for sensitivity analysis in Bayesian networks
Manuele Leonelli, Jim Q. Smith, Sophia K. Wright
Machine Learning for Complex Systems with Abnormal Pattern by Exception Maximization Outlier Detection Method
Zhikun Zhang, Yiting Duan, Xiangjun Wang, Mingyuan Zhang