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
Application of probabilistic modeling and automated machine learning framework for high-dimensional stress field
Lele Luan, Nesar Ramachandra, Sandipp Krishnan Ravi, Anindya Bhaduri, Piyush Pandita, Prasanna Balaprakash, Mihai Anitescu, Changjie Sun, Liping Wang
Generating symbolic music using diffusion models
Lilac Atassi
Denoising diffusion probabilistic models for probabilistic energy forecasting
Esteban Hernandez Capel, Jonathan Dumas
Image Inpainting via Iteratively Decoupled Probabilistic Modeling
Wenbo Li, Xin Yu, Kun Zhou, Yibing Song, Zhe Lin, Jiaya Jia
PoissonMat: Remodeling Matrix Factorization using Poisson Distribution and Solving the Cold Start Problem without Input Data
Hao Wang