DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models
School of Computer and Information Technology, Beijing Jiaotong University（北京交通大学计算机与信息技术学院）；National University of Singapore（新加坡国立大学）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Spatio-temporal graph neural networks (STGNN) have emerged as the dominant model for spatio-temporal graph (STG) forecasting. Despite their success, they fail to model intrinsic uncertainties within STG data, which cripples their practicality in downstream tasks for decision-making. To this end, this paper focuses on probabilistic STG forecasting, which is challenging due to the difficulty in modeling uncertainties and complex ST dependencies. In this study, we present the first attempt to generalize the popular denoising diffusion probabilistic models to STGs, leading to a novel non-autoregressive framework called DiffSTG, along with the first denoising network UGnet for STG in the framework. Our approach combines the spatio-temporal learning capabilities of STGNNs with the uncertainty measurements of diffusion models. Extensive experiments validate that DiffSTG reduces the Continuous Ranked Probability Score (CRPS) by 4%-14%, and Root Mean Squared Error (RMSE) by 2%-7% over existing methods on three real-world datasets.
Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning
National University of Singapore（新加坡国立大学）；University of Technology Sydney（悉尼科技大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Generative AI Enables EEG Super-Resolution via Spatio-Temporal Adaptive Diffusion Learning
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；The University of Hong Kong（香港大学）；Guangdong University of Technology（广东工业大学）
Generative diffusion models for spatiotemporal influenza forecasting
Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill（流行病学系，全球公共卫生学院，北卡罗来纳大学查佩尔希尔分校）；Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA（流行病学系，约翰·霍普金斯伯恩斯坦公共卫生学院，巴尔的摩，MD 21205, USA）；Carolina Population Center, University of North Carolina at Chapel Hill（卡罗来纳人口中心，北卡罗来纳大学查佩尔希尔分校）
Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Photovoltaic Timeseries Data Imputation
SunPower Corporation（SunPower公司）；SolarEdge Technologies（SolarEdge科技公司）；Case Western Reserve University（凯斯西储大学）；C2 Energy Capital（C2能源资本公司）；Sandia National Labs（桑迪亚国家实验室）
STeP-Diff: Spatio-Temporal Physics-Informed Diffusion Models for Mobile Fine-Grained Pollution Forecasting
Shenzhen International Graduate School, Tsinghua University, China（清华大学深圳国际研究生院）；Department of Electronic Engineering, Beijing National Research Center for Information Science and Tech- nology (BNRist), Tsinghua University, China（电子工程系，北京信息科学与技术国家研究中心（BNRist），清华大学）；Shenzhen Smartcity Communication, Shenzhen, China（深圳智慧城市通信）；College of Soil and Water Conservation, Southwest Forestry University, China（水土保持学院，西南林业大学）；College of Civil Engineering, Southwest Forestry University, China（土木工程学院，西南林业大学）
Dynamic Population Distribution Aware Human Trajectory Generation with Diffusion Model
Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University（电子工程系，北京信息科学与技术国家研究中心（BNRist），清华大学）
Learning Temporally Consistent Turbulence Between Sparse Snapshots via Diffusion Models
School of Engineering, The University of Manchester（工程学院，曼彻斯特大学）；School of Mathematics, The University of Manchester（数学学院，曼彻斯特大学）；IBM Research Europe, Daresbury, Warrington（IBM欧洲研究院，达尔斯伯里，沃林顿）
ProGen: Revisiting Probabilistic Spatial-Temporal Time Series Forecasting from a Continuous Generative Perspective Using Stochastic Differential Equations
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
DeStripe: A Self2Self Spatio-Spectral Graph Neural Network with Unfolded Hessian for Stripe Artifact Removal in Light-sheet Microscopy
Technical University of Munich（慕尼黑工业大学）；Georg-August-University Goettingen（哥廷根大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Institute of AI for Health, Helmholtz Munich - German Research Center for Environmental Health（亥姆霍兹慕尼黑中心-德国环境健康研究中心健康人工智能研究所）；Helmholtz AI, Helmholtz Munich - German Research Center for Environmental Health（亥姆霍兹慕尼黑中心-德国环境健康研究中心亥姆霍兹人工智能）
