The paper "Hyper-Diffusion: Estimating Epistemic and Aleatoric Uncertainty with a Single Model" by Matthew A. Chan, Maria J. Molina, and Christopher A. Metzler introduces a novel approach called hyper-diffusion to estimate both aleatoric and epistemic uncertainties using a single model. This method combines conditional denoising diffusion models and hyper-networks to accurately quantify uncertainty in high-stakes applications such as medical imaging and weather forecasting. Unlike traditional Monte Carlo dropout-based ensemble methods, hyper-diffusion offers the same prediction accuracy as multi-model ensembles while significantly reducing computational complexity. The authors validate their approach on two tasks: x-ray computed tomography (CT) reconstruction and weather temperature forecasting, demonstrating its effectiveness in providing accurate and useful uncertainty estimates. The paper also includes a detailed discussion of related work, problem definition, methodological details, and experimental results, highlighting the benefits and limitations of hyper-diffusion compared to other uncertainty estimation techniques.The paper "Hyper-Diffusion: Estimating Epistemic and Aleatoric Uncertainty with a Single Model" by Matthew A. Chan, Maria J. Molina, and Christopher A. Metzler introduces a novel approach called hyper-diffusion to estimate both aleatoric and epistemic uncertainties using a single model. This method combines conditional denoising diffusion models and hyper-networks to accurately quantify uncertainty in high-stakes applications such as medical imaging and weather forecasting. Unlike traditional Monte Carlo dropout-based ensemble methods, hyper-diffusion offers the same prediction accuracy as multi-model ensembles while significantly reducing computational complexity. The authors validate their approach on two tasks: x-ray computed tomography (CT) reconstruction and weather temperature forecasting, demonstrating its effectiveness in providing accurate and useful uncertainty estimates. The paper also includes a detailed discussion of related work, problem definition, methodological details, and experimental results, highlighting the benefits and limitations of hyper-diffusion compared to other uncertainty estimation techniques.