1 citations · 1 across the 3 of their papers we have counts for
6 papers
ESS-Flow: Training-free guidance of flow-based models as inference in source space
Adhithyan Kalaivanan, Zheng Zhao, Jens Sjölund +1
Guiding pretrained flow-based generative models for conditional generation or to produce samples with desired target properties enables solving diverse tasks without retraining on…
Discriminative Ordering Through Ensemble Consensus
Louis Ohl, Fredrik Lindsten
Evaluating the performance of clustering models is a challenging task where the outcome depends on the definition of what constitutes a cluster. Due to this design, current existin…
Building Machine Learning Limited Area Models: Kilometer-Scale Weather Forecasting in Realistic Settings
Simon Adamov, Joel Oskarsson, Leif Denby +8
Machine learning is revolutionizing global weather forecasting, with models that efficiently produce highly accurate forecasts. Apart from global forecasting there is also a large…
Diffusion-LAM: Probabilistic Limited Area Weather Forecasting with Diffusion
Erik Larsson, Joel Oskarsson, Tomas Landelius +1
Machine learning methods have been shown to be effective for weather forecasting, based on the speed and accuracy compared to traditional numerical models. While early efforts prim…
WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry
Filip Ekström Kelvinius, Oskar B. Andersson, Abhijith S. Parackal +3
Crystalline materials often exhibit a high level of symmetry. However, most generative models do not account for symmetry, but rather model each atom without any constraints on its…
Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo
Filip Ekström Kelvinius, Zheng Zhao, Fredrik Lindsten
A recent line of research has exploited pre-trained generative diffusion models as priors for solving Bayesian inverse problems. We contribute to this research direction by designi…