Score-Based Generative Modeling

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6 papers in the last four weeks, with none the four weeks before. 0.1% of all new papers.

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Score-based diffusion models for severely ill-posed problems in diffuse optical tomography
Feb 3, 2026Fabian Schneider, Meghdoot Mozumder, Konstantin Tamarov +4Medical Imaging Bayesian Inverse Problems
Department of Computational Engineering, School of Engineering Science LUT University, Finland. · Institute of Analysis and Scientific Computing, TU Wien, Austria. · Department of Technical Physics, Faculty of Science, Forestry and Technology, University of Eastern Finland, Finland. +1
Fast Score-Based Sampling via Log-Concave Reductions
Dec 30, 2025M. J. WainwrightSample Complexity Log-Concave Sampling
Laboratory for Information and Decision Systems · Statistics and Data Science Center · EECS & Mathematics +1
Control Variate Score Matching for Diffusion Models
Dec 23, 2025Khaled Kahouli, Romuald Elie, Klaus-Robert Müller +3Score Matching Diffusion Model Sampling
1Google DeepMind · 2BIFOLD – Berlin Institute for the Foundations of Learning and Data · 3Machine Learning Group, Technische Universität Berlin +2
Diffusion Models in Simulation-Based Inference: A Tutorial Review
Dec 22, 2025Jonas Arruda, Niels Bracher, Ullrich Köthe +2Simulation-Based Inference Parameter Estimation
Bonn Center for Mathematical Life Sciences, Life & Medical Sciences Institute, University of Bonn, Germany · Center for Modeling, Simulation, & Imaging in Medicine (CeMSIM), Rensselaer Polytechnic Institute, NY, USA · Computer Vision and Learning Lab, Heidelberg University, Germany
Local Learning Rules for Out-of-Equilibrium Physical Generative Models
Jun 23, 2025Cyrill Bösch, Geoffrey Roeder, Marc Serra-Garcia +1Generative Modeling Score-Based Generative Modeling
Department of Computer Science, Princeton University, Princeton, NJ 08540, USA · AMOLF, Science Park 104, 1098 XG Amsterdam, The Netherlands
The Effect of Stochasticity in Score-Based Diffusion Sampling: a KL Divergence Analysis
Jun 13, 2025Bernardo P. Schaeffer, Ricardo M. S. Rosa, Glauco ValleStochastic Differential Equations Diffusion Sampling
Instituto de Matem´atica, Universidade Federal do Rio de Janeiro
ScoreMix: Synthetic Data Generation by Score Composition in Diffusion Models Improves Recognition
Jun 11, 2025Parsa Rahimi, Sebastien MarcelSynthetic Data Augmentation Synthetic Data Generation
1EPFL, Switzerland · 2Idiap, Switzerland
Localized Diffusion Models
May 7, 2025Georg A. Gottwald, Shuigen Liu, Youssef Marzouk +2Score-Based Generative Modeling Diffusion Models
School of Mathematics and Statistics, The University of Sydney · Department of Mathematics, National University of Singapore · Laboratory for Information and Decision Systems, Massachusetts Institute of Technology +1
Riemannian Denoising Diffusion Probabilistic Models
May 7, 2025Zichen Liu, Wei Zhang, Christof Schütte +1Generative Modeling Score-Based Generative Modeling
Center for Data Science, Peking University, Beijing 100871, P.R. China · Zuse Institute Berlin, Takustrasse 7, Berlin 14195, Germany · Institute of Mathematics, Freie Universität Berlin and Zuse Institute Berlin, Takustrasse 7, Berlin 14195, Germany +1
Robustness and Structure Preservation in Flow-Based Generative Models via Wasserstein Path-Space Divergences
Oct 2, 2024Ziyu Chen, Markos A. Katsoulakis, Benjamin J. ZhangFlow Matching Equivariant Neural Networks
Department of Mathematics and School of Data and Information Sciences, University of North Carolina at Chapel Hill · Department of Mathematics and Statistics, University of Massachusetts Amherst · Department of Mathematics, Rutgers University–New Brunswick
Smoothing the Score Function to Enhance Generalization in Diffusion Models
Date pendingXinyu Zhou, Jiawei Zhang, Stephen J. WrightMemorization in Generative Models Generative Modeling
Department of Computer Sciences, University of Wisconsin Madison, WI, USA.

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Score-based diffusion models for severely ill-posed problems in diffuse optical tomography

Fabian Schneider, Meghdoot Mozumder, Konstantin Tamarov, Leila Taghizadeh+3

Department of Computational Engineering, School of Engineering Science LUT University, Finland. · Institute of Analysis and Scientific Computing, TU Wien, Austria. · Department of Technical Physics, Faculty of Science, Forestry and Technology, University of Eastern Finland, Finland.+1

Medical Imaging Bayesian Inverse Problems Diffusion-Based Inverse Problems Score-Based Generative Modeling Diffusion Posterior Sampling

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Score-based diffusion models are a recently developed framework for posterior sampling in Bayesian inverse problems, enabling high-quality reconstructions in inverse problems by leveraging expressive prior distributions learned from empirical data. Despite their strong empirical performance and growing interest within the machine learning community, their behaviour in realistic, severely ill-posed inverse problems with experimental measurement data remains under-explored. Diffuse optical tomography (DOT) is an inverse boundary value problem that uses boundary measurements of near-infrared light to recover spatially varying absorption and scattering parameters in biological tissue. The problem is highly ill-posed and particularly sensitive to both measurement noise and modelling errors. We introduce a regularization strategy by constructing a mixed score consisting of a learned component and a model-based component. We show that the resulting mixed score approximates the score of a corresponding mixture distribution locally and in the small diffusion-time regime, providing a theoretical justification for the approach. We compare four approaches for difference imaging in DOT: a classical model-based method, an approximate score-based diffusion method (DPS), an exact posterior sampling method (UCoS) and a novel, regularized version of UCoS. We show that both the model-based approach and approximate diffusion-based sampling degrade significantly in the presence of limited-view geometry and real experimental data, whereas UCoS yields more accurate reconstructions.

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