Generative Modeling

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39 papers in the last four weeks, up 144% on the four weeks before. 0.4% of all new papers.

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Latest papers 347

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Proximal-Based Generative Modeling for Bayesian Inverse Problems
May 13, 2026Boyang Zhang, Zhiguo Wang, Ya-Feng LiuScore Matching Bayesian Inverse Problems
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing, China · Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China · School of Mathematics, Sichuan University, Chengdu, China +2
Generative Modeling of Approximately Periodic Time Series by a Posterior-Weighted Gaussian Process
May 13, 2026Elias Reich, Saverio Messineo, Stefan HuberTime Series Generation Gaussian Processes
Josef Ressel Centre for Intelligent and Secure Industrial Automation Salzburg University of Applied Sciences, Salzburg, Austria
Asymmetric Flow Models
May 13, 2026Hansheng Chen, Jan Ackermann, Minseo Kim +2Flow Matching T2I Generation
Stanford University
Discrete MeanFlow: One-Step Generation via Conditional Transition Kernels
May 12, 2026Fairoz Nower Khan, Nabuat Zaman Nahim, Md Sajid Ahmed +2Markov Models MeanFlow
Department of Computer Science, University of Kentucky Lexington, Kentucky 40506, USA
Constraint-Aware Flow Matching: Decision Aligned End-to-End Training for Constrained Sampling
May 12, 2026Jacob K. Christopher, James E. Warner, Ferdinando FiorettoConstrained Generative Modeling Flow Matching
University of Virginia · NASA Langley Research Center
Machine Learning for neutron source distributions
May 12, 2026Jose Ignacio Robledo, Norberto Schmidt, Klaus Lieutenant +3Generative Modeling Diffusion Models
Jülich Centre for Neutron Science (JCNS-2), Forschungszentrum Jülich, Jülich, Germany · Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich, Jülich, Germany
When Policy Entropy Constraint Fails: Preserving Diversity in Flow-based RLHF via Perceptual Entropy
May 12, 2026Xiaofeng Tan, Jun Liu, Bin-Bin Gao +5Flow Matching Generative Modeling
Southeast University · Tencent Youtu Lab · Southern University of Science and Technology
Assessment of cloud and associated radiation fields from a GAN stochastic cloud subcolumn generator
May 12, 2026Dongmin Lee, Lazaros Oreopoulos, Nayeong Cho +1Generative Modeling Conditional Generative Modeling
GESTAR-II, Morgan State University, Baltimore, MD, USA. · Earth Sciences Division, NASA’s Goddard Space Flight Center, Greenbelt, MD, USA. · GESTAR-II, University of Maryland – Baltimore County, MD, USA.
Sobolev Regularized MMD Gradient Flow
May 12, 2026Chenyang Tian, Bharath K. Sriperumbudur, Arthur Gretton +1Maximum Mean Discrepancy Generative Modeling
Tsinghua University · Pennsylvania State University · University College London +1
One-Step Generative Modeling via Wasserstein Gradient Flows
May 12, 2026Jiaqi Han, Puheng Li, Qiushan Guo +3Wasserstein Gradient Flows Image Generation
Stanford University · ByteDance
Generative climate downscaling enables high-resolution compound risk assessment by preserving multivariate dependencies
May 12, 2026Takuro Kutsuna, Noriko N. Ishizaki, Norihiro Oyama +1Climate Science Climate Modeling
Toyota Central R&D Labs., Inc. · National Institute for Environmental Studies
LPDP: Inference-Time Reward Control for Variable-Length DNA Generation with Edit Flows
May 12, 2026Jeongchan Kim, Yunkyung Ko, Jong Chul YeInference-Time Search Generative Modeling
KAIST AI
Gradient-Free Noise Optimization for Reward Alignment in Generative Models
May 12, 2026Jeongsol Kim, Hongeun Kim, Jian Wang +1Diffusion Model Alignment Stochastic Optimal Control
KAIST AI1 · Snap Inc.2
Enforcing Constraints in Generative Sampling via Adaptive Correction Scheduling
May 11, 2026Noah Trupin, Yexiang XueConstrained Generative Modeling Diffusion Sampling
Department of Computer Science, Purdue University
Kernel-Gradient Drifting Models
May 11, 2026Maria Esteban-Casadevall, Jorge Carrasco-Pollo, Max Welling +3Synthetic Data Generation One-Step Generative Modeling
AMLab · University of Amsterdam · CuspAI +1
diffGHOST: Diffusion based Generative Hedged Oblivious Synthetic Trajectories
May 11, 2026Florent Guépin, Cheick Tidiani Cisse, Denis Renaud +2Trajectory Generation Synthetic Data Generation
Orange Research, Belfort, France · Orange Business, Belfort, France
GenMed: A Pairwise Generative Reformulation of Medical Diagnostic Tasks
May 11, 2026Hantao Zhang, Weidong Guo, Yuhe Liu +5Cross-Modality Medical Image Segmentation Diffusion Model Guidance
CVLab, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland · Fudan University, Shanghai, China · Beihang University, Beijing, China +3
Beyond Spatial Compression: Interface-Centric Generative States for Open-World 3D Structure
May 11, 2026Xiang Chen, Alexander Binder3D Asset Generation 3D Spatial Reasoning
DSC ScaDS.AI, Leipzig University · Institute for Cancer Genetics and Informatics (ICGI), Oslo, Norway · ICT Cluster, Singapore Institute of Technology, Singapore
A Stability Benchmark of Generative Regularizers for Inverse Problems
May 11, 2026Alexander Denker, Johannes Hertrich, Sebastian NeumayerGenerative Modeling Diffusion-Based Inverse Problems
Helmholtz Imaging, Deutsches Elektronen-Synchrotron DESY, Germany · ENS Paris & Inria, France · Chemnitz University of Technology, Germany
The two clocks and the innovation window: When and how generative models learn rules
May 11, 2026Binxu Wang, Emma Lucia Byrnes Finn, Bingbin LiuMemorization in Generative Models Generative Modeling
Kempner Institute at Harvard University
Yeti: A compact protein structure tokenizer for reconstruction and multi-modal generation
May 11, 2026Nabin Giri, Steven Farrell, Kristofer E. BouchardGenerative Modeling Multimodal Generation
Lawrence Berkeley National Laboratory
Spherical Boltzmann machines: a solvable theory of learning and generation in energy-based models
May 9, 2026Thomas Tulinski, Simona Cocco, Rémi Monasson +1Statistical Physics of Learning Phase Transitions
Laboratoire de Physique de l’École Normale Supérieure, PSL, CNRS UMR8023, Sorbonne Université, 24 rue Lhomond, 75005 Paris, France · Institut de Physique Théorique, Université Paris-Saclay,2026 CNRS UMR3681, CEA, Gif-sur-Yvette, France
Discrete Flow Matching: Convergence Guarantees Under Minimal Assumptions
May 9, 2026Le-Tuyet-Nhi Pham, Giovanni Conforti, Zhenjie Ren +1Flow Matching Generative Modeling
École Polytechnique, CMAP, IP Paris Route de Saclay, 91120 Palaiseau, France · Università degli Studi di Padova, Via Trieste, 63, 35131 Padova, Italia · LaMME, Université Évry Paris-Saclay Boulevard François Mitterrand 91000, France
Deterministic Decomposition of Stochastic Generative Dynamics
May 9, 2026Xingyu Song, Yuan Mei, Naoya TakeishiFlow Matching Stochastic Differential Equations
1The University of Tokyo · 2Zhejiang University · *Visiting student at The University of Tokyo from Zhejiang University.
Generalized Wasserstein Flow Matching: Transport Plans, Everywhere, All at Once
May 8, 2026Moritz Piening, Richard Duong, Gabriele SteidlFlow Matching Generative Modeling
Institut für Mathematik, Technische Universität Berlin, Germany
Exploring and Exploiting Stability in Latent Flow Matching
May 8, 2026Rania Briq, Michael Kamp, Ohad Fried +2Flow Matching Efficient Inference
Forschungszentrum Jülich · Technical University Dortmund · Lamarr Institute +4
Structured Coupling for Flow Matching
May 8, 2026Xavier Sumba, Carles Balsells-Rodas, Yingzhen LiFlow Matching Disentangled Representation Learning
Imperial College London
Generative Modeling with Flux Matching
May 8, 2026Peter Pao-Huang, Xiaojie Qiu, Stefano ErmonGenerative Modeling Score-Based Generative Modeling
Department of Computer Science, Stanford University · Department of Genetics, Stanford University
Coupling Models for One-Step Discrete Generation
May 8, 2026Fred Zhangzhi Peng, Avishek Joey Bose, Anru R. Zhang +1One-Step Generative Modeling Generative Modeling
Duke University · Imperial College London · AITHYRA
Conservative Flows: A New Paradigm of Generative Models
May 7, 2026Eshed Gal, Md Shahriar Rahim Siddiqui, Moshe Eliasof +1Langevin Dynamics Generative Flow Networks
The University of British Columbia Vancouver, Canada · University of Cambridge Cambridge, United Kingdom

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Proximal-Based Generative Modeling for Bayesian Inverse Problems

Boyang Zhang, Zhiguo Wang, Ya-Feng Liu

School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing, China · Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China · School of Mathematics, Sichuan University, Chengdu, China+2

Score Matching Bayesian Inverse Problems Generative Modeling Diffusion-Based Inverse Problems Score-Based Generative Modeling

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Score-based diffusion models demonstrate superior performance in generative tasks but encounter fundamental bottlenecks in inverse problems due to the analytical intractability of the time-dependent likelihood score. To bridge this gap, we propose a novel proximal-based generative modeling (PGM) framework that rigorously circumvents explicit likelihood evaluation. Our framework is built upon a theoretical equivalence between Gaussian convolution in diffusion processes and Moreau-Yosida regularization in nonsmooth optimization. This enables a new sampling mechanism driven by the proposed Moreau score, which admits a closed-form expression via proximal operators. Moreover, we introduce Moreau score matching to learn the proximal operators that rely solely on samples drawn from the prior distribution. Theoretically, PGM eliminates the early-stopping bias inherent in the score-based diffusion model and achieves non-asymptotic convergence. Experiments demonstrate that PGM significantly surpasses state-of-the-art methods in reconstruction quality and sampling time.

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Latest papers 347