Key papers

• Score matching for bridges without learning time-reversals (2024)
  Elizabeth L. Baker et al.
  2.32
• Reflected UAS: Corrected Deterministic Stability and Direct CTMC Drift Calculation (2026)
  Krishna Subedi
  1.94
• PTL-Diffusion: Manifold-Aware Diffusion with Periodic Terminal Laws (2026)
  Danqi Zhuang et al.
  1.89
• Expressivity of Bi-Lipschitz Normalizing Flows: A Score-Based Diffusion Perspective (2026)
  Meira Iske and Carola-Bibiane Sch\"onlieb
  1.83
• Generalized Wasserstein Flow Matching: Transport Plans, Everywhere, All at Once (2026)
  Moritz Piening et al.
  1.83
• Learning stochastic multiscale models through normalizing flows (2026)
  Anan Saha et al.
  1.83
• On the Limits of Latent Reuse in Diffusion Models (2026)
  Yifeng Yu et al.
  1.83
• Simple Approximation and Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures (2026)
  Chenyang Wang and Weizhong Wang and Yinuo Ren and Jose Blanchet and Yiping Lu
  1.83
• A note on connections between the F\"ollmer process and the denoising diffusion probabilistic model (2026)
  Yuta Koike
  1.83
• Wasserstein bounds for denoising diffusion probabilistic models via the F\"ollmer process (2026)
  Yuta Koike
  1.83
• SURGE: Approximation and Training Free Particle Filter for Diffusion Surrogate (2026)
  Lifu Wei and Yinuo Ren and Naichen Shi and Yiping Lu
  1.83
• Generating DDPM-based Samples from Tilted Distributions (2026)
  Himadri Mandal et al.
  1.78
• Lipschitz regularity in Flow Matching and Diffusion Models: sharp sampling rates and functional inequalities (2026)
  Arthur St\'ephanovitch
  1.78
• Diffusion Processes on Implicit Manifolds (2026)
  Victor Kawasaki-Borruat et al.
  1.78
• One-Shot Generative Flows: Existence and Obstructions (2026)
  Panos Tsimpos et al.
  1.78
• Neural Continuous-Time Markov Chain: Discrete Diffusion via Decoupled Jump Timing and Direction (2026)
  Jingyuan Li et al.
  1.78
• Total Variation Guarantees for Sampling with Stochastic Localization (2026)
  Jakob Kellermann
  1.72
• Diffusion Model's Generalization Can Be Characterized by Inductive Biases toward a Data-Dependent Ridge Manifold (2026)
  Ye He et al.
  1.67
• Constructive conditional normalizing flows (2026)
  Borjan Geshkovski and Dom\`enec Ruiz-Balet
  1.67
• Variational Optimality of F\"ollmer Processes in Generative Diffusions (2026)
  Yifan Chen et al.
  1.67
• Neural Diffusion Intensity Models for Point Process Data (2026)
  Xinlong Du and Harsha Honnappa and Vinayak Rao
  1.67
• Diffusion Models with Heavy-Tailed Targets: Score Estimation and Sampling Guarantees (2026)
  Yifeng Yu et al.
  1.61
• Spectral Diffusion Models on the Sphere (2026)
  Pierpaolo Brutti et al.
  1.61
• Provable Diffusion Posterior Sampling for Bayesian Inversion (2025)
  Jinyuan Chang et al.
  1.56
• Random-Bridges as Stochastic Transports for Generative Models (2025)
  Stefano Goria et al.
  1.56
• On The Hidden Biases of Flow Matching Samplers (2025)
  Soon Hoe Lim
  1.56
• A Review of Diffusion-based Simulation-Based Inference: Foundations and Applications in Non-Ideal Data Scenarios (2025)
  Haley Rosso et al.
  1.56
• Parallel Sampling via Autospeculation (2025)
  Nima Anari et al.
  1.50
• Diffusion annealed Langevin dynamics: a theoretical study (2025)
  Patrick Cattiaux et al.
  1.50
• Demystifying MaskGIT Sampler and Beyond: Adaptive Order Selection in Masked Diffusion (2025)
  Satoshi Hayakawa et al.
  1.44
• Permutation-Invariant Spectral Learning via Dyson Diffusion (2025)
  Tassilo Schwarz et al.
  1.44
• A Free Probabilistic Framework for Denoising Diffusion Models: Entropy, Transport, and Reverse Processes (2025)
  Swagatam Das
  1.44
• Scale-Adaptive Generative Flows for Multiscale Scientific Data (2025)
  Yifan Chen et al.
  1.39
• Sig-DEG for Distillation: Making Diffusion Models Faster and Lighter (2025)
  Lei Jiang et al.
  1.33
• A Malliavin calculus approach to score functions in diffusion generative models (2025)
  Ehsan Mirafzali et al.
  1.28
• The relative importance of being Gaussian (2025)
  F. Alberto Gr\"unbaum and Tondgi Xu
  1.28
• Set Invariance with Probability One for Controlled Diffusion: Score-based Approach (2025)
  Wenqing Wang et al.
  1.28
• Sampling conditioned diffusions via Pathspace Projected Monte Carlo (2025)
  Tobias Grafke
  1.22
• ItDPDM: Information-Theoretic Discrete Poisson Diffusion Model (2025)
  Sagnik Bhattacharya et al.
  1.17
• Reverse-BSDE Monte Carlo (2025)
  Jairon H. N. Batista et al.
  1.17
• Langevin Diffusion Approximation to Same Marginal Schr\"{o}dinger Bridge (2025)
  Medha Agarwal et al.
  1.17
• A Malliavin-Gamma calculus approach to Score Based Diffusion Generative models for random fields (2025)
  Giacomo Greco
  1.17
• On the Relation between Rectified Flows and Optimal Transport (2025)
  Johannes Hertrich et al.
  1.17
• Efficient Diffusion Models for Symmetric Manifolds (2025)
  Oren Mangoubi et al.
  1.17
• Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study (2025)
  Franck Gabriel et al.
  1.17
• Conditioning Diffusions Using Malliavin Calculus (2025)
  Jakiw Pidstrigach et al.
  1.11
• Benchmarking Diffusion Annealing-Based Bayesian Inverse Problem Solvers (2025)
  Evan Scope Crafts and Umberto Villa
  1.06
• Generative Modeling with Diffusion (2024)
  Justin Le
  1.00
• Analysis of Diffusion Models for Manifold Data (2025)
  Anand Jerry George et al.
  1.00
• Iterative Importance Fine-tuning of Diffusion Models (2025)
  Alexander Denker et al.
  1.00
• Advancing Wasserstein Convergence Analysis of Score-Based Models: Insights from Discretization and Second-Order Acceleration (2025)
  Yifeng Yu et al.
  1.00
• Non-asymptotic Analysis of Diffusion Annealed Langevin Monte Carlo for Generative Modelling (2025)
  Paula Cordero-Encinar et al.
  1.00
• HEROS-GAN: Honed-Energy Regularized and Optimal Supervised GAN for Enhancing Accuracy and Range of Low-Cost Accelerometers (2025)
  Yifeng Wang et al.
  1.00
• Flow Matching: Markov Kernels, Stochastic Processes and Transport Plans (2025)
  Christian Wald and Gabriele Steidl
  0.94
• Generative diffusion models from a PDE perspective (2025)
  Fei Cao (1) and Kimball Johnston (2) and Thomas Laurent (3) and Justin Le (2) and S\'ebastien Motsch (2) ((1) University of Massachusetts Amherst et al.
  0.94
• Non-asymptotic bounds for forward processes in denoising diffusions: Ornstein-Uhlenbeck is hard to beat (2024)
  Miha Bre\v{s}ar and Aleksandar Mijatovi\'c
  0.78
• Convergence Analysis for General Probability Flow ODEs of Diffusion Models in Wasserstein Distances (2024)
  Xuefeng Gao et al.
  —
• Stochastic Modified Flows for Riemannian Stochastic Gradient Descent (2024)
  Benjamin Gess et al.
  —
• Zeroth-Order Sampling Methods for Non-Log-Concave Distributions: Alleviating Metastability by Denoising Diffusion (2024)
  Ye He et al.
  —
• Extended Flow Matching: a Method of Conditional Generation with Generalized Continuity Equation (2024)
  Noboru Isobe et al.
  —