Diffusion Processes on Implicit Manifolds
Fuente:
arXiv
Saved in:
| Main Authors: | Kawasaki-Borruat, Victor, Grotehans, Clara, Vandergheynst, Pierre, Gosztolai, Adam |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Geometry-Induced Diffusion on Graphs: A Learnable Weighted Laplacian for Spectral GNNs
by: Zosso, Mia, et al.
Published: (2026)
by: Zosso, Mia, et al.
Published: (2026)
Implicit Gaussian process representation of vector fields over arbitrary latent manifolds
by: Peach, Robert L., et al.
Published: (2023)
by: Peach, Robert L., et al.
Published: (2023)
Interpretable statistical representations of neural population dynamics and geometry
by: Gosztolai, Adam, et al.
Published: (2023)
by: Gosztolai, Adam, et al.
Published: (2023)
Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps
by: Giovanis, Dimitris G, et al.
Published: (2025)
by: Giovanis, Dimitris G, et al.
Published: (2025)
Carré du champ flow matching: better quality-generalisation tradeoff in generative models
by: Bamberger, Jacob, et al.
Published: (2025)
by: Bamberger, Jacob, et al.
Published: (2025)
Neural Diffusion Intensity Models for Point Process Data
by: Du, Xinlong, et al.
Published: (2026)
by: Du, Xinlong, et al.
Published: (2026)
Normalizing Flows on Quotient Manifolds via Boundary Quotients
by: Ghanem, William, et al.
Published: (2025)
by: Ghanem, William, et al.
Published: (2025)
Efficient Diffusion Models for Symmetric Manifolds
by: Mangoubi, Oren, et al.
Published: (2025)
by: Mangoubi, Oren, et al.
Published: (2025)
A Free Probabilistic Framework for Denoising Diffusion Models: Entropy, Transport, and Reverse Processes
by: Das, Swagatam
Published: (2025)
by: Das, Swagatam
Published: (2025)
Stabilizing Temporal Difference Learning via Implicit Stochastic Recursion
by: Kim, Hwanwoo, et al.
Published: (2025)
by: Kim, Hwanwoo, et al.
Published: (2025)
Diffusion Model's Generalization Can Be Characterized by Inductive Biases toward a Data-Dependent Ridge Manifold
by: He, Ye, et al.
Published: (2026)
by: He, Ye, et al.
Published: (2026)
Implicit Compressibility of Overparametrized Neural Networks Trained with Heavy-Tailed SGD
by: Wan, Yijun, et al.
Published: (2023)
by: Wan, Yijun, et al.
Published: (2023)
Sharper Perturbed-Kullback-Leibler Exponential Tail Bounds for Beta and Dirichlet Distributions
by: Perrault, Pierre
Published: (2025)
by: Perrault, Pierre
Published: (2025)
Learning the Infinitesimal Generator of Stochastic Diffusion Processes
by: Kostic, Vladimir R., et al.
Published: (2024)
by: Kostic, Vladimir R., et al.
Published: (2024)
node2vec or triangle-biased random walks: stationarity, regularity & recurrence
by: Avena, Luca, et al.
Published: (2026)
by: Avena, Luca, et al.
Published: (2026)
Obstacle-aware Gaussian Process Regression
by: Shrivastava, Gaurav
Published: (2024)
by: Shrivastava, Gaurav
Published: (2024)
Analysis of Diffusion Models for Manifold Data
by: George, Anand Jerry, et al.
Published: (2025)
by: George, Anand Jerry, et al.
Published: (2025)
On the Limits of Latent Reuse in Diffusion Models
by: Yu, Yifeng, et al.
Published: (2026)
by: Yu, Yifeng, et al.
Published: (2026)
Theoretical guarantees in KL for Diffusion Flow Matching
by: Silveri, Marta Gentiloni, et al.
Published: (2024)
by: Silveri, Marta Gentiloni, et al.
Published: (2024)
Malliavin Calculus for Score-based Diffusion Models
by: Mirafzali, Ehsan, et al.
Published: (2025)
by: Mirafzali, Ehsan, et al.
Published: (2025)
Chernoff Bounds for Tensor Expanders on Riemannian Manifolds Using Graph Laplacian Approximation
by: Chang, Shih-Yu
Published: (2024)
by: Chang, Shih-Yu
Published: (2024)
Comparing statistical and deep learning techniques for parameter estimation of continuous-time stochastic differentiable equations
by: Sankoh, Aroon, et al.
Published: (2025)
by: Sankoh, Aroon, et al.
Published: (2025)
Diffusion annealed Langevin dynamics: a theoretical study
by: Cattiaux, Patrick, et al.
Published: (2025)
by: Cattiaux, Patrick, et al.
Published: (2025)
Permutation-Invariant Spectral Learning via Dyson Diffusion
by: Schwarz, Tassilo, et al.
Published: (2025)
by: Schwarz, Tassilo, et al.
Published: (2025)
Variational Optimality of Föllmer Processes in Generative Diffusions
by: Chen, Yifan, et al.
Published: (2026)
by: Chen, Yifan, et al.
Published: (2026)
ResNets of All Shapes and Sizes: Convergence of Training Dynamics in the Large-scale Limit
by: Chaintron, Louis-Pierre, et al.
Published: (2026)
by: Chaintron, Louis-Pierre, et al.
Published: (2026)
Flow Matching: Markov Kernels, Stochastic Processes and Transport Plans
by: Wald, Christian, et al.
Published: (2025)
by: Wald, Christian, et al.
Published: (2025)
Random ReLU Neural Networks as Non-Gaussian Processes
by: Parhi, Rahul, et al.
Published: (2024)
by: Parhi, Rahul, et al.
Published: (2024)
Sig-DEG for Distillation: Making Diffusion Models Faster and Lighter
by: Jiang, Lei, et al.
Published: (2025)
by: Jiang, Lei, et al.
Published: (2025)
Efficient Sampling on Riemannian Manifolds via Langevin MCMC
by: Cheng, Xiang, et al.
Published: (2024)
by: Cheng, Xiang, et al.
Published: (2024)
A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes
by: Noack, Marcus M., et al.
Published: (2023)
by: Noack, Marcus M., et al.
Published: (2023)
Machine learning-based system reliability analysis with Gaussian Process Regression
by: Zhou, Lisang, et al.
Published: (2024)
by: Zhou, Lisang, et al.
Published: (2024)
Structural and Convergence Analysis of Discrete-Time Denoising Diffusion Probabilistic Models
by: Nakano, Yumiharu
Published: (2024)
by: Nakano, Yumiharu
Published: (2024)
Resolving Node Identifiability in Graph Neural Processes via Laplacian Spectral Encodings
by: Yan, Zimo, et al.
Published: (2025)
by: Yan, Zimo, et al.
Published: (2025)
Long-time asymptotics of noisy SVGD outside the population limit
by: Priser, Victor, et al.
Published: (2024)
by: Priser, Victor, et al.
Published: (2024)
Deep ZakaiJ: Structured Filtering for Jump-Diffusion Time Series Forecasting
by: Leng, Yan, et al.
Published: (2026)
by: Leng, Yan, et al.
Published: (2026)
Convergence Analysis for General Probability Flow ODEs of Diffusion Models in Wasserstein Distances
by: Gao, Xuefeng, et al.
Published: (2024)
by: Gao, Xuefeng, et al.
Published: (2024)
Demystifying MaskGIT Sampler and Beyond: Adaptive Order Selection in Masked Diffusion
by: Hayakawa, Satoshi, et al.
Published: (2025)
by: Hayakawa, Satoshi, et al.
Published: (2025)
Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions
by: Tran, Hoang, et al.
Published: (2025)
by: Tran, Hoang, et al.
Published: (2025)
A Learning-Based Superposition Operator for Non-Renewal Arrival Processes in Queueing Networks
by: Sherzer, Eliran
Published: (2026)
by: Sherzer, Eliran
Published: (2026)
Similar Items
-
Geometry-Induced Diffusion on Graphs: A Learnable Weighted Laplacian for Spectral GNNs
by: Zosso, Mia, et al.
Published: (2026) -
Implicit Gaussian process representation of vector fields over arbitrary latent manifolds
by: Peach, Robert L., et al.
Published: (2023) -
Interpretable statistical representations of neural population dynamics and geometry
by: Gosztolai, Adam, et al.
Published: (2023) -
Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps
by: Giovanis, Dimitris G, et al.
Published: (2025) -
Carré du champ flow matching: better quality-generalisation tradeoff in generative models
by: Bamberger, Jacob, et al.
Published: (2025)