Saved in:
| Main Authors: | Hurault, Samuel, Moreau, Thomas, Peyré, Gabriel |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.08392 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
From Score Matching to Diffusion: A Fine-Grained Error Analysis in the Gaussian Setting
by: Hurault, Samuel, et al.
Published: (2025)
by: Hurault, Samuel, et al.
Published: (2025)
Tessellations of Semi-Discrete Flow Matching
by: Pierret, Emile, et al.
Published: (2026)
by: Pierret, Emile, et al.
Published: (2026)
Optimal and Diffusion Transports in Machine Learning
by: Peyré, Gabriel
Published: (2025)
by: Peyré, Gabriel
Published: (2025)
Robust Sublinear Convergence Rates for Iterative Bregman Projections
by: Peyré, Gabriel
Published: (2026)
by: Peyré, Gabriel
Published: (2026)
Muon Dynamics as a Spectral Wasserstein Flow
by: Peyré, Gabriel
Published: (2026)
by: Peyré, Gabriel
Published: (2026)
Optimal Transport for Machine Learners
by: Peyré, Gabriel
Published: (2025)
by: Peyré, Gabriel
Published: (2025)
Probing the Geometry of Diffusion Models with the String Method
by: Moreau, Elio, et al.
Published: (2026)
by: Moreau, Elio, et al.
Published: (2026)
Towards Understanding the Universality of Transformers for Next-Token Prediction
by: Sander, Michael E., et al.
Published: (2024)
by: Sander, Michael E., et al.
Published: (2024)
How Smooth Is Attention?
by: Castin, Valérie, et al.
Published: (2023)
by: Castin, Valérie, et al.
Published: (2023)
Intrinsic training dynamics of deep neural networks
by: Marcotte, Sibylle, et al.
Published: (2025)
by: Marcotte, Sibylle, et al.
Published: (2025)
Transformative or Conservative? Conservation laws for ResNets and Transformers
by: Marcotte, Sibylle, et al.
Published: (2025)
by: Marcotte, Sibylle, et al.
Published: (2025)
On the global convergence of gradient descent for wide shallow models with bounded nonlinearities
by: Petit, Romain, et al.
Published: (2026)
by: Petit, Romain, et al.
Published: (2026)
Learning from Samples: Inverse Problems over measures via Sharpened Fenchel-Young Losses
by: Andrade, Francisco, et al.
Published: (2025)
by: Andrade, Francisco, et al.
Published: (2025)
Abide by the Law and Follow the Flow: Conservation Laws for Gradient Flows
by: Marcotte, Sibylle, et al.
Published: (2023)
by: Marcotte, Sibylle, et al.
Published: (2023)
Keep the Momentum: Conservation Laws beyond Euclidean Gradient Flows
by: Marcotte, Sibylle, et al.
Published: (2024)
by: Marcotte, Sibylle, et al.
Published: (2024)
Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization
by: Ye, Zhenzhang, et al.
Published: (2024)
by: Ye, Zhenzhang, et al.
Published: (2024)
Understanding the training of infinitely deep and wide ResNets with Conditional Optimal Transport
by: Barboni, Raphaël, et al.
Published: (2024)
by: Barboni, Raphaël, et al.
Published: (2024)
Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regime
by: Barboni, Raphaël, et al.
Published: (2025)
by: Barboni, Raphaël, et al.
Published: (2025)
Token Sample Complexity of Attention
by: Bohbot, Léa, et al.
Published: (2025)
by: Bohbot, Léa, et al.
Published: (2025)
Multi-scale Autoregressive Models are Laplacian, Discrete, and Latent Diffusion Models in Disguise
by: Hong, Steve, et al.
Published: (2025)
by: Hong, Steve, et al.
Published: (2025)
Balanced LoRA: Removing Parameter Invariance to Accelerate Convergence
by: Castin, Valérie, et al.
Published: (2026)
by: Castin, Valérie, et al.
Published: (2026)
Transformers are Universal In-context Learners
by: Furuya, Takashi, et al.
Published: (2024)
by: Furuya, Takashi, et al.
Published: (2024)
On the Error-Correcting Effects of Stochasticity in Discrete Diffusion
by: Yuan, William, et al.
Published: (2026)
by: Yuan, William, et al.
Published: (2026)
How do Transformers perform In-Context Autoregressive Learning?
by: Sander, Michael E., et al.
Published: (2024)
by: Sander, Michael E., et al.
Published: (2024)
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms
by: Dagréou, Mathieu, et al.
Published: (2022)
by: Dagréou, Mathieu, et al.
Published: (2022)
A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization
by: Dagréou, Mathieu, et al.
Published: (2023)
by: Dagréou, Mathieu, et al.
Published: (2023)
Scaling Behavior of Discrete Diffusion Language Models
by: von Rütte, Dimitri, et al.
Published: (2025)
by: von Rütte, Dimitri, et al.
Published: (2025)
Simple Guidance Mechanisms for Discrete Diffusion Models
by: Schiff, Yair, et al.
Published: (2024)
by: Schiff, Yair, et al.
Published: (2024)
Unmixing Noise from Hawkes Process to Model Learned Physiological Events
by: Staerman, Guillaume, et al.
Published: (2024)
by: Staerman, Guillaume, et al.
Published: (2024)
Training Infinitely Deep and Wide Transformers
by: Barboni, Raphaël, et al.
Published: (2026)
by: Barboni, Raphaël, et al.
Published: (2026)
Discretization Error of Fourier Neural Operators
by: Lanthaler, Samuel, et al.
Published: (2024)
by: Lanthaler, Samuel, et al.
Published: (2024)
Constrained Discrete Diffusion
by: Cardei, Michael, et al.
Published: (2025)
by: Cardei, Michael, et al.
Published: (2025)
Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo
by: Lee, Cheuk Kit, et al.
Published: (2025)
by: Lee, Cheuk Kit, et al.
Published: (2025)
Geometry- and Relation-Aware Diffusion for EEG Super-Resolution
by: Yao, Laura, et al.
Published: (2026)
by: Yao, Laura, et al.
Published: (2026)
Geometry-Aware Attention Guidance for Diffusion Models via Modern Hopfield Dynamics
by: Kim, Kwanyoung
Published: (2026)
by: Kim, Kwanyoung
Published: (2026)
Continuous Geometry-Aware Graph Diffusion via Hyperbolic Neural PDE
by: Liu, Jiaxu, et al.
Published: (2024)
by: Liu, Jiaxu, et al.
Published: (2024)
Compositional Discrete Latent Code for High Fidelity, Productive Diffusion Models
by: Lavoie, Samuel, et al.
Published: (2025)
by: Lavoie, Samuel, et al.
Published: (2025)
Beyond Single Tokens: Distilling Discrete Diffusion Models via Discrete MMD
by: Hoogeboom, Emiel, et al.
Published: (2026)
by: Hoogeboom, Emiel, et al.
Published: (2026)
Self-Aware Markov Models for Discrete Reasoning
by: Kornhardt, Gregor, et al.
Published: (2026)
by: Kornhardt, Gregor, et al.
Published: (2026)
Dependency-Aware Discrete Diffusion for Scene Graph Generation
by: Rajagopalan, Rajalaxmi, et al.
Published: (2026)
by: Rajagopalan, Rajalaxmi, et al.
Published: (2026)
Similar Items
-
From Score Matching to Diffusion: A Fine-Grained Error Analysis in the Gaussian Setting
by: Hurault, Samuel, et al.
Published: (2025) -
Tessellations of Semi-Discrete Flow Matching
by: Pierret, Emile, et al.
Published: (2026) -
Optimal and Diffusion Transports in Machine Learning
by: Peyré, Gabriel
Published: (2025) -
Robust Sublinear Convergence Rates for Iterative Bregman Projections
by: Peyré, Gabriel
Published: (2026) -
Muon Dynamics as a Spectral Wasserstein Flow
by: Peyré, Gabriel
Published: (2026)