Imagen de Portada

Support Before Frequency in Discrete Diffusion

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Müller, Adrian, Gonon, Antoine, Shen, Zebang, Hsieh, Ya-Ping, He, Niao
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Manifold Generalization Provably Proceeds Memorization in Diffusion Models
por: Shen, Zebang, et al.
Publicado: (2026)

When Scores Learn Geometry: Rate Separations under the Manifold Hypothesis
por: Li, Xiang, et al.
Publicado: (2025)

Provable Maximum Entropy Manifold Exploration via Diffusion Models
por: De Santi, Riccardo, et al.
Publicado: (2025)

Flow Density Control: Generative Optimization Beyond Entropy-Regularized Fine-Tuning
por: De Santi, Riccardo, et al.
Publicado: (2025)

Poincaré Inequality for Local Log-Polyak-Lojasiewicz Measures : Non-asymptotic Analysis in Low-temperature Regime
por: Gong, Yun, et al.
Publicado: (2025)

A Schrödinger Eigenfunction Method for Long-Horizon Stochastic Optimal Control
por: Claeys, Louis, et al.
Publicado: (2026)

A Hessian-Aware Stochastic Differential Equation for Modelling SGD
por: Li, Xiang, et al.
Publicado: (2024)

Landing with the Score: Riemannian Optimization through Denoising
por: Kharitenko, Andrey, et al.
Publicado: (2025)

Scalable Neural Incentive Design with Parameterized Mean-Field Approximation
por: Corecco, Nathan, et al.
Publicado: (2025)

Learning to Steer Markovian Agents under Model Uncertainty
por: Huang, Jiawei, et al.
Publicado: (2024)

Truly No-Regret Learning in Constrained MDPs
por: Müller, Adrian, et al.
Publicado: (2024)

Gaussian Match-and-Copy: A Minimalist Benchmark for Studying Transformer Induction
por: Gonon, Antoine, et al.
Publicado: (2026)

Symmetry-Aware Bayesian Optimization via Max Kernels
por: Bardou, Anthony, et al.
Publicado: (2025)

A Rescaling-Invariant Lipschitz Bound Based on Path-Metrics for Modern ReLU Network Parameterizations
por: Gonon, Antoine, et al.
Publicado: (2024)

Non-Vacuous Generalization Bounds: Can Rescaling Invariances Help?
por: Rouchouse, Damien, et al.
Publicado: (2025)

Universal Approximation Theorem and error bounds for quantum neural networks and quantum reservoirs
por: Gonon, Lukas, et al.
Publicado: (2023)

Insights on Muon from Simple Quadratics
por: Gonon, Antoine, et al.
Publicado: (2026)

Fast Inference with Kronecker-Sparse Matrices
por: Gonon, Antoine, et al.
Publicado: (2024)

A path-norm toolkit for modern networks: consequences, promises and challenges
por: Gonon, Antoine, et al.
Publicado: (2023)

Robust Knowledge Transfer in Tiered Reinforcement Learning
por: Huang, Jiawei, et al.
Publicado: (2023)

On the Connectedness of Sublevel Sets in Invex Optimization
por: Thoma, Vinzenz, et al.
Publicado: (2026)

Poincare Inequality for Local Log-Polyak-Łojasiewicz Measures: Non-asymptotic Analysis in Low-temperature Regime
por: Gong, Yun, et al.
Publicado: (2024)

Distributional Adversarial Attacks and Training in Deep Hedging
por: He, Guangyi, et al.
Publicado: (2025)

A Convex Framework for Confounding Robust Inference
por: Ishikawa, Kei, et al.
Publicado: (2023)

Implicit Regularization of Sharpness-Aware Minimization for Scale-Invariant Problems
por: Li, Bingcong, et al.
Publicado: (2024)

Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement Learning
por: Yardim, Batuhan, et al.
Publicado: (2024)

Primal Methods for Variational Inequality Problems with Functional Constraints
por: Zhang, Liang, et al.
Publicado: (2024)

From Gradient Clipping to Normalization for Heavy Tailed SGD
por: Hübler, Florian, et al.
Publicado: (2024)

TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization
por: Li, Xiang, et al.
Publicado: (2022)

Linear Convergence of Entropy-Regularized Natural Policy Gradient with Linear Function Approximation
por: Cayci, Semih, et al.
Publicado: (2021)

On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation
por: Huang, Jiawei, et al.
Publicado: (2023)

Random Feature Representation Boosting
por: Zozoulenko, Nikita, et al.
Publicado: (2025)

Random Neural Network Expressivity for Non-Linear Partial Differential Equations
por: Mehmood, Muhammed Ali, et al.
Publicado: (2026)

Aligned Diffusion Schrödinger Bridges
por: Somnath, Vignesh Ram, et al.
Publicado: (2023)

Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detection
por: Zozoulenko, Nikita, et al.
Publicado: (2024)

Reservoir kernels and Volterra series
por: Gonon, Lukas, et al.
Publicado: (2022)

Steering No-Regret Agents in MFGs under Model Uncertainty
por: Widmer, Leo, et al.
Publicado: (2025)

Verifier-Constrained Flow Expansion for Discovery Beyond the Data
por: De Santi, Riccardo, et al.
Publicado: (2026)

A Unified Density Operator View of Flow Control and Merging
por: De Santi, Riccardo, et al.
Publicado: (2026)

Universal randomised signatures for generative time series modelling
por: Biagini, Francesca, et al.
Publicado: (2024)