Imagen de Portada

Improved Sampling Schedules for Discrete Diffusion Models

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Foresti, Alberto, Bounoua, Mustapha, Franzese, Giulio, Ambrogioni, Luca, Michiardi, Pietro
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

MINDE: Mutual Information Neural Diffusion Estimation
por: Franzese, Giulio, et al.
Publicado: (2023)

Learning to Match Unpaired Data with Minimum Entropy Coupling
por: Bounoua, Mustapha, et al.
Publicado: (2025)

S$Ω$I: Score-based O-INFORMATION Estimation
por: Bounoua, Mustapha, et al.
Publicado: (2024)

INFO-SEDD: Continuous Time Markov Chains as Scalable Information Metrics Estimators
por: Foresti, Alberto, et al.
Publicado: (2025)

TENDE: Transfer Entropy Neural Diffusion Estimation
por: Munoz, Simon Pedro Galeano, et al.
Publicado: (2025)

Latent Abstractions in Generative Diffusion Models
por: Franzese, Giulio, et al.
Publicado: (2024)

Relative Entropy Estimation in Function Space: Theory and Applications to Trajectory Inference
por: Wang, Chao, et al.
Publicado: (2026)

RFMI: Estimating Mutual Information on Rectified Flow for Text-to-Image Alignment
por: Wang, Chao, et al.
Publicado: (2025)

Entropic Time Schedulers for Generative Diffusion Models
por: Stancevic, Dejan, et al.
Publicado: (2025)

How Out-of-Equilibrium Phase Transitions can Seed Pattern Formation in Trained Diffusion Models
por: Ambrogioni, Luca
Publicado: (2026)

Information Theoretic Text-to-Image Alignment
por: Wang, Chao, et al.
Publicado: (2024)

The statistical thermodynamics of generative diffusion models: Phase transitions, symmetry breaking and critical instability
por: Ambrogioni, Luca
Publicado: (2023)

Stationarity without mean reversion in improper Gaussian processes
por: Ambrogioni, Luca
Publicado: (2023)

The Information Dynamics of Generative Diffusion
por: Stancevic, Dejan, et al.
Publicado: (2025)

In Praise of Stubbornness: An Empirical Case for Cognitive-Dissonance Aware Continual Update of Knowledge in LLMs
por: Clemente, Simone, et al.
Publicado: (2025)

Measuring Semantic Information Production in Generative Diffusion Models
por: Handke, Florian, et al.
Publicado: (2025)

CoVAE: Consistency Training of Variational Autoencoders
por: Silvestri, Gianluigi, et al.
Publicado: (2025)

The Entropic Signature of Class Speciation in Diffusion Models
por: Handke, Florian, et al.
Publicado: (2026)

Emergence of Distortions in High-Dimensional Guided Diffusion Models
por: Ventura, Enrico, et al.
Publicado: (2026)

Why Masking Diffusion Works: Condition on the Jump Schedule for Improved Discrete Diffusion
por: Amin, Alan N., et al.
Publicado: (2025)

Robust and highly scalable estimation of directional couplings from time-shifted signals
por: Rouillard, Louis, et al.
Publicado: (2024)

Language Diffusion Models are Associative Memories Capable of Retrieving Unseen Data
por: Pham, Bao, et al.
Publicado: (2026)

Noise Scheduling as Information-Guided Allocation in Diffusion Training
por: Raya, Gabriel, et al.
Publicado: (2026)

Gradient-based Discrete Sampling with Automatic Cyclical Scheduling
por: Pynadath, Patrick, et al.
Publicado: (2024)

The Cosine Schedule is Fisher-Rao-Optimal for Masked Discrete Diffusion Models
por: Zhang, Leo, et al.
Publicado: (2025)

$\textit{Jump Your Steps}$: Optimizing Sampling Schedule of Discrete Diffusion Models
por: Park, Yong-Hyun, et al.
Publicado: (2024)

Improving Antibody Design with Force-Guided Sampling in Diffusion Models
por: Kulytė, Paulina, et al.
Publicado: (2024)

Manifolds, Random Matrices and Spectral Gaps: The geometric phases of generative diffusion
por: Ventura, Enrico, et al.
Publicado: (2024)

Discrete Diffusion with Sample-Efficient Estimators for Conditionals
por: Elamvazhuthi, Karthik, et al.
Publicado: (2026)

Improving Discrete Diffusion Models via Structured Preferential Generation
por: Rissanen, Severi, et al.
Publicado: (2024)

Scaling Laws for Uncertainty in Deep Learning
por: Rosso, Mattia, et al.
Publicado: (2025)

Data Augmentation for Traffic Classification
por: Wang, Chao, et al.
Publicado: (2024)

NI Sampling: Accelerating Discrete Diffusion Sampling by Token Order Optimization
por: Liu, Enshu, et al.
Publicado: (2026)

Digital Shielding for Cross-Domain Wi-Fi Signal Adaptation using Relativistic Average Generative Adversarial Network
por: Avola, Danilo, et al.
Publicado: (2025)

Discrete State Diffusion Models: A Sample Complexity Perspective
por: Srikanth, Aadithya, et al.
Publicado: (2025)

Loopholing Discrete Diffusion: Deterministic Bypass of the Sampling Wall
por: Jo, Mingyu, et al.
Publicado: (2025)

Memorization to Generalization: Emergence of Diffusion Models from Associative Memory
por: Pham, Bao, et al.
Publicado: (2025)

Stratified Hazard Sampling: Minimal-Variance Event Scheduling for CTMC/DTMC Discrete Diffusion and Flow Models
por: Jang, Seunghwan, et al.
Publicado: (2026)

VCT: Training Consistency Models with Variational Noise Coupling
por: Silvestri, Gianluigi, et al.
Publicado: (2025)

Optimizing Noise Schedules of Generative Models in High Dimensionss
por: Aranguri, Santiago, et al.
Publicado: (2025)