Imagen de Portada

Foundations of Diffusion Models in General State Spaces: A Self-Contained Introduction

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Pauline, Vincent, Höppe, Tobias, Neklyudov, Kirill, Tong, Alexander, Bauer, Stefan, Dittadi, Andrea
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

The Superposition of Diffusion Models Using the Itô Density Estimator
por: Skreta, Marta, et al.
Publicado: (2024)

Minimum-Excess-Work Guidance
por: Kolloff, Christopher, et al.
Publicado: (2025)

Breaking the Likelihood-Quality Trade-off in Diffusion Models by Merging Pretrained Experts
por: Esfandiari, Yasin, et al.
Publicado: (2025)

Exploring the Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models
por: Mamaghan, Amir Mohammad Karimi, et al.
Publicado: (2024)

A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots
por: Guan, Vincent, et al.
Publicado: (2026)

Self-Refining Training for Amortized Density Functional Theory
por: Hassan, Majdi, et al.
Publicado: (2025)

A Computational Framework for Solving Wasserstein Lagrangian Flows
por: Neklyudov, Kirill, et al.
Publicado: (2023)

Does Data Scaling Lead to Visual Compositional Generalization?
por: Uselis, Arnas, et al.
Publicado: (2025)

Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts
por: Skreta, Marta, et al.
Publicado: (2025)

Compositional Generalization Requires Linear, Orthogonal Representations in Vision Embedding Models
por: Uselis, Arnas, et al.
Publicado: (2026)

Wavefunction Flows: Efficient Quantum Simulation of Continuous Flow Models
por: Layden, David, et al.
Publicado: (2025)

From Words to Amino Acids: Does the Curse of Depth Persist?
por: Siji, Aleena, et al.
Publicado: (2026)

Riemannian MeanFlow
por: Woo, Dongyeop, et al.
Publicado: (2026)

Multi-Modal and Multi-Attribute Generation of Single Cells with CFGen
por: Palma, Alessandro, et al.
Publicado: (2024)

Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities
por: Akhound-Sadegh, Tara, et al.
Publicado: (2025)

Are Object-Centric Representations Better At Compositional Generalization?
por: Kapl, Ferdinand, et al.
Publicado: (2026)

Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold
por: Atanackovic, Lazar, et al.
Publicado: (2024)

DiffEnc: Variational Diffusion with a Learned Encoder
por: Nielsen, Beatrix M. G., et al.
Publicado: (2023)

Sparse Shift Autoencoders for Identifying Concepts from Large Language Model Activations
por: Joshi, Shruti, et al.
Publicado: (2025)

Amortized Sampling with Transferable Normalizing Flows
por: Tan, Charlie B., et al.
Publicado: (2025)

Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints
por: Kong, Lingkai, et al.
Publicado: (2024)

Do Depth-Grown Models Overcome the Curse of Depth? An In-Depth Analysis
por: Kapl, Ferdinand, et al.
Publicado: (2025)

Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC
por: Lin, Wu, et al.
Publicado: (2023)

When Does Closeness in Distribution Imply Representational Similarity? An Identifiability Perspective
por: Nielsen, Beatrix M. G., et al.
Publicado: (2025)

Discrete Feynman-Kac Correctors
por: Hasan, Mohsin, et al.
Publicado: (2026)

Theoretical Foundations of Deep Selective State-Space Models
por: Cirone, Nicola Muca, et al.
Publicado: (2024)

An Introduction to Flow Matching and Diffusion Models
por: Holderrieth, Peter, et al.
Publicado: (2025)

FunDiff: Diffusion Models over Function Spaces for Physics-Informed Generative Modeling
por: Wang, Sifan, et al.
Publicado: (2025)

Flow-Based Density Ratio Estimation for Intractable Distributions with Applications in Genomics
por: Antipov, Egor, et al.
Publicado: (2026)

Diffusion Models: A Mathematical Introduction
por: Maleki, Sepehr, et al.
Publicado: (2025)

Stabilizing Self-Consuming Diffusion Models with Latent Space Filtering
por: Cai, Zhongteng, et al.
Publicado: (2025)

Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition
por: Carrasquilla, Juan, et al.
Publicado: (2023)

Unlocking Guidance for Discrete State-Space Diffusion and Flow Models
por: Nisonoff, Hunter, et al.
Publicado: (2024)

State Space Models as Foundation Models: A Control Theoretic Overview
por: Alonso, Carmen Amo, et al.
Publicado: (2024)

Logit Distance Bounds Representational Similarity
por: Nielsen, Beatrix M. G., et al.
Publicado: (2026)

Adversarial Water-Filling: Theory, Algorithms and Foundation Model
por: Tong, Xindi, et al.
Publicado: (2026)

When to Commit? Towards Variable-Size Self-Contained Blocks for Discrete Diffusion Language Models
por: Wang, Danny, et al.
Publicado: (2026)

Graph State-Space Models and Latent Relational Inference
por: Zambon, Daniele, et al.
Publicado: (2023)

Visual Disentangled Diffusion Autoencoders: Scalable Counterfactual Generation for Foundation Models
por: Bender, Sidney, et al.
Publicado: (2026)

Demystifying the Token Dynamics of Deep Selective State Space Models
por: Vo, Thieu N, et al.
Publicado: (2024)