Discrete Flow Maps
Fuente:
arXiv
Saved in:
| Main Authors: | Potaptchik, Peter, Yim, Jason, Saravanan, Adhi, Holderrieth, Peter, Vanden-Eijnden, Eric, Albergo, Michael S. |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Meta Flow Maps enable scalable reward alignment
by: Potaptchik, Peter, et al.
Published: (2026)
by: Potaptchik, Peter, et al.
Published: (2026)
NETS: A Non-Equilibrium Transport Sampler
by: Albergo, Michael S., et al.
Published: (2024)
by: Albergo, Michael S., et al.
Published: (2024)
Flow map matching with stochastic interpolants: A mathematical framework for consistency models
by: Boffi, Nicholas M., et al.
Published: (2024)
by: Boffi, Nicholas M., et al.
Published: (2024)
Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
by: Albergo, Michael S., et al.
Published: (2023)
by: Albergo, Michael S., et al.
Published: (2023)
Discrete Tilt Matching
by: Chen, Yuyuan, et al.
Published: (2026)
by: Chen, Yuyuan, et al.
Published: (2026)
LEAPS: A discrete neural sampler via locally equivariant networks
by: Holderrieth, Peter, et al.
Published: (2025)
by: Holderrieth, Peter, et al.
Published: (2025)
How to build a consistency model: Learning flow maps via self-distillation
by: Boffi, Nicholas M., et al.
Published: (2025)
by: Boffi, Nicholas M., et al.
Published: (2025)
Tilt Matching for Scalable Sampling and Fine-Tuning
by: Potaptchik, Peter, et al.
Published: (2025)
by: Potaptchik, Peter, et al.
Published: (2025)
Multitask Learning with Stochastic Interpolants
by: Negrel, Hugo, et al.
Published: (2025)
by: Negrel, Hugo, et al.
Published: (2025)
Scale-Adaptive Generative Flows for Multiscale Scientific Data
by: Chen, Yifan, et al.
Published: (2025)
by: Chen, Yifan, et al.
Published: (2025)
Stochastic interpolants with data-dependent couplings
by: Albergo, Michael S., et al.
Published: (2023)
by: Albergo, Michael S., et al.
Published: (2023)
SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers
by: Ma, Nanye, et al.
Published: (2024)
by: Ma, Nanye, et al.
Published: (2024)
An Introduction to Flow Matching and Diffusion Models
by: Holderrieth, Peter, et al.
Published: (2025)
by: Holderrieth, Peter, et al.
Published: (2025)
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
by: Chen, Yifan, et al.
Published: (2024)
by: Chen, Yifan, et al.
Published: (2024)
Hamiltonian Score Matching and Generative Flows
by: Holderrieth, Peter, et al.
Published: (2024)
by: Holderrieth, Peter, et al.
Published: (2024)
Variational Optimality of Föllmer Processes in Generative Diffusions
by: Chen, Yifan, et al.
Published: (2026)
by: Chen, Yifan, et al.
Published: (2026)
Model-free learning of probability flows: Elucidating the nonequilibrium dynamics of flocking
by: Boffi, Nicholas M., et al.
Published: (2024)
by: Boffi, Nicholas M., et al.
Published: (2024)
Test-time scaling of diffusions with flow maps
by: Sabour, Amirmojtaba, et al.
Published: (2025)
by: Sabour, Amirmojtaba, et al.
Published: (2025)
Lipschitz-Guided Design of Interpolation Schedules in Generative Models
by: Chen, Yifan, et al.
Published: (2025)
by: Chen, Yifan, et al.
Published: (2025)
An Efficient On-Policy Deep Learning Framework for Stochastic Optimal Control
by: Hua, Mengjian, et al.
Published: (2024)
by: Hua, Mengjian, et al.
Published: (2024)
Neural Galerkin Schemes with Active Learning for High-Dimensional Evolution Equations
by: Bruna, Joan, et al.
Published: (2022)
by: Bruna, Joan, et al.
Published: (2022)
Deep learning probability flows and entropy production rates in active matter
by: Boffi, Nicholas M., et al.
Published: (2023)
by: Boffi, Nicholas M., et al.
Published: (2023)
Simulation-Free Differential Dynamics through Neural Conservation Laws
by: Hua, Mengjian, et al.
Published: (2025)
by: Hua, Mengjian, et al.
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)
Analysis of learning a flow-based generative model from limited sample complexity
by: Cui, Hugo, et al.
Published: (2023)
by: Cui, Hugo, et al.
Published: (2023)
Optimizing Noise Schedules of Generative Models in High Dimensionss
by: Aranguri, Santiago, et al.
Published: (2025)
by: Aranguri, Santiago, et al.
Published: (2025)
Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations
by: Zhang, Huan, et al.
Published: (2024)
by: Zhang, Huan, et al.
Published: (2024)
Generative Modeling from Black-box Corruptions via Self-Consistent Stochastic Interpolants
by: Modi, Chirag, et al.
Published: (2025)
by: Modi, Chirag, et al.
Published: (2025)
Schrödinger Bridge Matching for Tree-Structured Costs and Entropic Wasserstein Barycentres
by: Howard, Samuel, et al.
Published: (2025)
by: Howard, Samuel, et al.
Published: (2025)
Linear Convergence of Diffusion Models Under the Manifold Hypothesis
by: Potaptchik, Peter, et al.
Published: (2024)
by: Potaptchik, Peter, et al.
Published: (2024)
Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective
by: Shaul, Neta, et al.
Published: (2024)
by: Shaul, Neta, et al.
Published: (2024)
Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design
by: Campbell, Andrew, et al.
Published: (2024)
by: Campbell, Andrew, et al.
Published: (2024)
Diamond Maps: Efficient Reward Alignment via Stochastic Flow Maps
by: Holderrieth, Peter, et al.
Published: (2026)
by: Holderrieth, Peter, et al.
Published: (2026)
Generalised Flow Maps for Few-Step Generative Modelling on Riemannian Manifolds
by: Davis, Oscar, et al.
Published: (2025)
by: Davis, Oscar, et al.
Published: (2025)
MGD: Moment Guided Diffusion for Maximum Entropy Generation
by: Lempereur, Etienne, et al.
Published: (2026)
by: Lempereur, Etienne, et al.
Published: (2026)
GLASS Flows: Transition Sampling for Alignment of Flow and Diffusion Models
by: Holderrieth, Peter, et al.
Published: (2025)
by: Holderrieth, Peter, et al.
Published: (2025)
Generator Matching: Generative modeling with arbitrary Markov processes
by: Holderrieth, Peter, et al.
Published: (2024)
by: Holderrieth, Peter, et al.
Published: (2024)
Continuously Tempered Diffusion Samplers
by: Erives, Ezra, et al.
Published: (2025)
by: Erives, Ezra, et al.
Published: (2025)
Metric Flow Matching for Smooth Interpolations on the Data Manifold
by: Kapuśniak, Kacper, et al.
Published: (2024)
by: Kapuśniak, Kacper, et al.
Published: (2024)
Rare Event Analysis via Stochastic Optimal Control
by: Du, Yuanqi, et al.
Published: (2026)
by: Du, Yuanqi, et al.
Published: (2026)
Similar Items
-
Meta Flow Maps enable scalable reward alignment
by: Potaptchik, Peter, et al.
Published: (2026) -
NETS: A Non-Equilibrium Transport Sampler
by: Albergo, Michael S., et al.
Published: (2024) -
Flow map matching with stochastic interpolants: A mathematical framework for consistency models
by: Boffi, Nicholas M., et al.
Published: (2024) -
Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
by: Albergo, Michael S., et al.
Published: (2023) -
Discrete Tilt Matching
by: Chen, Yuyuan, et al.
Published: (2026)