Stochastic interpolants with data-dependent couplings
Fuente:
arXiv
Saved in:
| Main Authors: | Albergo, Michael S., Goldstein, Mark, Boffi, Nicholas M., Ranganath, Rajesh, Vanden-Eijnden, Eric |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
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)
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
by: Chen, Yifan, et al.
Published: (2024)
by: Chen, Yifan, et al.
Published: (2024)
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)
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)
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)
NETS: A Non-Equilibrium Transport Sampler
by: Albergo, Michael S., et al.
Published: (2024)
by: Albergo, Michael S., et al.
Published: (2024)
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)
Multitask Learning with Stochastic Interpolants
by: Negrel, Hugo, et al.
Published: (2025)
by: Negrel, Hugo, et al.
Published: (2025)
Test-time scaling of diffusions with flow maps
by: Sabour, Amirmojtaba, et al.
Published: (2025)
by: Sabour, Amirmojtaba, et al.
Published: (2025)
What's the score? Automated Denoising Score Matching for Nonlinear Diffusions
by: Singhal, Raghav, et al.
Published: (2024)
by: Singhal, Raghav, et al.
Published: (2024)
Discrete Flow Maps
by: Potaptchik, Peter, et al.
Published: (2026)
by: Potaptchik, Peter, et al.
Published: (2026)
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)
Scale-Adaptive Generative Flows for Multiscale Scientific Data
by: Chen, Yifan, et al.
Published: (2025)
by: Chen, Yifan, et al.
Published: (2025)
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)
Variational Optimality of Föllmer Processes in Generative Diffusions
by: Chen, Yifan, et al.
Published: (2026)
by: Chen, Yifan, et al.
Published: (2026)
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)
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)
Lipschitz-Guided Design of Interpolation Schedules in Generative Models
by: Chen, Yifan, et al.
Published: (2025)
by: Chen, Yifan, et al.
Published: (2025)
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)
Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities
by: Saporta, Adriel, et al.
Published: (2024)
by: Saporta, Adriel, et al.
Published: (2024)
Simulation-Free Differential Dynamics through Neural Conservation Laws
by: Hua, Mengjian, et al.
Published: (2025)
by: Hua, Mengjian, et al.
Published: (2025)
Rare Event Analysis via Stochastic Optimal Control
by: Du, Yuanqi, et al.
Published: (2026)
by: Du, Yuanqi, 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)
Probing the Geometry of Diffusion Models with the String Method
by: Moreau, Elio, et al.
Published: (2026)
by: Moreau, Elio, et al.
Published: (2026)
Training-Free Generative Modeling via Kernelized Stochastic Interpolants
by: Coeurdoux, Florentin, et al.
Published: (2026)
by: Coeurdoux, Florentin, et al.
Published: (2026)
To Use or not to Use Muon: How Simplicity Bias in Optimizers Matters
by: Dragutinović, Sara, et al.
Published: (2026)
by: Dragutinović, Sara, et al.
Published: (2026)
Dynamic Test-Time Compute Scaling in Control Policy: Difficulty-Aware Stochastic Interpolant Policy
by: Chun, Inkook, et al.
Published: (2025)
by: Chun, Inkook, et al.
Published: (2025)
Time After Time: Deep-Q Effect Estimation for Interventions on When and What to do
by: Wald, Yoav, et al.
Published: (2025)
by: Wald, Yoav, 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)
Three Forms of Stochastic Injection for Improved Distribution-to-Distribution Generative Modeling
by: Su, Shiye, et al.
Published: (2025)
by: Su, Shiye, et al.
Published: (2025)
Preference learning made easy: Everything should be understood through win rate
by: Zhang, Lily H., et al.
Published: (2025)
by: Zhang, Lily H., et al.
Published: (2025)
Attention and Compression is all you need for Controllably Efficient Language Models
by: Prakash, Jatin, et al.
Published: (2025)
by: Prakash, Jatin, et al.
Published: (2025)
BoltzNCE: Learning Likelihoods for Boltzmann Generation with Stochastic Interpolants and Noise Contrastive Estimation
by: Aggarwal, Rishal, et al.
Published: (2025)
by: Aggarwal, Rishal, et al.
Published: (2025)
Explanations that reveal all through the definition of encoding
by: Puli, Aahlad, et al.
Published: (2024)
by: Puli, Aahlad, et al.
Published: (2024)
Shallow diffusion networks provably learn hidden low-dimensional structure
by: Boffi, Nicholas M., et al.
Published: (2024)
by: Boffi, Nicholas M., et al.
Published: (2024)
A Monte Carlo estimator of flow fields for sampling and noise problems
by: Albergo, Michael S., et al.
Published: (2026)
by: Albergo, Michael S., 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)
Tilt Matching for Scalable Sampling and Fine-Tuning
by: Potaptchik, Peter, et al.
Published: (2025)
by: Potaptchik, Peter, et al.
Published: (2025)
Similar Items
-
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) -
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
by: Chen, Yifan, et al.
Published: (2024) -
How to build a consistency model: Learning flow maps via self-distillation
by: Boffi, Nicholas M., et al.
Published: (2025) -
SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers
by: Ma, Nanye, et al.
Published: (2024)