31 citations · 40 across the 30 of their papers we have counts for
10 papers · 1 filter
Generative Modeling from Black-box Corruptions via Self-Consistent Stochastic Interpolants
Chirag Modi, Jiequn Han, Eric Vanden-Eijnden +1
Transport-based methods have emerged as a leading paradigm for building generative models from large, clean datasets. However, in many scientific and engineering domains, clean dat…
Test-time scaling of diffusions with flow maps
Amirmojtaba Sabour, Michael S. Albergo, Carles Domingo-Enrich +4
A common recipe to improve diffusion models at test-time so that samples score highly against a user-specified reward is to introduce the gradient of the reward into the dynamics o…
DASIP: Dynamic Test-Time Compute Scaling for Robot Control with Stochastic Interpolant Policies
Inkook Chun, Seungjae Lee, Michael S. Albergo +2
Diffusion- and flow-based policies deliver state-of-the-art performance on long-horizon robotic manipulation and imitation learning tasks. However, these controllers employ a fixed…
Scale-Adaptive Generative Flows for Multiscale Scientific Data
Yifan Chen, Eric Vanden-Eijnden
Flow-based generative models can face numerical challenges on scientific data with multiscale Fourier spectra, often producing large errors at fine scales. We approach this problem…
Lipschitz-Guided Design of Interpolation Schedules in Generative Models
Yifan Chen, Eric Vanden-Eijnden, Jiawei Xu
We study the design of interpolation schedules in flow and diffusion-based generative models from both statistical and numerical perspectives. Within the stochastic interpolants fr…
Multitask Learning with Stochastic Interpolants
Hugo Negrel, Florentin Coeurdoux, Michael S. Albergo +1
We propose a framework for learning maps between probability distributions that broadly generalizes the time dynamics of flow and diffusion models. To enable this, we generalize st…
Simulation-Free Differential Dynamics through Neural Conservation Laws
Mengjian Hua, Eric Vanden-Eijnden, Ricky T. Q. Chen
We present a novel simulation-free framework for training continuous-time diffusion processes over very general objective functions. Existing methods typically involve either presc…
How to build a consistency model: Learning flow maps via self-distillation
Nicholas M. Boffi, Michael S. Albergo, Eric Vanden-Eijnden
Flow-based generative models achieve state-of-the-art sample quality, but require the expensive solution of a differential equation at inference time. Flow map models, commonly kno…
FEAT: Free energy Estimators with Adaptive Transport
Jiajun He, Yuanqi Du, Francisco Vargas +4
We present Free energy Estimators with Adaptive Transport (FEAT), a novel framework for free energy estimation -- a critical challenge across scientific domains. FEAT leverages lea…
Optimizing Noise Schedules of Generative Models in High Dimensionss
Santiago Aranguri, Giulio Biroli, Marc Mezard +1
Recent works have shown that diffusion models can undergo phase transitions, the resolution of which is needed for accurately generating samples. This has motivated the use of diff…