6 papers
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…
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…
Model-free learning of probability flows: Elucidating the nonequilibrium dynamics of flocking
Nicholas M. Boffi, Eric Vanden-Eijnden
Active systems comprise a class of nonequilibrium dynamics in which individual components autonomously dissipate energy. Efforts towards understanding the role played by activity h…