222 citations · 961 across the 42 of their papers we have counts for
9 papers · 1 filter
Tilt Matching for Scalable Sampling and Fine-Tuning
Peter Potaptchik, Cheuk-Kit Lee, Michael S. Albergo
We propose a simple, scalable algorithm for using stochastic interpolants to sample from unnormalized densities and for fine-tuning generative models. The approach, Tilt Matching,…
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…
Generalised Flow Maps for Few-Step Generative Modelling on Riemannian Manifolds
Oscar Davis, Michael S. Albergo, Nicholas M. Boffi +2
Geometric data and purpose-built generative models on them have become ubiquitous in high-impact deep learning application domains, ranging from protein backbone generation and com…
Any-Order Flexible Length Masked Diffusion
Jaeyeon Kim, Lee Cheuk-Kit, Carles Domingo-Enrich +5
Masked diffusion models (MDMs) have recently emerged as a promising alternative to autoregressive models over discrete domains. MDMs generate sequences in an any-order, parallel fa…
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…
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…
LEAPS: A discrete neural sampler via locally equivariant networks
Peter Holderrieth, Michael S. Albergo, Tommi Jaakkola
We propose "LEAPS", an algorithm to sample from discrete distributions known up to normalization by learning a rate matrix of a continuous-time Markov chain (CTMC). LEAPS can be se…
Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo
Cheuk Kit Lee, Paul Jeha, Jes Frellsen +3
Discrete diffusion models are a class of generative models that produce samples from an approximated data distribution within a discrete state space. Often, there is a need to targ…