Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …

Deep generative models have unlocked another profound realm of human creativity. By
capturing and generalizing patterns within data, we have entered the epoch of all …

Solving transport problems, ie finding a map transporting one given distribution to another,
has numerous applications in machine learning. Novel mass transport methods motivated …

Denoising diffusion models are a recent class of generative models exhibiting state-of-the-
art performance in image and audio synthesis. Such models approximate the time-reversal …

Diffusion Schrödinger bridges (DSBs) have recently emerged as a powerful framework for
recovering stochastic dynamics via their marginal observations at different time points …

More than twenty years after its introduction, Annealed Importance Sampling (AIS) remains
one of the most effective methods for marginal likelihood estimation. It relies on a sequence …

Diffusion models are powerful generative models that map noise to data using stochastic
processes. However, for many applications such as image editing, the model input comes …

The static optimal transport $(\mathrm {OT}) $ problem between Gaussians seeks to recover
an optimal map, or more generally a coupling, to morph a Gaussian into another. It has been …

Diffusion models have emerged as powerful tools for molecular generation, particularly in
the context of 3D molecular structures. Inspired by nonequilibrium statistical physics, these …

Diffusion models (DMs) represent state-of-the-art generative models for continuous inputs.
DMs work by constructing a Stochastic Differential Equation (SDE) in the input space (ie …