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

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

A popular approach to protein design is to combine a generative model with a discriminative
model for conditional sampling. The generative model samples plausible sequences while …

To achieve the highest perceptual quality, state-of-the-art diffusion models are optimized
with objectives that typically look very different from the maximum likelihood and the …

Abstract Despite Flow Matching and diffusion models having emerged as powerful
generative paradigms for continuous variables such as images and videos, their application …

Abstract Neural network-based Combinatorial Optimization (CO) methods have shown
promising results in solving various NP-complete (NPC) problems without relying on hand …

Diffusion models have achieved great success in modeling continuous data modalities such
as images, audio, and video, but have seen limited use in discrete domains such as …

Natural data is redundant yet predominant architectures tile computation uniformly across
their input and output space. We propose the Recurrent Interface Networks (RINs), an …

Protein denoising diffusion probabilistic models are used for the de novo generation of
protein backbones but are limited in their ability to guide generation of proteins with …

Diffusion models have gained significant attention in the realm of image generation due to
their exceptional performance. Their success has been recently expanded to text generation …