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 …

While diffusion models excel at generating high-quality images, prior work reports a
significant performance gap between diffusion and autoregressive (AR) methods in …

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

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

Combining discrete and continuous data is an important capability for generative models.
We present Discrete Flow Models (DFMs), a new flow-based model of discrete data that …

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

Mainstream solutions to sequential recommendation represent items with fixed vectors.
These vectors have limited capability in capturing items' latent aspects and users' diverse …