3 papers
cs.LG2025
Think While You Generate: Discrete Diffusion with Planned Denoising
Sulin Liu, Juno Nam, Andrew Campbell +4
Discrete diffusion has achieved state-of-the-art performance, outperforming or approaching autoregressive models on standard benchmarks. In this work, we introduce Discrete Diffusi…
q-bio.QM2024
Improved motif-scaffolding with SE(3) flow matching
Jason Yim, Andrew Campbell, Emile Mathieu +9
Protein design often begins with the knowledge of a desired function from a motif which motif-scaffolding aims to construct a functional protein around. Recently, generative models…
stat.ML2024
Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design
Andrew Campbell, Jason Yim, Regina Barzilay +2
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 provid…