3 citations · 6 across the 4 of their papers we have counts for
4 papers
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…
Improving Antibody Design with Force-Guided Sampling in Diffusion Models
Paulina Kulytė, Francisco Vargas, Simon Valentin Mathis +3
Antibodies, crucial for immune defense, primarily rely on complementarity-determining regions (CDRs) to bind and neutralize antigens, such as viruses. The design of these CDRs dete…
DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised -transform
Alexander Denker, Francisco Vargas, Shreyas Padhy +7
Generative modelling paradigms based on denoising diffusion processes have emerged as a leading candidate for conditional sampling in inverse problems. In many real-world applicati…
A framework for conditional diffusion modelling with applications in motif scaffolding for protein design
Kieran Didi, Francisco Vargas, Simon V Mathis +4
Many protein design applications, such as binder or enzyme design, require scaffolding a structural motif with high precision. Generative modelling paradigms based on denoising dif…