3 papers
cs.LG2025
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…
q-bio.QM2024
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…
cs.LG2024
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…