4 papers
Symmetry-Breaking De Novo Crystal Generation via Markovian Jump Diffusion
Van Khoa Nguyen, Alexandros Kalousis
Generating crystals has recently attracted significant interest due to their broad applications in materials science. However, existing generative models struggle to produce comple…
Stein Diffusion Guidance: Training-Free Posterior Correction for Sampling Beyond High-Density Regions
Van Khoa Nguyen, Lionel Blondé, Alexandros Kalousis
Training-free diffusion guidance offers a flexible framework for leveraging off-the-shelf classifiers without additional training. Yet, current approaches hinge on posterior approx…
MING: A Functional Approach to Learning Molecular Generative Models
Van Khoa Nguyen, Maciej Falkiewicz, Giangiacomo Mercatali +1
Traditional molecule generation methods often rely on sequence- or graph-based representations, which can limit their expressive power or require complex permutation-equivariant ar…
GLAD: Improving Latent Graph Generative Modeling with Simple Quantization
Van Khoa Nguyen, Yoann Boget, Frantzeska Lavda +1
Learning graph generative models over latent spaces has received less attention compared to models that operate on the original data space and has so far demonstrated lacklustre pe…