4 citations · 7 across the 5 of their papers we have counts for
4 papers · 1 filter
SymADiT: Symmetry-Aware Crystal Generation with Latent Flow Matching
Anmar Karmush, Cedric Mathieu Brandenburg, Soheil Ershadrad +4
Tackling the task of crystal generation, we aim to enhance the previously proposed, symmetry-agnostic, All-atom Diffusion Transformer (ADiT) by introducing SymADiT, a symmetry-awar…
Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo
Filip Ekström Kelvinius, Zheng Zhao, Fredrik Lindsten
A recent line of research has exploited pre-trained generative diffusion models as priors for solving Bayesian inverse problems. We contribute to this research direction by designi…
Discriminator Guidance for Autoregressive Diffusion Models
Filip Ekström Kelvinius, Fredrik Lindsten
We introduce discriminator guidance in the setting of Autoregressive Diffusion Models. The use of a discriminator to guide a diffusion process has previously been used for continuo…
Accelerating Molecular Graph Neural Networks via Knowledge Distillation
Filip Ekström Kelvinius, Dimitar Georgiev, Artur Petrov Toshev +1
Recent advances in graph neural networks (GNNs) have enabled more comprehensive modeling of molecules and molecular systems, thereby enhancing the precision of molecular property p…