124 citations · 264 across the 8 of their papers we have counts for
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cs.LG2023
Sparse Training of Discrete Diffusion Models for Graph Generation
Yiming Qin, Clement Vignac, Pascal Frossard
Generative graph models struggle to scale due to the need to predict the existence or type of edges between all node pairs. To address the resulting quadratic complexity, existing…
cs.LG2023★ 10 cited
MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation
Clement Vignac, Nagham Osman, Laura Toni +1
This work introduces MiDi, a novel diffusion model for jointly generating molecular graphs and their corresponding 3D arrangement of atoms. Unlike existing methods that rely on pre…