22 citations · 52 across the 12 of their papers we have counts for
3 papers · 1 filter
Efficient and Scalable Graph Generation through Iterative Local Expansion
Andreas Bergmeister, Karolis Martinkus, Nathanaël Perraudin +1
In the realm of generative models for graphs, extensive research has been conducted. However, most existing methods struggle with large graphs due to the complexity of representing…
AbDiffuser: Full-Atom Generation of in vitro Functioning Antibodies
Karolis Martinkus, Jan Ludwiczak, Kyunghyun Cho +8
We introduce AbDiffuser, an equivariant and physics-informed diffusion model for the joint generation of antibody 3D structures and sequences. AbDiffuser is built on top of a new r…
Discovering Graph Generation Algorithms
Mihai Babiac, Karolis Martinkus, Roger Wattenhofer
We provide a novel approach to construct generative models for graphs. Instead of using the traditional probabilistic models or deep generative models, we propose to instead find a…