1 code implementation • 14 Oct 2020 • Karolis Martinkus, Aurelien Lucchi, Nathanaël Perraudin
However, the dynamics of many real-world systems are challenging to learn due to the presence of nonlinear potentials and a number of interactions that scales quadratically with the number of particles $N$, as in the case of the N-body problem.
no code implementations • 27 Sep 2021 • Yue Ding, Karolis Martinkus, Damian Pascual, Simon Clematide, Roger Wattenhofer
Different studies of the embedding space of transformer models suggest that the distribution of contextual representations is highly anisotropic - the embeddings are distributed in a narrow cone.
1 code implementation • NeurIPS 2021 • Pál András Papp, Karolis Martinkus, Lukas Faber, Roger Wattenhofer
In DropGNNs, we execute multiple runs of a GNN on the input graph, with some of the nodes randomly and independently dropped in each of these runs.
Ranked #11 on Graph Classification on IMDb-B
1 code implementation • 4 Apr 2022 • Karolis Martinkus, Andreas Loukas, Nathanaël Perraudin, Roger Wattenhofer
We approach the graph generation problem from a spectral perspective by first generating the dominant parts of the graph Laplacian spectrum and then building a graph matching these eigenvalues and eigenvectors.
no code implementations • 26 May 2022 • Peter Müller, Lukas Faber, Karolis Martinkus, Roger Wattenhofer
We propose the fully explainable Decision Tree Graph Neural Network (DT+GNN) architecture.
1 code implementation • 22 Jun 2022 • Karolis Martinkus, Pál András Papp, Benedikt Schesch, Roger Wattenhofer
AgentNet is inspired by sublinear algorithms, featuring a computational complexity that is independent of the graph size.
1 code implementation • 4 Oct 2022 • Kilian Konstantin Haefeli, Karolis Martinkus, Nathanaël Perraudin, Roger Wattenhofer
Denoising diffusion probabilistic models and score-matching models have proven to be very powerful for generative tasks.
1 code implementation • 20 Nov 2022 • Jeremia Geiger, Karolis Martinkus, Oliver Richter, Roger Wattenhofer
Rigid origami has shown potential in large diversity of practical applications.
no code implementations • 25 Apr 2023 • Mihai Babiac, Karolis Martinkus, Roger Wattenhofer
We provide a novel approach to construct generative models for graphs.
no code implementations • NeurIPS 2023 • Karolis Martinkus, Jan Ludwiczak, Kyunghyun Cho, Wei-Ching Liang, Julien Lafrance-Vanasse, Isidro Hotzel, Arvind Rajpal, Yan Wu, Richard Bonneau, Vladimir Gligorijevic, Andreas Loukas
We introduce AbDiffuser, an equivariant and physics-informed diffusion model for the joint generation of antibody 3D structures and sequences.
1 code implementation • 14 Dec 2023 • Andreas Bergmeister, Karolis Martinkus, Nathanaël Perraudin, Roger Wattenhofer
However, most existing methods struggle with large graphs due to the complexity of representing the entire joint distribution across all node pairs and capturing both global and local graph structures simultaneously.
no code implementations • insights (ACL) 2022 • Yue Ding, Karolis Martinkus, Damian Pascual, Simon Clematide, Roger Wattenhofer
Different studies of the embedding space of transformer models suggest that the distribution of contextual representations is highly anisotropic - the embeddings are distributed in a narrow cone.