Karolis Martinkus

According to our database, Karolis Martinkus authored at least 15 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Generalizing to any diverse distribution: uniformity, gentle finetuning and rebalancing.

Andreas Loukas, Karolis Martinkus, Ed Wagstaff, Kyunghyun Cho

CoRR, 2024

GraphChef: Decision-Tree Recipes to Explain Graph Neural Networks.

Peter Müller, Lukas Faber, Karolis Martinkus, Roger Wattenhofer

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Efficient and Scalable Graph Generation through Iterative Local Expansion.

Andreas Bergmeister, Karolis Martinkus, Nathanaël Perraudin, Roger Wattenhofer

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Learning to Understand Graph Structure: From Classification to Generation.

Karolis Martinkus

PhD thesis, 2023

Discovering Graph Generation Algorithms.

Mihai Babiac, Karolis Martinkus, Roger Wattenhofer

CoRR, 2023

AbDiffuser: full-atom generation of in-vitro functioning antibodies.

Karolis Martinkus, Jan Ludwiczak, Wei-Ching Liang, Julien Lafrance-Vanasse, Isidro Hötzel, Arvind Rajpal, Yan Wu, Kyunghyun Cho, Richard Bonneau, Vladimir Gligorijevic, Andreas Loukas

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Automating Rigid Origami Design.

Jeremia Geiger, Karolis Martinkus, Oliver Richter, Roger Wattenhofer

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Agent-based Graph Neural Networks.

Karolis Martinkus, Pál András Papp, Benedikt Schesch, Roger Wattenhofer

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Diffusion Models for Graphs Benefit From Discrete State Spaces.

Kilian Konstantin Haefeli, Karolis Martinkus, Nathanaël Perraudin, Roger Wattenhofer

CoRR, 2022

DT+GNN: A Fully Explainable Graph Neural Network using Decision Trees.

Peter Müller, Lukas Faber, Karolis Martinkus, Roger Wattenhofer

CoRR, 2022

SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators.

Karolis Martinkus, Andreas Loukas, Nathanaël Perraudin, Roger Wattenhofer

Proceedings of the International Conference on Machine Learning, 2022

On Isotropy Calibration of Transformer Models.

Yue Ding, Karolis Martinkus, Damian Pascual, Simon Clematide, Roger Wattenhofer

Proceedings of the Third Workshop on Insights from Negative Results in NLP, 2022

2021

On Isotropy Calibration of Transformers.

Yue Ding, Karolis Martinkus, Damian Pascual, Simon Clematide, Roger Wattenhofer

CoRR, 2021

DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks.

Pál András Papp, Karolis Martinkus, Lukas Faber, Roger Wattenhofer

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Scalable Graph Networks for Particle Simulations.

Karolis Martinkus, Aurélien Lucchi, Nathanaël Perraudin

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021