Karolis Martinkus

24 papers A* 8Journal 14Unranked 1
YearRankTypeTitle / Venue / Authors
2025 J jnl
Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins.
CoRR
Frédéric A. Dreyer, Jan Ludwiczak, Karolis Martinkus, Brennan Abanades, Robert G. Alberstein, Pan Kessel, Pranav Rao, Jae Hyeon Lee, Richard Bonneau, Andrew M. Watkins, Franziska Seeger
2025 J jnl
Unified all-atom molecule generation with neural fields.
CoRR
Matthieu Kirchmeyer, Pedro O. Pinheiro, Emma Willett, Karolis Martinkus, Joseph Kleinhenz, Emily K. Makowski, Andrew M. Watkins, Vladimir Gligorijevic, Richard Bonneau, Saeed Saremi
2024 A* conf
Efficient and Scalable Graph Generation through Iterative Local Expansion.
ICLR
Andreas Bergmeister, Karolis Martinkus, Nathanaël Perraudin, Roger Wattenhofer
2024 J jnl
Generalizing to any diverse distribution: uniformity, gentle finetuning and rebalancing.
CoRR
Andreas Loukas, Karolis Martinkus, Ed Wagstaff, Kyunghyun Cho
2024 A* conf
GraphChef: Decision-Tree Recipes to Explain Graph Neural Networks.
ICLR
Peter Müller, Lukas Faber, Karolis Martinkus, Roger Wattenhofer
2023 J jnl
AbDiffuser: Full-Atom Generation of In-Vitro Functioning Antibodies.
CoRR
Karolis Martinkus, Jan Ludwiczak, Kyunghyun Cho, Wei-Ching Liang, Julien Lafrance-Vanasse, Isidro Hötzel, Arvind Rajpal, Yan Wu, Richard Bonneau, Vladimir Gligorijevic, Andreas Loukas
2023 A* conf
AbDiffuser: full-atom generation of in-vitro functioning antibodies.
NeurIPS
Karolis Martinkus, Jan Ludwiczak, Wei-Ching Liang, Julien Lafrance-Vanasse, Isidro Hötzel, Arvind Rajpal, Yan Wu, Kyunghyun Cho, Richard Bonneau, Vladimir Gligorijevic, Andreas Loukas
2023 A* conf
Agent-based Graph Neural Networks.
ICLR
Karolis Martinkus, Pál András Papp, Benedikt Schesch, Roger Wattenhofer
2023 A* conf
Automating Rigid Origami Design.
IJCAI
Jeremia Geiger, Karolis Martinkus, Oliver Richter, Roger Wattenhofer
2023 J jnl
Discovering Graph Generation Algorithms.
CoRR
Mihai Babiac, Karolis Martinkus, Roger Wattenhofer
2023 J jnl
Efficient and Scalable Graph Generation through Iterative Local Expansion.
CoRR
Andreas Bergmeister, Karolis Martinkus, Nathanaël Perraudin, Roger Wattenhofer
2023 — —
Learning to Understand Graph Structure: From Classification to Generation.
Karolis Martinkus
2022 J jnl
Agent-based Graph Neural Networks.
CoRR
Karolis Martinkus, Pál András Papp, Benedikt Schesch, Roger Wattenhofer
2022 J jnl
Automating Rigid Origami Design.
CoRR
Jeremia Geiger, Karolis Martinkus, Oliver Richter, Roger Wattenhofer
2022 J jnl
DT+GNN: A Fully Explainable Graph Neural Network using Decision Trees.
CoRR
Peter Müller, Lukas Faber, Karolis Martinkus, Roger Wattenhofer
2022 J jnl
Diffusion Models for Graphs Benefit From Discrete State Spaces.
CoRR
Kilian Konstantin Haefeli, Karolis Martinkus, Nathanaël Perraudin, Roger Wattenhofer
2022 — conf
On Isotropy Calibration of Transformer Models.
Insights@ACL
Yue Ding, Karolis Martinkus, Damian Pascual, Simon Clematide, Roger Wattenhofer
2022 J jnl
SPECTRE : Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators.
CoRR
Karolis Martinkus, Andreas Loukas, Nathanaël Perraudin, Roger Wattenhofer
2022 A* conf
SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators.
ICML
Karolis Martinkus, Andreas Loukas, Nathanaël Perraudin, Roger Wattenhofer
2021 A* conf
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks.
NeurIPS
Pál András Papp, Karolis Martinkus, Lukas Faber, Roger Wattenhofer
2021 J jnl
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks.
CoRR
Pál András Papp, Karolis Martinkus, Lukas Faber, Roger Wattenhofer
2021 J jnl
On Isotropy Calibration of Transformers.
CoRR
Yue Ding, Karolis Martinkus, Damian Pascual, Simon Clematide, Roger Wattenhofer
2021 A* conf
Scalable Graph Networks for Particle Simulations.
AAAI
Karolis Martinkus, Aurélien Lucchi, Nathanaël Perraudin
2020 J jnl
Scalable Graph Networks for Particle Simulations.
CoRR
Karolis Martinkus, Aurélien Lucchi, Nathanaël Perraudin