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Karolis Martinkus
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2020 – today
- 2024
- [c9]Andreas Bergmeister, Karolis Martinkus, Nathanaël Perraudin, Roger Wattenhofer:
Efficient and Scalable Graph Generation through Iterative Local Expansion. ICLR 2024 - [c8]Peter Müller, Lukas Faber, Karolis Martinkus, Roger Wattenhofer:
GraphChef: Decision-Tree Recipes to Explain Graph Neural Networks. ICLR 2024 - [i12]Andreas Loukas, Karolis Martinkus, Ed Wagstaff, Kyunghyun Cho:
Generalizing to any diverse distribution: uniformity, gentle finetuning and rebalancing. CoRR abs/2410.05980 (2024) - 2023
- [b1]Karolis Martinkus:
Learning to Understand Graph Structure: From Classification to Generation. ETH Zurich, Zürich, Switzerland, 2023 - [c7]Karolis Martinkus, Pál András Papp, Benedikt Schesch, Roger Wattenhofer:
Agent-based Graph Neural Networks. ICLR 2023 - [c6]Jeremia Geiger, Karolis Martinkus, Oliver Richter, Roger Wattenhofer:
Automating Rigid Origami Design. IJCAI 2023: 5815-5823 - [c5]Karolis Martinkus, Jan Ludwiczak, Wei-Ching Liang, Julien Lafrance-Vanasse, Isidro Hötzel, Arvind Rajpal, Yan Wu, Kyunghyun Cho, Richard Bonneau, Vladimir Gligorijevic, Andreas Loukas:
AbDiffuser: full-atom generation of in-vitro functioning antibodies. NeurIPS 2023 - [i11]Mihai Babiac, Karolis Martinkus, Roger Wattenhofer:
Discovering Graph Generation Algorithms. CoRR abs/2304.12895 (2023) - [i10]Karolis Martinkus, Jan Ludwiczak, Kyunghyun Cho, Wei-Ching Liang, Julien Lafrance-Vanasse, Isidro Hötzel, Arvind Rajpal, Yan Wu, Richard Bonneau, Vladimir Gligorijevic, Andreas Loukas:
AbDiffuser: Full-Atom Generation of In-Vitro Functioning Antibodies. CoRR abs/2308.05027 (2023) - [i9]Andreas Bergmeister, Karolis Martinkus, Nathanaël Perraudin, Roger Wattenhofer:
Efficient and Scalable Graph Generation through Iterative Local Expansion. CoRR abs/2312.11529 (2023) - 2022
- [c4]Yue Ding, Karolis Martinkus, Damian Pascual, Simon Clematide, Roger Wattenhofer:
On Isotropy Calibration of Transformer Models. Insights@ACL 2022: 1-9 - [c3]Karolis Martinkus, Andreas Loukas, Nathanaël Perraudin, Roger Wattenhofer:
SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators. ICML 2022: 15159-15179 - [i8]Karolis Martinkus, Andreas Loukas, Nathanaël Perraudin, Roger Wattenhofer:
SPECTRE : Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators. CoRR abs/2204.01613 (2022) - [i7]Peter Müller, Lukas Faber, Karolis Martinkus, Roger Wattenhofer:
DT+GNN: A Fully Explainable Graph Neural Network using Decision Trees. CoRR abs/2205.13234 (2022) - [i6]Karolis Martinkus, Pál András Papp, Benedikt Schesch, Roger Wattenhofer:
Agent-based Graph Neural Networks. CoRR abs/2206.11010 (2022) - [i5]Kilian Konstantin Haefeli, Karolis Martinkus, Nathanaël Perraudin, Roger Wattenhofer:
Diffusion Models for Graphs Benefit From Discrete State Spaces. CoRR abs/2210.01549 (2022) - [i4]Jeremia Geiger, Karolis Martinkus, Oliver Richter, Roger Wattenhofer:
Automating Rigid Origami Design. CoRR abs/2211.13219 (2022) - 2021
- [c2]Karolis Martinkus, Aurélien Lucchi, Nathanaël Perraudin:
Scalable Graph Networks for Particle Simulations. AAAI 2021: 8912-8920 - [c1]Pál András Papp, Karolis Martinkus, Lukas Faber, Roger Wattenhofer:
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks. NeurIPS 2021: 21997-22009 - [i3]Yue Ding, Karolis Martinkus, Damian Pascual, Simon Clematide, Roger Wattenhofer:
On Isotropy Calibration of Transformers. CoRR abs/2109.13304 (2021) - [i2]Pál András Papp, Karolis Martinkus, Lukas Faber, Roger Wattenhofer:
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks. CoRR abs/2111.06283 (2021) - 2020
- [i1]Karolis Martinkus, Aurélien Lucchi, Nathanaël Perraudin:
Scalable Graph Networks for Particle Simulations. CoRR abs/2010.06948 (2020)
Coauthor Index
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