5 citations · 5 across the 2 of their papers we have counts for
3 papers
DT+GNN: A Fully Explainable Graph Neural Network using Decision Trees
Peter Müller, Lukas Faber, Karolis Martinkus +1
We propose the fully explainable Decision Tree Graph Neural Network (DT+GNN) architecture. In contrast to existing black-box GNNs and post-hoc explanation methods, the reasoning of…
On Isotropy Calibration of Transformers
Yue Ding, Karolis Martinkus, Damian Pascual +2
Different studies of the embedding space of transformer models suggest that the distribution of contextual representations is highly anisotropic - the embeddings are distributed in…
Scalable Graph Networks for Particle Simulations
Karolis Martinkus, Aurelien Lucchi, Nathanaël Perraudin
Learning system dynamics directly from observations is a promising direction in machine learning due to its potential to significantly enhance our ability to understand physical sy…