3 citations · 3 across the 2 of their papers we have counts for
2 papers
cs.LG2023
Layer-wise training for self-supervised learning on graphs
Oscar Pina, Verónica Vilaplana
End-to-end training of graph neural networks (GNN) on large graphs presents several memory and computational challenges, and limits the application to shallow architectures as dept…
stat.ML2023★ 3 cited
SurvLIMEpy: A Python package implementing SurvLIME
Cristian Pachón-García, Carlos Hernández-Pérez, Pedro Delicado +1
In this paper we present SurvLIMEpy, an open-source Python package that implements the SurvLIME algorithm. This method allows to compute local feature importance for machine learni…