2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2021
Pointspectrum: Equivariance Meets Laplacian Filtering for Graph Representation Learning
Marinos Poiitis, Pavlos Sermpezis, Athena Vakali
Graph Representation Learning (GRL) has become essential for modern graph data mining and learning tasks. GRL aims to capture the graph's structural information and exploit it in c…
cs.LG2021★ 2 cited
What training reveals about neural network complexity
Andreas Loukas, Marinos Poiitis, Stefanie Jegelka
This work explores the Benevolent Training Hypothesis (BTH) which argues that the complexity of the function a deep neural network (NN) is learning can be deduced by its training d…