89 citations · 148 across the 19 of their papers we have counts for
6 papers · 2 filters
On the Relationship between Self-Attention and Convolutional Layers
Jean-Baptiste Cordonnier, Andreas Loukas, Martin Jaggi
Recent trends of incorporating attention mechanisms in vision have led researchers to reconsider the supremacy of convolutional layers as a primary building block. Beyond helping C…
What graph neural networks cannot learn: depth vs width
Andreas Loukas
This paper studies the expressive power of graph neural networks falling within the message-passing framework (GNNmp). Two results are presented. First, GNNmp are shown to be Turin…
Discriminative structural graph classification
Younjoo Seo, Andreas Loukas, Nathanaël Perraudin
This paper focuses on the discrimination capacity of aggregation functions: these are the permutation invariant functions used by graph neural networks to combine the features of n…
The role of invariance in spectral complexity-based generalization bounds
Konstantinos Pitas, Andreas Loukas, Mike Davies +1
Deep convolutional neural networks (CNNs) have been shown to be able to fit a random labeling over data while still being able to generalize well for normal labels. Describing CNN…
Extrapolating paths with graph neural networks
Jean-Baptiste Cordonnier, Andreas Loukas
We consider the problem of path inference: given a path prefix, i.e., a partially observed sequence of nodes in a graph, we want to predict which nodes are in the missing suffix. I…
Approximating Spectral Clustering via Sampling: a Review
Nicolas Tremblay, Andreas Loukas
Spectral clustering refers to a family of unsupervised learning algorithms that compute a spectral embedding of the original data based on the eigenvectors of a similarity graph. T…