5 citations · 10 across the 13 of their papers we have counts for
3 papers · 1 filter
Topological obstruction to the training of shallow ReLU neural networks
Marco Nurisso, Pierrick Leroy, Francesco Vaccarino
Studying the interplay between the geometry of the loss landscape and the optimization trajectories of simple neural networks is a fundamental step for understanding their behavior…
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain
Guillermo Bernárdez, Lev Telyatnikov, Marco Montagna +70
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learnin…
Higher-order Laplacian Renormalization
Marco Nurisso, Marta Morandini, Maxime Lucas +3
We propose a cross-order Laplacian renormalization group (X-LRG) scheme for arbitrary higher-order networks. The renormalization group is a pillar of the theory of scaling, scale-i…