2 citations · 2 across the 4 of their papers we have counts for
4 papers
Topology and Geometry of the Learning Space of ReLU Networks: Connectivity and Singularities
Marco Nurisso, Pierrick Leroy, Giovanni Petri +1
Understanding the properties of the parameter space in feed-forward ReLU networks is critical for effectively analyzing and guiding training dynamics. After initialization, trainin…
Attributes Shape the Embedding Space of Face Recognition Models
Pierrick Leroy, Antonio Mastropietro, Marco Nurisso +1
Face Recognition (FR) tasks have made significant progress with the advent of Deep Neural Networks, particularly through margin-based triplet losses that embed facial images into h…
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…