2 citations · 2 across the 6 of their papers we have counts for
6 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…
Diagrammatic Hochschild cohomology via cohomology of categories, and incidence algebras
Luigi Caputi, Francesco Vaccarino
In this paper, we study the Hochschild cohomology of diagrams of algebras introduced by Gerstenhaber and Schack and provide computations for filtrations of incidence algebras. Our…
Bound by semanticity: universal laws governing the generalization-identification tradeoff
Marco Nurisso, Jesseba Fernando, Raj Deshpande +9
Intelligent systems must deploy internal representations that are simultaneously structured -- to support broad generalization -- and selective -- to preserve input identity. We ex…
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…
Scale-Free Image Keypoints Using Differentiable Persistent Homology
Giovanni Barbarani, Francesco Vaccarino, Gabriele Trivigno +3
In computer vision, keypoint detection is a fundamental task, with applications spanning from robotics to image retrieval; however, existing learning-based methods suffer from scal…