127 citations · 296 across the 22 of their papers we have counts for
6 papers · 1 filter
Graph Kernel Neural Networks
Luca Cosmo, Giorgia Minello, Alessandro Bicciato +4
The convolution operator at the core of many modern neural architectures can effectively be seen as performing a dot product between an input matrix and a filter. While this is rea…
Unsupervised Source Separation via Bayesian Inference in the Latent Domain
Michele Mancusi, Emilian Postolache, Giorgio Mariani +4
State of the art audio source separation models rely on supervised data-driven approaches, which can be expensive in terms of labeling resources. On the other hand, approaches for…
Shape registration in the time of transformers
Giovanni Trappolini, Luca Cosmo, Luca Moschella +3
In this paper, we propose a transformer-based procedure for the efficient registration of non-rigid 3D point clouds. The proposed approach is data-driven and adopts for the first t…
Universal Spectral Adversarial Attacks for Deformable Shapes
Arianna Rampini, Franco Pestarini, Luca Cosmo +2
Machine learning models are known to be vulnerable to adversarial attacks, namely perturbations of the data that lead to wrong predictions despite being imperceptible. However, the…
Learning Spectral Unions of Partial Deformable 3D Shapes
Luca Moschella, Simone Melzi, Luca Cosmo +5
Spectral geometric methods have brought revolutionary changes to the field of geometry processing. Of particular interest is the study of the Laplacian spectrum as a compact, isome…
Learning disentangled representations via product manifold projection
Marco Fumero, Luca Cosmo, Simone Melzi +1
We propose a novel approach to disentangle the generative factors of variation underlying a given set of observations. Our method builds upon the idea that the (unknown) low-dimens…