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pro vyhledávání: '"Moschella, A"'
The emergence of similar representations between independently trained neural models has sparked significant interest in the representation learning community, leading to the development of various methods to obtain communication between latent space
Externí odkaz:
http://arxiv.org/abs/2406.15057
Autor:
Moschella, Luca
As NNs permeate various scientific and industrial domains, understanding the universality and reusability of their representations becomes crucial. At their core, these networks create intermediate neural representations, indicated as latent spaces,
Externí odkaz:
http://arxiv.org/abs/2406.11014
Autor:
Ricciardi, Antonio Pio, Maiorca, Valentino, Moschella, Luca, Marin, Riccardo, Rodolà, Emanuele
Visual Reinforcement Learning is a popular and powerful framework that takes full advantage of the Deep Learning breakthrough. However, it is also known that variations in the input (e.g., different colors of the panorama due to the season of the yea
Externí odkaz:
http://arxiv.org/abs/2404.12917
Autor:
Moschella, Ugo
We review the role of the spectral condition as a characteristic feature unifying Minkowski, de Sitter and anti de Sitter Quantum Field Theory. In this context, we highlight the role of an important class of plane waves which are either de Sitter or
Externí odkaz:
http://arxiv.org/abs/2403.15893
We discuss general one and two-loops banana diagrams with arbitrary masses on the de Sitter spacetime by using direct methods of dS quantum field theory in the dimensional regularization approach. In the one-loop case we also compute the effective po
Externí odkaz:
http://arxiv.org/abs/2403.13145
We discuss general one and two-loop banana diagrams and one-loop diagrams with external lines with arbitrary masses on the anti de Sitter spacetime by using methods of AdS quantum field theory in the dimensional regularization approach. The banana di
Externí odkaz:
http://arxiv.org/abs/2403.13142
Autor:
Crisostomi, Donato, Cannistraci, Irene, Moschella, Luca, Barbiero, Pietro, Ciccone, Marco, Liò, Pietro, Rodolà, Emanuele
Models trained on semantically related datasets and tasks exhibit comparable inter-sample relations within their latent spaces. We investigate in this study the aggregation of such latent spaces to create a unified space encompassing the combined inf
Externí odkaz:
http://arxiv.org/abs/2311.06547
Autor:
Maiorca, Valentino, Moschella, Luca, Norelli, Antonio, Fumero, Marco, Locatello, Francesco, Rodolà, Emanuele
While different neural models often exhibit latent spaces that are alike when exposed to semantically related data, this intrinsic similarity is not always immediately discernible. Towards a better understanding of this phenomenon, our work shows how
Externí odkaz:
http://arxiv.org/abs/2311.00664
It has been observed that representations learned by distinct neural networks conceal structural similarities when the models are trained under similar inductive biases. From a geometric perspective, identifying the classes of transformations and the
Externí odkaz:
http://arxiv.org/abs/2310.01211
We reconsider the computation of banana integrals at different loops, by working in the configuration space, in any dimension. We show how the 2-loop banana integral can be computed directly from the configuration space representation, without the ne
Externí odkaz:
http://arxiv.org/abs/2304.00624