Zobrazeno 1 - 10
of 3 363
pro vyhledávání: '"A, Fumero"'
Deep learning systems deployed in real-world applications often encounter data that is different from their in-distribution (ID). A reliable system should ideally abstain from making decisions in this out-of-distribution (OOD) setting. Existing state
Externí odkaz:
http://arxiv.org/abs/2410.04525
Causal representation learning aims at recovering latent causal variables from high-dimensional observations to solve causal downstream tasks, such as predicting the effect of new interventions or more robust classification. A plethora of methods hav
Externí odkaz:
http://arxiv.org/abs/2409.02772
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
Neural models learn data representations that lie on low-dimensional manifolds, yet modeling the relation between these representational spaces is an ongoing challenge. By integrating spectral geometry principles into neural modeling, we show that th
Externí odkaz:
http://arxiv.org/abs/2406.14183
In this paper, we present a novel data-free method for merging neural networks in weight space. Differently from most existing works, our method optimizes for the permutations of network neurons globally across all layers. This allows us to enforce c
Externí odkaz:
http://arxiv.org/abs/2405.17897
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
Impaired noise adaptation contributes to speech intelligibility problems in people with hearing loss
Autor:
Miriam I. Marrufo-Pérez, Milagros J. Fumero, Almudena Eustaquio-Martín, Enrique A. Lopez-Poveda
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-10 (2024)
Abstract Understanding speech in noisy settings is harder for hearing-impaired (HI) people than for normal-hearing (NH) people, even when speech is audible. This is often attributed to hearing loss altering the neural encoding of temporal and/or spec
Externí odkaz:
https://doaj.org/article/cdeef93eea704dfdae046be864fb7830
Autor:
Fumero, Giuseppe, Batignani, Giovanni, Cassetta, Edoardo, Ferrante, Carino, Giagu, Stefano, Scopigno, Tullio
Noise manifests ubiquitously in nonlinear spectroscopy, where multiple sources contribute to experimental signals generating interrelated unwanted components, from random point-wise fluctuations to structured baseline signals. Mitigating strategies a
Externí odkaz:
http://arxiv.org/abs/2309.16933
Autor:
Kubicek, Ales, Stratikopoulos, Athanasios, Fumero, Juan, Foutris, Nikos, Kotselidis, Christos
In this article, we present TornadoQSim, an open-source quantum circuit simulation framework implemented in Java. The proposed framework has been designed to be modular and easily expandable for accommodating different user-defined simulation backend
Externí odkaz:
http://arxiv.org/abs/2305.14398