Zobrazeno 1 - 10
of 32 245
pro vyhledávání: '"Vilas, A."'
Contrary to on-road autonomous navigation, off-road autonomy is complicated by various factors ranging from sensing challenges to terrain variability. In such a milieu, data-driven approaches have been commonly employed to capture intricate vehicle-e
Externí odkaz:
http://arxiv.org/abs/2409.10347
Autor:
Castro, David Pérez, Vilas, Ana Fernández, Fernández-Veiga, Manuel, Rodríguez, Mateo Blanco, Redondo, Rebeca P. Díaz
We implement a simulation environment on top of NetSquid that is specifically designed for estimating the end-to-end fidelity across a path of quantum repeaters or quantum switches. The switch model includes several generalizations which are not curr
Externí odkaz:
http://arxiv.org/abs/2410.09779
Many proposed applications of neural networks in machine learning, cognitive/brain science, and society hinge on the feasibility of inner interpretability via circuit discovery. This calls for empirical and theoretical explorations of viable algorith
Externí odkaz:
http://arxiv.org/abs/2410.08025
Independent sets in graphs, i.e., subsets of vertices where no two are adjacent, have long been studied, for instance as a model of hard-core gas. The $d$-dimensional hypercube, $\{0,1\}^d$, with the nearest neighbor structure, has been a particularl
Externí odkaz:
http://arxiv.org/abs/2410.07080
Autor:
Heredia-Lidón, Álvaro, Echeverry-Quiceno, Luis M., González, Alejandro, Hostalet, Noemí, Pomarol-Clotet, Edith, Fortea, Juan, Fatjó-Vilas, Mar, Martínez-Abadías, Neus, Sevillano, Xavier
Facial dysmorphologies have emerged as potential critical indicators in the diagnosis and prognosis of genetic, psychotic and rare disorders. While in certain conditions these dysmorphologies are severe, in other cases may be subtle and not perceivab
Externí odkaz:
http://arxiv.org/abs/2410.00711
Autor:
Cajaraville-Aboy, Diego, Fernández-Vilas, Ana, Díaz-Redondo, Rebeca P., Fernández-Veiga, Manuel
Federated Learning (FL) emerges as a distributed machine learning approach that addresses privacy concerns by training AI models locally on devices. Decentralized Federated Learning (DFL) extends the FL paradigm by eliminating the central server, the
Externí odkaz:
http://arxiv.org/abs/2409.17754
Autor:
Guo, Manshan, Choksi, Bhavin, Sadiya, Sari, Gifford, Alessandro T., Vilas, Martina G., Cichy, Radoslaw M., Roig, Gemma
In contrast to human vision, artificial neural networks (ANNs) remain relatively susceptible to adversarial attacks. To address this vulnerability, efforts have been made to transfer inductive bias from human brains to ANNs, often by training the ANN
Externí odkaz:
http://arxiv.org/abs/2409.03646
Societal-scale deployment of autonomous vehicles requires them to coexist with human drivers, necessitating mutual understanding and coordination among these entities. However, purely real-world or simulation-based experiments cannot be employed to e
Externí odkaz:
http://arxiv.org/abs/2406.05465
Autor:
Beigi, Majed Valad, Cao, Yi, Gurumurthi, Sudhanva, Recchia, Charles, Walton, Andrew, Sridharan, Vilas
This paper is a corrigendum to the paper by Beigi et al. published at HPCA 2023 https://doi.org/10.1109/HPCA56546.2023.10071066. The HPCA paper presented a detailed field data analysis of faults observed at scale in DDR4 DRAM from two different memor
Externí odkaz:
http://arxiv.org/abs/2408.15302
Autor:
Panda, Mahadev Prasad, Tiezzi, Matteo, Vilas, Martina, Roig, Gemma, Eskofier, Bjoern M., Zanca, Dario
We introduce Foveation-based Explanations (FovEx), a novel human-inspired visual explainability (XAI) method for Deep Neural Networks. Our method achieves state-of-the-art performance on both transformer (on 4 out of 5 metrics) and convolutional mode
Externí odkaz:
http://arxiv.org/abs/2408.02123