Zobrazeno 1 - 10
of 5 000
pro vyhledávání: '"Vezzani, A."'
Autor:
Ertl, Veronika, Vezzani, Alberto
We use motivic methods to give a quick proof of Berthelot's conjecture stating that the push-forward map in rigid cohomology of the structural sheaf along a smooth and proper map has a canonical structure of overconvergent F-isocrystal on the base.
Externí odkaz:
http://arxiv.org/abs/2406.02182
We study rare events in the extreme value statistics of stochastic symmetric jump processes with power tails in the distributions of the jumps, using the big jump principle. The principle states that in the presence of stochastic processes with power
Externí odkaz:
http://arxiv.org/abs/2404.19406
Human behaviour strongly influences the spread of infectious diseases: understanding the interplay between epidemic dynamics and adaptive behaviours is essential to improve response strategies to epidemics, with the goal of containing the epidemic wh
Externí odkaz:
http://arxiv.org/abs/2402.09076
Millions of people around the world are infected with mosquito-borne diseases each year. One of the most dangerous species is Aedes aegypti, the main vector of viruses such as dengue, yellow fever, chikungunya, and Zika, among others. Mosquito preven
Externí odkaz:
http://arxiv.org/abs/2401.16258
We perform accurate numerical experiments with fully-connected (FC) one-hidden layer neural networks trained with a discretized Langevin dynamics on the MNIST and CIFAR10 datasets. Our goal is to empirically determine the regimes of validity of a rec
Externí odkaz:
http://arxiv.org/abs/2401.11004
Publikováno v:
Phys. Rev. Lett. 132, 187101 (2024)
Rare events in the first-passage distributions of jump processes are capable of triggering anomalous reactions or series of events. Estimating their probability is particularly important when the jump probabilities have broad-tailed distributions, an
Externí odkaz:
http://arxiv.org/abs/2309.16227
Autor:
Simoni, Alessandro, Marchetti, Francesco, Borghi, Guido, Becattini, Federico, Seidenari, Lorenzo, Vezzani, Roberto, Del Bimbo, Alberto
Technologies to enable safe and effective collaboration and coexistence between humans and robots have gained significant importance in the last few years. A critical component useful for realizing this collaborative paradigm is the understanding of
Externí odkaz:
http://arxiv.org/abs/2308.12914
Autor:
Di Nucci, Davide, Simoni, Alessandro, Tomei, Matteo, Ciuffreda, Luca, Vezzani, Roberto, Cucchiara, Rita
Neural Radiance Fields (NeRFs) have gained widespread recognition as a highly effective technique for representing 3D reconstructions of objects and scenes derived from sets of images. Despite their efficiency, NeRF models can pose challenges in cert
Externí odkaz:
http://arxiv.org/abs/2307.12718
Feature learning, or the ability of deep neural networks to automatically learn relevant features from raw data, underlies their exceptional capability to solve complex tasks. However, feature learning seems to be realized in different ways in fully-
Externí odkaz:
http://arxiv.org/abs/2307.11807
Autor:
Bousmalis, Konstantinos, Vezzani, Giulia, Rao, Dushyant, Devin, Coline, Lee, Alex X., Bauza, Maria, Davchev, Todor, Zhou, Yuxiang, Gupta, Agrim, Raju, Akhil, Laurens, Antoine, Fantacci, Claudio, Dalibard, Valentin, Zambelli, Martina, Martins, Murilo, Pevceviciute, Rugile, Blokzijl, Michiel, Denil, Misha, Batchelor, Nathan, Lampe, Thomas, Parisotto, Emilio, Żołna, Konrad, Reed, Scott, Colmenarejo, Sergio Gómez, Scholz, Jon, Abdolmaleki, Abbas, Groth, Oliver, Regli, Jean-Baptiste, Sushkov, Oleg, Rothörl, Tom, Chen, José Enrique, Aytar, Yusuf, Barker, Dave, Ortiz, Joy, Riedmiller, Martin, Springenberg, Jost Tobias, Hadsell, Raia, Nori, Francesco, Heess, Nicolas
The ability to leverage heterogeneous robotic experience from different robots and tasks to quickly master novel skills and embodiments has the potential to transform robot learning. Inspired by recent advances in foundation models for vision and lan
Externí odkaz:
http://arxiv.org/abs/2306.11706