Zobrazeno 1 - 10
of 10 071
pro vyhledávání: '"Franceschelli, A"'
This study explores the potential of neuromorphic EBV cameras for fast latent coordinate representation in turbulent flows. Unlike conventional imaging systems, EBV cameras asynchronously capture changes in temporal contrast at each pixel, delivering
Externí odkaz:
http://arxiv.org/abs/2410.18940
Autor:
Pardolesi, Roberto
Publikováno v:
Il Foro Italiano, 2016 Sep 01. 139(9), 2729/2730-2737/2738.
Externí odkaz:
https://www.jstor.org/stable/44875833
The training process of foundation models as for other classes of deep learning systems is based on minimizing the reconstruction error over a training set. For this reason, they are susceptible to the memorization and subsequent reproduction of trai
Externí odkaz:
http://arxiv.org/abs/2407.13493
This paper studies the problem of increasing the connectivity of an ad-hoc peer-to-peer network subject to cyber-attacks targeting the agents in the network. The adopted strategy involves the design of local interaction rules for the agents to locall
Externí odkaz:
http://arxiv.org/abs/2406.18467
Autor:
Molinaro, E.
Publikováno v:
Il Foro Italiano, 2013 Jan 01. 136(1), 41/42-43/44.
Externí odkaz:
https://www.jstor.org/stable/23392383
Autor:
Sebastián, Eduardo, Franceschelli, Mauro, Gasparri, Andrea, Montijano, Eduardo, Sagüés, Carlos
This paper presents a novel accelerated distributed algorithm for unconstrained consensus optimization over static undirected networks. The proposed algorithm combines the benefits of acceleration from momentum, the robustness of the alternating dire
Externí odkaz:
http://arxiv.org/abs/2405.08590
Large language models are revolutionizing several areas, including artificial creativity. However, the process of generation in machines profoundly diverges from that observed in humans. In particular, machine generation is characterized by a lack of
Externí odkaz:
http://arxiv.org/abs/2405.00099
The Overfitted Brain hypothesis suggests dreams happen to allow generalization in the human brain. Here, we ask if the same is true for reinforcement learning agents as well. Given limited experience in a real environment, we use imagination-based re
Externí odkaz:
http://arxiv.org/abs/2403.07979
Publikováno v:
Il Foro Italiano, 2011 Jul 01. 134(7/8), 2009/2010-2013/2014.
Externí odkaz:
https://www.jstor.org/stable/23207148
Publikováno v:
Il Foro Italiano, 2010 Sep 01. 133(9), 2537/2538-2545/2546.
Externí odkaz:
https://www.jstor.org/stable/23206559