Zobrazeno 1 - 10
of 358
pro vyhledávání: '"Rubino, Gerardo"'
Autor:
Kaada, Soumeya, Tran, Dinh-Hieu, Van Huynh, Nguyen, Morel, Marie-Line Alberi, Jelassi, Sofiene, Rubino, Gerardo
Resilience is defined as the ability of a network to resist, adapt, and quickly recover from disruptions, and to continue to maintain an acceptable level of services from users' perspective. With the advent of future radio networks, including advance
Externí odkaz:
http://arxiv.org/abs/2407.18066
Modeling non-stationary data is a challenging problem in the field of continual learning, and data distribution shifts may result in negative consequences on the performance of a machine learning model. Classic learning tools are often vulnerable to
Externí odkaz:
http://arxiv.org/abs/2404.16656
Autor:
Basterrech, Sebastian, Rubino, Gerardo
The Echo State Network (ESN) is a class of Recurrent Neural Network with a large number of hidden-hidden weights (in the so-called reservoir). Canonical ESN and its variations have recently received significant attention due to their remarkable succe
Externí odkaz:
http://arxiv.org/abs/2206.04951
We discuss estimating the probability that the sum of nonnegative independent and identically distributed random variables falls below a given threshold, i.e., $\mathbb{P}(\sum_{i=1}^{N}{X_i} \leq \gamma)$, via importance sampling (IS). We are partic
Externí odkaz:
http://arxiv.org/abs/2101.09514
Autor:
Basterrech, Sebastián, Rubino, Gerardo
Publikováno v:
In Applied Soft Computing Journal September 2023 144
Autor:
Basterrech, Sebastián, Rubino, Gerardo
Publikováno v:
Neural Network World, Volume 5, Number 15, pp.:457-499, 2015
Random Neural Networks (RNNs) are a class of Neural Networks (NNs) that can also be seen as a specific type of queuing network. They have been successfully used in several domains during the last 25 years, as queuing networks to analyze the performan
Externí odkaz:
http://arxiv.org/abs/1609.04846
Autor:
Torres-Cruz, Noé, Rivero-Angeles, Mario E., Rubino, Gerardo, Menchaca-Mendez, Ricardo, Menchaca-Mendez, Rolando, Ramirez, David
Publikováno v:
In Journal of Network and Computer Applications 1 July 2020 161
Publikováno v:
In Optical Switching and Networking January 2020 35