1 citations · 4 across the 8 of their papers we have counts for
8 papers
Assessing the Robustness of Intelligence-Driven Reinforcement Learning
Lorenzo Nodari, Federico Cerutti
Robustness to noise is of utmost importance in reinforcement learning systems, particularly in military contexts where high stakes and uncertain environments prevail. Noise and unc…
Knowledge from Uncertainty in Evidential Deep Learning
Cai Davies, Marc Roig Vilamala, Alun D. Preece +3
This work reveals an evidential signal that emerges from the uncertainty value in Evidential Deep Learning (EDL). EDL is one example of a class of uncertainty-aware deep learning a…
Sound-skwatter (Did You Mean: Sound-squatter?) AI-powered Generator for Phishing Prevention
Rodolfo Valentim, Idilio Drago, Marco Mellia +1
Sound-squatting is a phishing attack that tricks users into malicious resources by exploiting similarities in the pronunciation of words. Proactive defense against sound-squatting…
Research Note on Uncertain Probabilities and Abstract Argumentation
Pietro Baroni, Federico Cerutti, Massimiliano Giacomin +2
The sixth assessment of the international panel on climate change (IPCC) states that "cumulative net CO2 emissions over the last decade (2010-2019) are about the same size as the 1…
SOLBP: Second-Order Loopy Belief Propagation for Inference in Uncertain Bayesian Networks
Conrad D. Hougen, Lance M. Kaplan, Magdalena Ivanovska +3
In second-order uncertain Bayesian networks, the conditional probabilities are only known within distributions, i.e., probabilities over probabilities. The delta-method has been ap…
Uncertain Bayesian Networks: Learning from Incomplete Data
Conrad D. Hougen, Lance M. Kaplan, Federico Cerutti +1
When the historical data are limited, the conditional probabilities associated with the nodes of Bayesian networks are uncertain and can be empirically estimated. Second order esti…