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» Graph model selection using maximum likelihood
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ACSAC
2009
IEEE
14 years 3 months ago
Modeling Modern Network Attacks and Countermeasures Using Attack Graphs
Abstract—By accurately measuring risk for enterprise networks, attack graphs allow network defenders to understand the most critical threats and select the most effective counter...
Kyle Ingols, Matthew Chu, Richard Lippmann, Seth E...
IROS
2008
IEEE
156views Robotics» more  IROS 2008»
14 years 3 months ago
Bayesian state estimation and behavior selection for autonomous robotic exploration in dynamic environments
— In order to be truly autonomous, robots that operate in natural, populated environments must have the ability to create a model of these unpredictable dynamic environments and ...
Georgios Lidoris, Dirk Wollherr, Martin Buss
TNN
2008
177views more  TNN 2008»
13 years 8 months ago
Adaptive Importance Sampling to Accelerate Training of a Neural Probabilistic Language Model
Previous work on statistical language modeling has shown that it is possible to train a feed-forward neural network to approximate probabilities over sequences of words, resulting...
Yoshua Bengio, Jean-Sébastien Senecal
ISQED
2000
IEEE
117views Hardware» more  ISQED 2000»
14 years 1 months ago
Realistic Worst-Case Modeling by Performance Level Principal Component Analysis
A new algorithm to determine the number and value of realistic worst-case models for the performance of module library components is presented in this paper. The proposed algorith...
Alessandra Nardi, Andrea Neviani, Carlo Guardiani
INFORMATICALT
2011
112views more  INFORMATICALT 2011»
13 years 3 months ago
The Minimum Density Power Divergence Approach in Building Robust Regression Models
It is well known that in situations involving the study of large datasets where influential observations or outliers maybe present, regression models based on the Maximum Likeliho...
Alessandra Durio, Ennio Davide Isaia