140 citations · 401 across the 23 of their papers we have counts for
28 papers
Neural State-Space Models: Empirical Evaluation of Uncertainty Quantification
Marco Forgione, Dario Piga
Effective quantification of uncertainty is an essential and still missing step towards a greater adoption of deep-learning approaches in different applications, including mission-c…
Experience in Engineering Complex Systems: Active Preference Learning with Multiple Outcomes and Certainty Levels
Le Anh Dao, Loris Roveda, Marco Maccarini +8
Black-box optimization refers to the optimization problem whose objective function and/or constraint sets are either unknown, inaccessible, or non-existent. In many applications, e…
Learning Choice Functions with Gaussian Processes
Alessio Benavoli, Dario Azzimonti, Dario Piga
In consumer theory, ranking available objects by means of preference relations yields the most common description of individual choices. However, preference-based models assume tha…
Visual Servoing with Geometrically Interpretable Neural Perception
Antonio Paolillo, Mirko Nava, Dario Piga +1
An increasing number of nonspecialist robotic users demand easy-to-use machines. In the context of visual servoing, the removal of explicit image processing is becoming a trend, al…
Direct identification of continuous-time linear switched state-space models
Manas Mejari, Dario Piga
This paper presents an algorithm for direct continuous-time (CT) identification of linear switched state-space (LSS) models. The key idea for direct CT identification is based on a…
Learning neural state-space models: do we need a state estimator?
Marco Forgione, Manas Mejari, Dario Piga
In recent years, several algorithms for system identification with neural state-space models have been introduced. Most of the proposed approaches are aimed at reducing the computa…