We present a methodology to learn explicit Model Predictive Control (eMPC) laws from sample data points with tunable complexity. The learning process is cast in a special Neural Network setting where the coefficients of two linear layers and a parametric q ...
Over the past few years, there have been fundamental breakthroughs in core problems in machine learning, largely driven by advances in deep neural networks. The amount of annotated data drastically increased and supervised deep discriminative models exceed ...
Purpose Within higher education, there was an abrupt shift from face-to-face to online lecturing with the introduction of social distancing measures in light of a global pandemic. The purpose of this study is to enrich the connection between students and i ...
We are living in the era of "Big Data", an era characterized by a voluminous amount of available data. Such amount is mainly due to the continuing advances in the computational capabilities for capturing, storing, transmitting and processing data. However, ...
Object manipulation is a challenging task for robotics, as the physics involved in object interaction is complex and hard to express analytically. Here we introduce a modular approach for learning a manipulation strategy from human demonstration. Firstly w ...
Massive online open courses’ (MOOCs’) students who use discussion forums have higher chances of finishing the course. However, little research has been conducted for understanding the underlying fac- tors. One of the reasons which hinders the analysis is t ...
