We explore the problem of budgeted machine learning, in which the learning algorithm has free access to the training examples’ labels but has to pay for each attribute that is s...
Kun Deng, Chris Bourke, Stephen D. Scott, Julie Su...
Flexible resource management and scheduling policies require detailed system-state information. Traditional, monolithic operating systems with a centralized kernel derive the requ...
After having drawn up a state of the art on the theoretical feasibility of a system of periodic tasks scheduled by a preemptive algorithm at fixed priorities, we show in this art...
Abstract. We aim to create a model of emotional reactive virtual humans. This model will help to define realistic behavior for virtual characters based on emotions and events in t...
Abstract. During recent years much effort has been spent in incorporating problem specific a-priori knowledge into kernel methods for machine learning. A common example is a-prior...