In this paper, we present a general and an efficient algorithm for automatic selection of new application-specific instructions under hardware resources constraints. The instructi...
Carlo Galuzzi, Elena Moscu Panainte, Yana Yankova,...
Domain-specific features are important in representing problem structure throughout machine learning and decision-theoretic planning. In planning, once state features are provide...
Background: We have recently introduced a predictive framework for studying gene transcriptional regulation in simpler organisms using a novel supervised learning algorithm called...
Anshul Kundaje, Manuel Middendorf, Mihir Shah, Chr...
Meta-Learning has been used to relate the performance of algorithms and the features of the problems being tackled. The knowledge in Meta-Learning is acquired from a set of meta-e...
One Feature (1F) is a simple and intuitive pruning strategy that reduces considerably the amount of computations required by Nearest-Neighbor gesture classifiers while still pres...