We present an efficient framework for dynamic reconfiguration of application-specific custom instructions. A key component of this framework is an iterative algorithm for temporal...
Extensive experimental evidence is required to study the impact of text categorization approaches on real data and to assess the performance within operational scenarios. In this ...
Roberto Basili, Alessandro Moschitti, Maria Teresa...
"Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning...
Carl Edward Rasmussen and Christopher K. I. Willia...
Abstract. We present an implementation of model-based online reinforcement learning (RL) for continuous domains with deterministic transitions that is specifically designed to achi...
We propose a low-overhead sampling infrastructure for gathering information from the executions experienced by a program’s user community. Several example applications illustrat...
Ben Liblit, Alexander Aiken, Alice X. Zheng, Micha...