Transfer learning problems are typically framed as leveraging knowledge learned on a source task to improve learning on a related, but different, target task. Current transfer met...
Temporally-asymmetric Hebbian learning is a class of algorithms motivated by data from recent neurophysiology experiments. While traditional Hebbian learning rules use mean firin...
The APE (Adaptive Programming Environment) project focuses on applying Machine Learning techniques to embed a software assistant into the VisualWorks Smalltalk interactive program...
Coordinate gradient learning is motivated by the problem of variable selection and determining variable covariation. In this paper we propose a novel unifying framework for coordi...
Automated software customization is drawing increasing attention as a means to help users deal with the scope, complexity, potential intrusiveness, and ever-changing nature of mod...