We present a novel framework for learning to interpret and generate language using only perceptual context as supervision. We demonstrate its capabilities by developing a system t...
Many machine-learning algorithms learn rules of behavior from individual end users, such as taskoriented desktop organizers and handwriting recognizers. These rules form a “prog...
Todd Kulesza, Simone Stumpf, Margaret M. Burnett, ...
Goal-driven autonomy (GDA) is a conceptual model for creating an autonomous agent that monitors a set of expectations during plan execution, detects when discrepancies occur, buil...
Abstract. Topic models are a discrete analogue to principle component analysis and independent component analysis that model topic at the word level within a document. They have ma...
Abstract. We study predicate selection functions (also known as splitting rules) for structural decision trees and propose two improvements to existing schemes. The first is in cl...