This paper presents a direct method for finding corresponding pairs of parts between two shapes. Statistical knowledge about a large number of parts from many different objects is...
Dimensionality reduction is a much-studied task in machine learning in which high-dimensional data is mapped, possibly via a non-linear transformation, onto a low-dimensional mani...
Abstract. We consider a problem that is related to the “Universal Encoding Problem” from information theory. The basic goal is to find rules that map “partial information”...
Abstract. We consider the problem of learning a mapping from question to answer messages. The training data for this problem consist of pairs of messages that have been received an...
Our KNOWITALL system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an autonomous, domain...
Oren Etzioni, Michael J. Cafarella, Doug Downey, A...