When: April 4th, 2013 from 11AM to noon.
Where: room B21
Speaker: Antoine Bordes (CR CNRS) from Heudiasyc (UT Compiègne)
Title: New models for highly multi-relational data
Abstract:
Many data such as movie preferences or knowledge bases are
multi-relational, in that they describe multiple relations between
entities. While there is a large body of work focused on modeling these
data, modeling these multiple types of relations jointly remains
challenging. Further, existing approaches tend to breakdown when the
number of these types grows. In this talk, we present methods for
modeling large multi-relational datasets, with possibly thousands of
relations. Our models are based on bilinear structures, which captures
various orders of interaction of the data. We illustrate the performance
of these approaches on standard tensor-factorization datasets where we
attain, or outperform, state-of-the-art results. Finally, a NLP
application demonstrates our scalability and the ability of our models
to learn efficient and semantically meaningful word representations.
