Embedding And Ranking For Images Entities Items And Text
Abstract: We describe several recent results of applying embedding and ranking techniques to the real-world problems of image search and annotation, relation extraction, and music and video recommendation (Google Music and YouTube). Key issues include modifying the algorithms to increase their capacity, and making them scale to the size of the task (particularly at test time).
This is joint work with Aurelien Lucchi, Antoine Bordes, Nicolas Usunier, Oksana Yakhnenko, Ron Weiss, Hector Yee and Ameesh Makadia.
Bio: Jason Weston is a Research Scientist at Google NY since July 2009. He earned his PhD in machine learning at Royal Holloway, University of London and at AT&T Research in Red Bank, NJ (advisors: Alex Gammerman, Volodya Vovk and Vladimir Vapnik) in 2000. From 2000 to 2002, he was a Researcher at Biowulf technologies, New York. From 2002 to 2003 he was a Research Scientist at the Max Planck Institute for Biological Cybernetics, Tuebingen, Germany. From 2003 to June 2009 he was a Research Staff Member at NEC Labs America, Princeton. His interests lie in statistical machine learning and its application to text, audio and images. Jason has published over 80 papers, including best paper awards at ICML and ECML. He was also recently part of the YouTube team that won a National Academy of Television Arts & Sciences Emmy Award for Technology and Engineering for Personalized Recommendation Engines for Video Discovery.