Kicking off next week's special DIRO talks we have Jacob Andreas from UC Berkeley giving a talk on Monday Feb 26 at 11:00AM in room Claire-McNicoll Z240.
You should be able to learn quite a bit from this presentation just by listening!
Michael
TITLE Learning from Language
KEYWORDS NLP, compositional/modular learning, reinforcement learning, interpretable ML
ABSTRACT
Natural language is built from a library of concepts and compositional
operators that provide a rich source of information about how humans
understand the world. Can this information help us build better
machine learning models? In this talk, we'll explore three ways of
integrating compositional linguistic structure and learning: using
language as a source of modular reasoning operators for question
answering, as a scaffold for fast and generalizable reinforcement
learning, and as a tool for understanding representations in neural
networks.
BIO
Jacob Andreas is a fifth-year PhD student at UC Berkeley working in
natural language processing. He holds a B.S. from Columbia and an
M.Phil. from Cambridge, where he studied as a Churchill scholar. His
papers were recognized at NAACL 2016 and ICML 2017. Jacob has been an
NSF graduate fellow, a Huawei--Berkeley AI fellow, and a Facebook
fellow.