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--></style></div><div data-marker="__QUOTED_TEXT__"><div class="WordSection1"><p class="MsoNormal">Jacob Andreas from University of California at Berkeley</p><p class="MsoNormal">Monday, April 16, 2018 - 12:30pm</p><p class="MsoNormal">Computer Science - Room 105</p><p class="MsoNormal">Host: Sanjeev Arora</p><p class="MsoNormal"> </p><p class="MsoNormal">“Learning from Language”</p><p class="MsoNormal"> </p><p class="MsoNormal">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. <span style="mso-spacerun:yes"> </span>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.</p><p class="MsoNormal"> </p><p class="MsoNormal">Bio:</p><p class="MsoNormal">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.</p><p class="MsoNormal"> </p></div><br></div></div></body></html>