Brains, Minds + Machines Seminar Series: Calibrating Generative Models: The Probabilistic Chomsky-Schützenberger Hierarchy

Tuesday, October 29, 2019 at 4:00pm to 5:00pm

Building 32, Star Seminar Room (Stata D463)
32 VASSAR ST, Cambridge, MA 02139

Speaker: Prof. Thomas Icard, Stanford  University

Abstract: How might we assess the expressive capacity of different classes of probabilistic generative models? The subject of this talk is an approach that appeals to machines of increasing strength (finite-state, recursive, etc.), or equivalently, by probabilistic grammars of increasing complexity, giving rise to a probabilistic version of the familiar Chomsky hierarchy. Many common probabilistic models — hidden Markov models, generative neural networks, probabilistic programming languages, etc. — naturally fit into the hierarchy. The aim of the talk is to give as comprehensive a picture as possible of the landscape of distributions that can be expressed at each level in the hierarchy. Of special interest is what this pattern of results might mean for cognitive modeling.




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School of Engineering (SoE), School of Science


neuroscience, McGovernMIT, neurosciencemit, machine learning, artificial intelligence, Computational Neuroscience


Center for Brains, Minds and Machines (CBMM)


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