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Learning Tensors and Random Matrix Theory

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Type-driven compositional distributional semantics interprets grammatical types of a system such as the CCG as tensor spaces and words of that type as elements therein. It offers a canonical way of composing the tensors, via tensor contraction. There is much less known about the characteristics of the tensors: is there a canonical way of building them and what statistical rules govern their distribution? Based on recent joint work with Wijnholds and Clark and with Kartsaklis, Ramgoolam and Sword, we provide some answers.

This talk is part of the NLIP Seminar Series series.

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