Learning from language: Intersections of infant and machine learning

Thursday, July 11, 2019, 11:00 am - 12:00 pm PDTiCal
6th Floor-CR#689
NL Seminar
Sandy LaTourette

Abstract: Young infants and machine learning algorithms face many of the same fundamental challenges when learning language. Learners often must identify referents in complex scenes, determine the relevance of different object features, and extend labels from previously viewed referents to new ones. In this talk, I examine several ways that infants solve these problems. In some cases, our work reveals word-learning mechanisms that are specific to the infant learner, such as labels' influence on object representations. However, other word-learning mechanisms, like infants' capacity for semi-supervised learning, show striking similarities in the ways that infants and machines overcome the challenges of language learning. Both similarities and differences offer intriguing opportunities for mutually informative, interdisciplinary exchanges.

Bio: Sandy LaTourrette is a 5th-year Ph.D. student in Cognitive Psychology at Northwestern University, advised by Dr. Sandra Waxman. He is a NSF Graduate Fellow, and his work focuses on the interactions of language learning and cognition across human development.

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