MC4 – Learning Mappings via Symbolic, Probabilistic, and Connectionist Modeling

Lecturer: Afsaneh Fazly
Fields: Machine Learning, Cognitive Modelling, Language Acquisition


In session 1, we cover the basics of several mapping (association) problems, including theoretically important challenges such as the acquisition of word meanings in young children, as well as applied settings such as learning multimodal or multilingual representations.

Session 2 focuses on the early approaches applied to a mapping problem, including symbolic and probabilistic methods.

Session 3 covers the more recent techniques (linear transformations and deep learning), in the context of several mapping problems, such as learning multimodal and multilingual mappings.


The objective is to cover three different approaches applied to the same problem of learning mappings across modalities (e.g., learning the meanings of words, learning mappings between audio/words and image/video segments, learning multilingual representations, etc.).


J.M. Siskind (1995). Grounding Language in PerceptionArtificial Intelligence Review, 8:371-391, 1995. [LINK]

J.M. Siskind (1996). A Computational Study of Cross-Situational Techniques for Learning Word-to-Meaning MappingsCognition, 61(1-2):39-91, October/November 1996. Also appeared in Computational Approaches to Language Acquisition, M.R. Brent, ed., Elsevier, pp. 39-91, 1996. [LINK]

Frank, M. C., Goodman, N. D., & Tenenbaum, J. B. (2009). Using speakers’ referential intentions to model early cross-situational word learningPsychological Science, 20, 579-585. [LINK]

Fazly, A., Alishahi A., Stevenson, S. (2010). A probabilistic computational model of cross-situational word learning. Cognitive Science: A Multidisciplinary Journal, 34(6): 1017—1063. [LINK]

Tadas Baltrusaitis, Chaitanya Ahuja, and Louis-Philippe Morency (2017). Multimodal Machine Learning: A Survey and Taxonomy. [LINK]

Zhang, Y., Chen, C.H., & Yu, C. (2019). Mechanisms of Cross-situational Learning: Behavioral and Computational Evidence. Advances in child development and behavior. [LINK]

Sebastian Ruder, Ivan Vulić, Anders Søgaard (2019). A Survey of Cross-lingual Word Embedding Models. Journal of Artificial Intelligence Research 65: 569-631. [LINK]


Dr. Afsaneh Fazly

Afsaneh Fazly is a Research Director at Samsung Toronto AI Centre, and an Adjunct Professor at the Computer Science Department of University of Toronto in Canada. Afsaneh has extensive experience in both academia and the industry, publishing award-winning papers, and building strong teams solving real-world problems. Afsaneh’s research draws on many subfields of AI, including Computational Linguistics, Cognitive Science, Computational Vision, and Machine Learning. Afsaneh strongly believes that solving many of today’s real-world problems requires an interdisciplinary approach that can bridge the gap between machine intelligence and human cognition.

Before joining Samsung Research, Afsaneh worked at several Canadian companies as Research Director, where she helped build and lead teams of outstanding scientists and engineers solving a diverse set of AI problems. Prior to that, Afsaneh was a Research Scientist and Course Instructor at the University of Toronto, where she also received her PhD from. Afsaneh lives in Toronto, with her husband and two young children. Afsaneh’s main hobby these days is reading and spending time with her family.

Affiliation: Samsung Toronto AI Centre