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Poster
VICE: Variational Interpretable Concept Embeddings
Lukas Muttenthaler · Charles Zheng · Patrick McClure · Robert Vandermeulen · Martin N Hebart · Francisco Pereira

Thu Dec 01 02:00 PM -- 04:00 PM (PST) @ Hall J #116

A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in a triplet odd-one-out task. VICE uses variational inference to obtain sparse, non-negative representations of object concepts with uncertainty estimates for the embedding values. These estimates are used to automatically select the dimensions that best explain the data. We derive a PAC learning bound for VICE that can be used to estimate generalization performance or determine a sufficient sample size for experimental design. VICE rivals or outperforms its predecessor, SPoSE, at predicting human behavior in the triplet odd-one-out task. Furthermore, VICE's object representations are more reproducible and consistent across random initializations, highlighting the unique advantage of using VICE for deriving interpretable embeddings from human behavior.

Author Information

Lukas Muttenthaler (TU Berlin)
Charles Zheng (National Institute of Mental Health)
Patrick McClure (Naval Postgraduate School)
Robert Vandermeulen (TU Berlin)
Martin N Hebart (Max Planck Institute for Human Cognitive and Brain Sciences)
Francisco Pereira (National Institute of Mental Health)

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