C
Charles Kemp
Researcher at University of Melbourne
Publications - 103
Citations - 7765
Charles Kemp is an academic researcher from University of Melbourne. The author has contributed to research in topics: Inference & Inductive reasoning. The author has an hindex of 33, co-authored 99 publications receiving 6855 citations. Previous affiliations of Charles Kemp include Massachusetts Institute of Technology & Carnegie Mellon University.
Papers
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Journal ArticleDOI
How to Grow a Mind: Statistics, Structure, and Abstraction
TL;DR: This review describes recent approaches to reverse-engineering human learning and cognitive development and, in parallel, engineering more humanlike machine learning systems.
Journal ArticleDOI
Theory-based Bayesian models of inductive learning and reasoning
TL;DR: This work argues that both components of induction are necessary to explain the nature, use and acquisition of human knowledge, and introduces a theory-based Bayesian framework for modeling inductive learning and reasoning as statistical inferences over structured knowledge representations.
Proceedings Article
Learning systems of concepts with an infinite relational model
TL;DR: A nonparametric Bayesian model is presented that discovers systems of related concepts and applies the approach to four problems: clustering objects and features, learning ontologies, discovering kinship systems, and discovering structure in political data.
Journal ArticleDOI
The discovery of structural form
Charles Kemp,Joshua B. Tenenbaum +1 more
TL;DR: This work presents a computational model that learns structures of many different forms and that discovers which form is best for a given dataset and brings structure learning methods closer to human abilities and may lead to a deeper computational understanding of cognitive development.
Book ChapterDOI
Bayesian models of cognition
TL;DR: The goal in this work is to illustrate the kinds of computational models of cognition that the authors can build if they assume that human learning and inference approximately follow the principles of Bayesian probabilistic inference.