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A core tension in models of concept learning is that the model must carefully balance the tractability of inference against the expressivity of the hypothesis class.
A behavioral model of rational choice
Herbert A Simon · 1955
Earlier work this paper cites.
A formal theory of inductive inference
Ray J Solomonoff · 1964
Earlier work this paper cites.
Pattern Recognition
M. M. Bongard · 1970
Earlier work this paper cites.
The language of thought
Jerry A Fodor · 1975
Earlier work this paper cites.
Knowledge representation and reasoning
Hector J Levesque · 1986
Earlier work this paper cites.
Inductive logic programming
Stephen Muggleton · 1991
Earlier work this paper cites.
Reflections of the environment in memory
John R Anderson and Lael J Schooler · 1991
Earlier work this paper cites.
Probabilistic constraints and syntactic ambiguity resolution
Maryellen C MacDonald · 1994
Earlier work this paper cites.
The "wake-sleep" algorithm for unsupervised neural networks
Geoffrey E Hinton, Peter Dayan, Brendan J Frey, and Radford M Neal · 1995
Earlier work this paper cites.
Prism: a language for symbolic-statistical modeling
Taisuke Sato and Yoshitaka Kameya · 1997
Earlier work this paper cites.
A Bayesian framework for concept learning
Joshua Brett Tenenbaum · 1999
Earlier work this paper cites.
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David JC MacKay · 2003
Earlier work this paper cites.
Core systems of number
Lisa Feigenson, Stanislas Dehaene, and Elizabeth Spelke · 2004
Earlier work this paper cites.
Predicting good probabilities with supervised learning
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Christopher M. Bishop · 2006
Earlier work this paper cites.
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Matthew Richardson and Pedro Domingos · 2006
Earlier work this paper cites.
Core knowledge
Elizabeth S Spelke and Katherine D Kinzler · 2007
Earlier work this paper cites.
Introduction to statistical relational learning
Lise Getoor and Ben Taskar · 2007
Earlier work this paper cites.
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Earlier work this paper cites.
The discovery of structural form
Charles Kemp and Joshua B Tenenbaum · 2008
Earlier work this paper cites.
Church: a language for generative models
Noah D. Goodman, Vikash K. Mansinghka, Daniel M. Roy, Keith Bonawitz, and Joshua B. Tenenbaum · 2008
Earlier work this paper cites.
A rational analysis of rule-based concept learning
Noah D Goodman, Joshua B Tenenbaum, Jacob Feldman, and Thomas L Griffiths · 2008
Earlier work this paper cites.
Learning programs: A hierarchical bayesian approach
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Earlier work this paper cites.
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David Marr · 2010
Earlier work this paper cites.
How to grow a mind: Statistics, structure, and abstraction
Joshua B Tenenbaum, Charles Kemp, Thomas L Griffiths, and Noah D Goodman · 2011
Earlier work this paper cites.
Learning and the language of thought
Steven Thomas Piantadosi · 2011
Earlier work this paper cites.
Thinking, fast and slow
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Earlier work this paper cites.
Functional specificity for high-level linguistic processing in the human brain
Evelina Fedorenko, Michael K Behr, and Nancy Kanwisher · 2011
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Machine learning: a probabilistic perspective
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Bridging levels of analysis for probabilistic models of cognition
Thomas L Griffiths, Edward Vul, and Adam N Sanborn · 2012
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Kinship categories across languages reflect general communicative principles
Charles Kemp and Terry Regier · 2012
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Pedro Domingos and William Webb · 2012
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Human-level reinforcement learning through theory-based modeling, exploration, and planning
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