Bayesian modeling of human concept learning
NeurIPS'98, 1998. [All Versions]. [Preprint]. This work considers the problem of learning concepts from small numbers of positive examples, a feat which humans perform routinely but which computers are rarely capable of. Bridging machine learning and cognitive science…
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Human-level concept learning through probabilistic program induction science.org
Science, 2015. [All Versions]. [Preprint]. [Supplementary Material]. People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy…
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Rules and Similarity in Concept Learning proceedings.neurips.cc
NeurIPS'99, 1999. [All Versions]. [Preprint]. This paper argues that two apparently distinct modes of generalizing concepts - abstracting rules and computing similarity to exemplars - should both be seen as special cases of a more general Bayesian learning framework. Bayes…
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Generating new concepts with hybrid neuro-symbolic models. arxiv.org
CogSci'20, 2020. [All Versions].
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Word learning as Bayesian inference psycnet.apa.org
Psychological Review, 2007. [All Versions]. [Preprint]. The authors present a Bayesian framework for understanding how adults and children learn the meanings of words. The theory explains how learners can generalize meaningfully from just one or a few positive examples of a…
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Unit Testing for Concepts in Neural Networks aclanthology.org
Transactions of the Association for Computational Linguistics, 2022. [All Versions]. Testing the concept representation by neural networks through Fodor's theory of concepts.
AGI & CoCoSci › Papers > Bayesian Modeling > Bayesian Induction: “NeurIPS'98, 1998. [All Versions]. [Preprint]. This work considers the problem of learning concepts from small numbers of positive examples, a feat which humans perform routinely but which computers are rarely capable of. Bridging machine learning and cognitive science…”