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Open AccessDissertation

Learning and the language of thought

TLDR
An inductive statistical model is presented over a compositionally structured representation system, a language of thought (LOT) (Fodor, 1975), that formalizes an optimal Bayesian trade-off between representational complexity and fit to the observed data.
Abstract
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2011.

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Journal Article

The learnability of abstract syntactic principles

TL;DR: This article used a Bayesian framework for grammar induction and showed that an ideal learner could recognize the hierarchical phrase structure of language without having this knowledge innately specified as part of the language faculty.
Posted Content

DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning

TL;DR: DreamCoder is presented, a system that learns to solve problems by writing programs that builds expertise by creating programming languages for expressing domain concepts, together with neural networks to guide the search for programs within these languages.
Journal ArticleDOI

Grammatical morphology as a source of early number word meanings

TL;DR: Although exposure to counting is important to learning number word meanings, hearing number words used outside of these routines—in the quantificational structures of language—may also be highly important in early acquisition.
Journal ArticleDOI

Holistic Reinforcement Learning: The Role of Structure and Attention.

TL;DR: This work proposes an integration of Bayesian cognitive models in which structured knowledge learned via approximate Bayesian inference acts as a source of selective attention, which biases reinforcement learning towards relevant dimensions of the environment.
References
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Journal ArticleDOI

A new look at the statistical model identification

TL;DR: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
Book

Elements of information theory

TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.
Journal ArticleDOI

Estimating the Dimension of a Model

TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.

Estimating the dimension of a model

TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Journal ArticleDOI

Equation of state calculations by fast computing machines

TL;DR: In this article, a modified Monte Carlo integration over configuration space is used to investigate the properties of a two-dimensional rigid-sphere system with a set of interacting individual molecules, and the results are compared to free volume equations of state and a four-term virial coefficient expansion.