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We give a model of how to infer natural language rules by doing experiments.
A behavioral model of rational choice
Herbert A Simon · 1955
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On a measure of the information provided by an experiment
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The structure of scientific revolutions
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Conceptual Change In Childhood
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The scientist in the crib: Minds, brains, and how children learn
Alison Gopnik, Andrew N Meltzoff, and Patricia K Kuhl · 1999
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Detecting blickets: How young children use information about novel causal powers in categorization and induction
Alison Gopnik and David M Sobel · 2000
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Monte Carlo strategies in scientific computing
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What Makes Us Smart? Core Knowledge and Natural Language
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A rational analysis of rule-based concept learning
Noah D Goodman, Joshua B Tenenbaum, Jacob Feldman, and Thomas L Griffiths · 2008
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Active learning literature survey
Burr Settles · 2009
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Rational approximations to rational models: alternative algorithms for category learning
Adam N Sanborn, Thomas L Griffiths, and Daniel J Navarro · 2010
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Where science starts: Spontaneous experiments in preschoolers’ exploratory play
Claire Cook, Noah D. Goodman, and Laura E. Schulz · 2011
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How to grow a mind: Statistics, structure, and abstraction
Joshua B Tenenbaum, Charles Kemp, Thomas L Griffiths, and Noah D Goodman · 2011
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Learning and the language of thought
Steven Thomas Piantadosi · 2011
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Learning dependency-based compositional semantics
Percy Liang, Michael I. Jordan, and Dan Klein · 2011
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The origins of inquiry: Inductive inference and exploration in early childhood
Laura Schulz · 2012
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Machine learning: a probabilistic perspective
Kevin P Murphy · 2012
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Rational use of cognitive resources: Levels of analysis between the computational and the algorithmic
Thomas L. Griffiths, Falk Lieder, and Noah D. Goodman · 2015
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Nested sequential monte carlo methods
Christian Naesseth, Fredrik Lindsten, and Thomas Schon · 2015
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Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
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From sensory signals to modality-independent conceptual representations: A probabilistic language of thought approach
Goker Erdogan, Ilker Yildirim, and Robert A Jacobs · 2015
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Bayesian brains without probabilities
Adam N Sanborn and Nick Chater · 2016
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The logical primitives of thought: Empirical foundations for compositional cognitive models
Steven T Piantadosi, Joshua B Tenenbaum, and Noah D Goodman · 2016
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The language of geometry: Fast comprehension of geometrical primitives and rules in human adults and preschoolers
Controlling the input: How one-year-old infants sustain visual attention
Andres H Mendez, Chen Yu, and Linda B Smith · 2023
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Linlu Qiu, Liwei Jiang, Ximing Lu, Melanie Sclar, Valentina Pyatkin, Chandra Bhagavatula, Bailin Wang, Yoon Kim, Yejin Choi, Nouha Dziri, and Xiang Ren · 2023
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Hypothesis search: Inductive reasoning with language models
Ruocheng Wang, Eric Zelikman, Gabriel Poesia, Yewen Pu, Nick Haber, and Noah D Goodman · 2023
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Human-like few-shot learning via bayesian reasoning over natural language
Kevin Ellis · 2023
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Active inductive inference in children and adults: A constructivist perspective
Neil R Bramley and Fei Xu · 2023
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Grounding compositional hypothesis generation in specific instances
Neil Bramley, Anselm Rothe, Josh Tenenbaum, Fei Xu, and Todd Gureckis · 2018
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Bayesian synthesis of probabilistic programs for automatic data modeling
Feras A Saad, Marco F Cusumano-Towner, Ulrich Schaechtle, Martin C Rinard, and Vikash K Mansinghka · 2019
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On the measure of intelligence, 2019
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Bayesian deep learning and a probabilistic perspective of generalization
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Teaching large language models to self-debug
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Modeling infant object perception as program induction
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Find: A function description benchmark for evaluating interpretability methods
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Eliciting human preferences with language models
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Active preference inference using language models and probabilistic reasoning
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Sequential monte carlo steering of large language models using probabilistic programs
Alexander K Lew, Tan Zhi-Xuan, Gabriel Grand, and Vikash K Mansinghka · 2023
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Large language models as commonsense knowledge for large-scale task planning
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Modern bayesian experimental design
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