www.sciencemag.org/contentsuppl/DC1 Supplementary Material for Human-level concept learning through probabilistic program induction Brenden M. Lake,* Ruslan Salakhutdinov, Joshua B. Tenenbaum *Correspondi

Source URL: www.cs.toronto.edu

Using Physical Theories to Infer Hidden Causal Structure Thomas L. Griffiths Elizabeth R. Baraff & Joshua B. Tenenbaum

Source URL: ccdlab.rutgers.edu

• Causality
• Philosophy of science
• Graphical models
• Statistical inference
• Causal inference
• Causal graph
• Bayesian network
• Causal model
• Determinism
• Inductive reasoning
• Bayesian inference
• Clark Glymour

Infants consider both the sample and the sampling process in inductive generalization Hyowon Gweon, Joshua B. Tenenbaum, and Laura E. Schulz1 Department of Brain and Cognitive Sciences, Massachusetts Institute of Technol

Source URL: eccl.mit.edu

• Statistics
• Knowledge
• Statistical theory
• Statistical inference
• Psychometrics
• Hypothesis testing
• Survey methodology
• Sampling
• Bayesian inference
• Simple random sample
• Bayesian network
• Statistical hypothesis testing

What are you trying to tell me? A Bayesian model of how toddlers can simultaneously infer property extension and sampling processes Hyowon Gweon, Joshua B. Tenenbaum, and Laura E. Schulz (hyora, jbt, ) Dep

Source URL: sll.stanford.edu

• Statistics
• Knowledge
• Statistical theory
• Statistical inference
• Hypothesis testing
• Sampling techniques
• Survey methodology
• Sampling
• Bayesian inference
• Causality
• Bayesian network
• Simple random sample

Language understanding & common sense reasoning Joshua K Hartshorne • Tobias Gerstenberg • Timothy J. O’Donnell • Joshua B. Tenenbaum Massachusetts Institute of Technology Winograd Schema Motivation

Source URL: joshuakhartshorne.org

TOWARDS COMMON-SENSE REASONING VIA CONDITIONAL SIMULATION: LEGACIES OF TURING IN ARTIFICIAL INTELLIGENCE CAMERON E. FREER, DANIEL M. ROY, AND JOSHUA B. TENENBAUM Abstract. The problem of replicating the flexibility of h

Source URL: cfreer.org

Church: a language for generative models Noah D. Goodman, Vikash K. Mansinghka∗, Daniel M. Roy, Keith Bonawitz & Joshua B. Tenenbaum MIT BCS/CSAIL Cambridge, MA 02139

Source URL: stanford.edu

• Software engineering
• Computer programming
• Computing
• Lambda calculus
• Functional programming
• Programming languages
• Data types
• Control flow
• Eval
• Bayesian network
• Scheme
• Lisp

Sparse multidimensional scaling using landmark points Vin de Silva∗ & Joshua B. Tenenbaum† June 30, 2004 Corresponding author: Vin de Silva, Department of Mathematics, Building 380, Stanford

Source URL: pages.pomona.edu

Learning annotated hierarchies from relational data Daniel M. Roy, Charles Kemp, Vikash K. Mansinghka, and Joshua B. Tenenbaum CSAIL, Dept. of Brain & Cognitive Sciences, MIT, Cambridge, MA 02139 {droy, ckemp, vkm, jbt}

Source URL: danroy.org

SEE COMMENTARY The discovery of structural form Charles Kemp*† and Joshua B. Tenenbaum‡ *Department of Psychology, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213; and ‡Department of Brain and

Source URL: www.psy.cmu.edu