J. Tenenbaum

Joshua B. Tenenbaum, William T. Freeman, J. Tenenbaum, 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

A Rational Analysis of Rule-Based Concept Learning

Thomas L. Griffiths, Joshua B. Tenenbaum, Noah D. Goodman, 2008, Cogn. Sci..

A Comparative Evaluation of Approximate Probabilistic Simulation and Deep Neural Networks as Accounts of Human Physical Scene Understanding pdf

Jiajun Wu, Joshua B. Tenenbaum, William T. Freeman, 2016, CogSci.

Help or Hinder: Bayesian Models of Social Goal Inference

Joshua B. Tenenbaum, Noah D. Goodman, Tomer Ullman, 2009, NIPS.

Theory-based Social Goal Inference

Noah D. Goodman, Chris L. Baker, J. Tenenbaum, 2008 .

Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks pdf

Leslie Pack Kaelbling, Tom Silver, Rohan Chitnis, 2020, AAAI.

Title : The imaginary fundamentalists : The unshocking truth about Bayesian cognitive science

Noah D. Goodman, J. Tenenbaum, T. Griffiths, 2011 .

Bayesian Networks, Bayesian Learning and Cognitive Development

J. Tenenbaum, A. Gopnik, Publishing Blackwell, 2022 .

Mapping a Manifold of Perceptual Observations

Joshua B. Tenenbaum, J. Tenenbaum, 1997, NIPS.

Human Learning in Atari

Joshua B. Tenenbaum, Samuel Gershman, Pedro Tsividis, 2017, AAAI Spring Symposia.

Probabilistic internal physics models guide judgments about object dynamics

Jessica B. Hamrick, Joshua B. Tenenbaum, Peter W. Battaglia, 2011, CogSci.

Differentiable Physics and Stable Modes for Tool-Use and Manipulation Planning

Marc Toussaint, Joshua B. Tenenbaum, Kevin A. Smith, 2018, Robotics: Science and Systems.

Functional neuroanatomy of intuitive physical inference

John G. Mikhael, J. Tenenbaum, N. Kanwisher, 2016, Proceedings of the National Academy of Sciences.

Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints pdf

Marc Toussaint, Joshua B. Tenenbaum, Ilker Yildirim, 2017, CogSci.

Ingredients of intelligence: From classic debates to an engineering roadmap

J. Tenenbaum, B. Lake, T. Ullman, 2017, Behavioral and Brain Sciences.

One and Done? Optimal Decisions From Very Few Samples

Thomas L. Griffiths, Joshua B. Tenenbaum, Noah D. Goodman, 2014, Cogn. Sci..

Feature-based Joint Planning and Norm Learning in Collaborative Games

Joshua B. Tenenbaum, Michael L. Littman, Joseph L. Austerweil, 2016, CogSci.

Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs

Joshua B. Tenenbaum, Yura N. Perov, Vikash K. Mansinghka, 2013, NIPS.

Inferring causal networks from observations and interventions

Joshua B. Tenenbaum, Mark Steyvers, Eric-Jan Wagenmakers, 2003, Cogn. Sci..

Bayesian Policy Search with Policy Priors

Leslie Pack Kaelbling, Joshua B. Tenenbaum, Noah D. Goodman, 2011, IJCAI.

Structure Discovery in Nonparametric Regression through Compositional Kernel Search pdf

Joshua B. Tenenbaum, Zoubin Ghahramani, Roger B. Grosse, 2013, ICML.

The learnability of abstract syntactic principles

J. Tenenbaum, Amy Perfors, T. Regier, 2011, Cognition.

Learning Structured Preferences

Joshua B. Tenenbaum, Owain Evans, Leon Bergen, 2010 .

Probabilistic models of cognition: where next?

J. Tenenbaum, A. Yuille, N. Chater, 2006, Trends in Cognitive Sciences.

Going beyond the evidence: Abstract laws and preschoolers’ responses to anomalous data

Noah D. Goodman, Adrianna C. Jenkins, J. Tenenbaum, 2008, Cognition.

Teacakes, Trains, Taxicabs and Toxins: A Bayesian Account of Predicting the Future

J. Tenenbaum, T. Griffiths, 2000 .

A theory of learning to infer.

Eric Schulz, Joshua B Tenenbaum, Ishita Dasgupta, 2020, Psychological review.

The Rational Basis of Representatives

J. Tenenbaum, T. Griffiths, 2001 .

Learning the Structure of Similarity

Joshua B. Tenenbaum, J. Tenenbaum, 1995, NIPS.

Responsibility judgments in voting scenarios

Joseph Y. Halpern, Joshua B. Tenenbaum, Tobias Gerstenberg, 2015, CogSci.

Visual Object Networks: Image Generation with Disentangled 3D Representations

Jiajun Wu, Joshua B. Tenenbaum, Chengkai Zhang, 2018, NeurIPS.

Reasoning About Physical Interactions with Object-Centric Models

S. Levine, J. Tenenbaum, W. Freeman, 2018 .

Generative Modeling of Audible Shapes for Object Perception

Jiajun Wu, Joshua B. Tenenbaum, William T. Freeman, 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

Blaming humans in autonomous vehicle accidents: Shared responsibility across levels of automation pdf

Iyad Rahwan, Joshua B. Tenenbaum, Sohan Dsouza, 2018, ArXiv.

The Role of Causal Models in Reasoning Under Uncertainty

Joshua B. Tenenbaum, Tevye R. Krynski, J. Tenenbaum, 2003 .

Coordinate to cooperate or compete: Abstract goals and joint intentions in social interaction

Joshua B. Tenenbaum, Michael L. Littman, Joseph L. Austerweil, 2016, CogSci.

Learning a commonsense moral theory

J. Tenenbaum, R. Saxe, Max Kleiman-Weiner, 2017, Cognition.

Discovering Syntactic Hierarchies

Joshua B. Tenenbaum, Daniel M. Roy, Virginia Savova, 2007 .

Learning individual words and learning about words simultaneously

Joshua B. Tenenbaum, Fei Xu, Amy Perfors, 2011, CogSci.

A tutorial introduction to Bayesian models of cognitive development

J. Tenenbaum, Fei Xu, T. Griffiths, 2011, Cognition.

A stochastic programming perspective on nonparametric Bayes

Vikash K. Mansinghka, J. Tenenbaum, D. Roy, 2008 .

Using Vocabulary Knowledge in Bayesian Multinomial Estimation

Thomas L. Griffiths, Joshua B. Tenenbaum, J. Tenenbaum, 2001, NIPS.

Bayesian Modeling of Human Concept Learning

Joshua B. Tenenbaum, J. Tenenbaum, 1998, NIPS.

Neural Radiance Flow for 4D View Synthesis and Video Processing pdf

Jiajun Wu, Joshua B. Tenenbaum, Yilun Du, 2020, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

Deep Audio Priors Emerge From Harmonic Convolutional Networks

Chuang Gan, Jiajun Wu, Joshua B. Tenenbaum, 2020, ICLR.

Learning to learn generative programs with Memoised Wake-Sleep pdf

Joshua B. Tenenbaum, Tuan Anh Le, Luke B. Hewitt, 2020, UAI.

The Isomap Algorithm and Topological Stability

Joshua B. Tenenbaum, J. Tenenbaum, 2002, Science.

Topics in semantic association

J. Tenenbaum, T. Griffiths, 2005 .

Stochastic Digital Circuits for Probabilistic Inference

Joshua B. Tenenbaum, Vikash K. Mansinghka, Eric Jonas, 2008 .

Bayesian Models of Inductive Generalization

Joshua B. Tenenbaum, Neville E. Sanjana, J. Tenenbaum, 2002, NIPS.

Learning to Generalize Intrinsic Images with a Structured Disentangling Autoencoder

Jiajun Wu, Josh Tenenbaum, Tejas D. Kulkarni, 2017, NIPS 2017.

Children's causal inferences from indirect evidence: Backwards blocking and Bayesian reasoning in preschoolers

Alison Gopnik, Joshua B. Tenenbaum, David M. Sobel, 2004, Cogn. Sci..

Deep Reinforcement Learning with Temporal Abstraction and Intrinsic Motivation

Tejas D. Kulkarni, J. Tenenbaum, Karthik Narasimhan, 2016 .

Intuitive Theories

J. Tenenbaum, Tobias Gerstenberg, 2020, Encyclopedia of Creativity, Invention, Innovation and Entrepreneurship.

An integrative computational architecture for object-driven cortex

J. Tenenbaum, N. Kanwisher, Jiajun Wu, 2019, Current Opinion in Neurobiology.

Rapid Physical Predictions from Convolutional Neural Networks

Kevin A. Smith, J. Tenenbaum, Filipe Peres, 2016 .

Intuitive experimentation in the physical world

Neil R. Bramley, J. Tenenbaum, Tobias Gerstenberg, 2018, Cognitive Psychology.

Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning

Kevin A. Smith, Kelsey R. Allen, J. Tenenbaum, 2019, Proceedings of the National Academy of Sciences.

Structure and strength in causal judgments

J. Tenenbaum, T. Griffiths, 2003 .

A critical period for second language acquisition: Evidence from 2/3 million English speakers

Joshua B. Tenenbaum, Steven Pinker, Joshua K. Hartshorne, 2018, Cognition.

Physical object representations for perception and cognition

Max H. Siegel, J. Tenenbaum, Ilker Yildirim, 2018 .

Learning Skill Hierarchies from Predicate Descriptions and Self-Supervision

J. Tenenbaum, L. Kaelbling, Rohan Chitnis, 2019 .

Physical p redictions over t ime

Kevin A. Smith, J. Tenenbaum, 2013 .

Neural Radiance Flow for 4D View Synthesis and Video Processing pdf

Jiajun Wu, Joshua B. Tenenbaum, Yilun Du, 2020, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

M ETA-L EARNING FOR S EMI-S UPERVISED F EW-S HOT

R. Zemel, J. Tenenbaum, H. Larochelle, 2018 .

The Child as Hacker

Joshua S. Rule, J. Tenenbaum, S. Piantadosi, 2020, Trends in Cognitive Sciences.

Faulty Towers : A counterfactual simulation model of physical support

Kevin A. Smith, J. Tenenbaum, Tobias Gerstenberg, 2017 .

COMPOSITIONAL INDUCTIVE BIASES 3 Compositional Inductive Biases in Function Learning

J. Tenenbaum, S. Gershman, Eric Schulz, 2016 .

From mere coincidences to meaningful discoveries q

J. Tenenbaum, T. Griffiths, 2006 .

How Tall Is Tall? Compositionality, Statistics, and Gradable Adjectives

Noah D. Goodman, J. Tenenbaum, Lauren A. Schmidt, 2009 .

DreamCoder: Building interpretable hierarchical knowledge representations with wake-sleep Bayesian program learning

J. Tenenbaum, Armando Solar-Lezama, Kevin Ellis, 2020 .

BAYESIAN SPECIAL SECTION Learning overhypotheses with hierarchical Bayesian models

J. Tenenbaum, Charles Kemp, Amy Perfors, 2007 .

Modeling the Acquisition of Domain Structure and Feature Understanding

J. Tenenbaum, Charles Kemp, Amy Perfors, 2005 .

Eye-Tracking Causality

Noah D. Goodman, J. Tenenbaum, Tobias Gerstenberg, 2017, Psychological science.

Inference and Planning with Virtual and Physical Constraints for Object Manipulation

Kelsey R. Allen, J. Tenenbaum, J. Loula, 2020 .

Bayesian Theory of Mind

Chris L. Baker, J. Tenenbaum, R. Saxe, 2010 .

Running head : UNDER REVIEW 1 Intuitive Experiments in the Physical World Neil

J. Tenenbaum, Tobias Gerstenberg, T. Gureckis, 2017 .

Running head: ABSTRACT CAUSAL KNOWLEDGE The acquisition and use of abstract causal knowledge

Noah D. Goodman, J. Tenenbaum, 2008 .

BAYESIAN SPECIAL SECTION: INTRODUCTION Bayesian networks, Bayesian learning and cognitive development

J. Tenenbaum, A. Gopnik, 2007 .

Learning Grounded Causal Models

Noah D. Goodman, Vikash K. Mansinghka, J. Tenenbaum, 2007 .

Probabilistic Models of Cogntion: Conceptual Foundations

J. Tenenbaum, A. Yuille, N. Chater, 2006 .

Structure and strength 1 Running head : STRUCTURE AND STRENGTH Structure and strength in causal induction

J. Tenenbaum, T. Griffiths, 2005 .

Structure and strength in causal induction q

J. Tenenbaum, T. Griffiths, 2005 .

Elemental causal induction 1 Running head: ELEMENTAL CAUSAL INDUCTION Elemental causal induction

J. Tenenbaum, T. Griffiths, 2004 .

ynamic Causal Learning

J. Tenenbaum, T. Griffiths, D. Danks, 2002 .

Probabilistic Models of Physical Reasoning

Jessica B. Hamrick, Kevin A. Smith, Adam N. Sanborn, 2020 .

Reasoning About Physical Interactions with Object-Oriented Prediction and Planning pdf

Sergey Levine, Jiajun Wu, Joshua B. Tenenbaum, 2018, ICLR.

Visual Grounding of Learned Physical Models

Daniel L. K. Yamins, J. Tenenbaum, A. Torralba, 2020, ICML.

Drivers are blamed more than their automated cars when both make mistakes

J. Tenenbaum, Max Kleiman-Weiner, E. Awad, 2019, Nature Human Behaviour.

End-to-End Differentiable Physics for Learning and Control

Joshua B. Tenenbaum, Kevin A. Smith, J. Zico Kolter, 2018, NeurIPS.

Understanding as Inverse Planning Appendix

Chris L. Baker, J. Tenenbaum, R. Saxe, 2009 .

Unsupervised Learning of Overlapping Concepts

J. Tenenbaum, 2007 .

On the Units of GANs

J. Tenenbaum, A. Torralba, W. Freeman, 2020 .

Categorization as Causal Explanation

Daniel M. Oppenheimer, Tevye R. Krynski, J. Tenenbaum, 2013 .

Designing Perceptual Puzzles by Differentiating Probabilistic Programs

J. Tenenbaum, Jonathan Ragan-Kelley, Tzu-Mao Li, 2022, SIGGRAPH.

TOWARD HUMAN-LIKE OBJECT NAMING IN ARTIFICIAL NEURAL SYSTEMS

J. Tenenbaum, Noga Zaslavsky, R. Levy, 2020 .

Efficient Bayesian Inference for Nested Simulators

Vikash K. Mansinghka, J. Tenenbaum, 2019 .

3D SHAPE PROGRAMS

J. Tenenbaum, W. Freeman, Yonglong Tian, 2019 .

Engine s " s " # $ Learned Physics Engine s " s " # $ Learned Physics Engine s " s " # $ Learned Physics Engine s "

J. Tenenbaum, L. Kaelbling, Maria Bauzá, 2018 .

Intrinsic Exploration as Empowerment in a Richly Structured Online Game

J. Tenenbaum, S. Gershman, Eric Schulz, 2022 .

Computational Models of Intuitive Physics - eScholarship

Kenneth D. Forbus, Adam N. Sanborn, J. Tenenbaum, 2012 .

The causes and consequences explicit in verbs

Joshua K. Hartshorne, Joshua B Tenenbaum, Timothy J O'Donnell, 2015, Language, cognition and neuroscience.

Running head: RAPID TRIAL-AND-ERROR LEARNING SUPPORTS TOOL USE 1 Rapid trial-and-error learning with mental simulation supports flexible tool use

Kevin A. Smith, Kelsey R. Allen, J. Tenenbaum, 2019 .

Learning constraint-based planning models from demonstrations

Kelsey R. Allen, J. Tenenbaum, Tom Silver, 2020, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Learning to Infer Program Sketches pdf

Armando Solar-Lezama, Joshua B. Tenenbaum, Luke B. Hewitt, 2019, ICML.

Lucky or clever? From expectations to responsibility judgments

Joshua B. Tenenbaum, David A. Lagnado, Max Kleiman-Weiner, 2018, Cognition.

Changing minds: Children's inferences about third party belief revision

Max H. Siegel, J. Tenenbaum, L. Schulz, 2017, Developmental science.

Inferring the intentional states of autonomous virtual agents

Joshua B. Tenenbaum, Ari Weinstein, Steven A. Cholewiak, 2014, Cognition.

Xt-1 Yt-1 R Ot-1 Bt-1 At-1 Xt Yt Ot Bt At

Chris L. Baker, J. Tenenbaum, 2011 .

Developmental and computational perspectives on infant social cognition

Christopher G. Lucas, Noah D. Goodman, J. Tenenbaum, 2010 .

Lucky or clever ? From changed expectations to responsibility judgments

J. Tenenbaum, Tobias Gerstenberg, Max Kleiman-Weiner, 2017 .

Inductive reasoning about causally transmitted properties

Joshua B. Tenenbaum, Charles Kemp, Patrick Shafto, 2008, Cognition.

Inductive Reasoning: Theory-Based Bayesian Models of Inductive Reasoning

Joshua B. Tenenbaum, Charles Kemp, Patrick Shafto, 2007 .

AClass: A simple, online, parallelizable algorithm for probabilistic classification

Joshua B. Tenenbaum, Daniel M. Roy, Vikash K. Mansinghka, 2007, AISTATS.

THEORY-BASED BAYESIAN MODELS OF INDUCTIVE INFERENCE Final Technical Report

J. Tenenbaum, MtKUMI I YKt, KMUJtU I NUMBtM, 2012 .

The Infinite Latent Events Model Citation

Noah D. Goodman, Daniel M. Roy, J. Tenenbaum, 2009 .

Supplement to Infinite Mixture Prototypes for Few-Shot Learning

Kelsey R. Allen, J. Tenenbaum, Evan Shelhamer, 2019 .

Title Probabilistic Models of Cognition : Where Next ?

J. Tenenbaum, A. Yuille, N. Chater, 2006 .

Theory Learning and Logical Rule Induction with Neural Theorem Proving

J. Tenenbaum, S. Riedel, Tim Klinger, 2018 .

Formalizing accessibility using linguistic productivity

J. Tenenbaum, Kyle Mahowald, T. O’Donnell, 2017 .

Adventures in Flatland: Perceiving Social Interactions Under Physical Dynamics

J. Tenenbaum, T. Ullman, Tianmin Shu, 2020, CogSci.

Modeling Parts, Structure, and System Dynamics via Predictive Learning

J. Tenenbaum, K. Murphy, Jiajun Wu, 2019 .

The effects of object motion observations on physical prediction

Joshua B. Tenenbaum, Kevin A. Smith, Moyuru Yamada, 2019, CogSci.

Tracking hidden objects with efficient physical prediction

Joshua B. Tenenbaum, Edward Vul, Kevin A. Smith, 2014 .

DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs

Yunbo Wang, Fei-Fei Li, Yuke Zhu, 2020, IJCAI.

Learning Overhypotheses

J. Tenenbaum, Charles Kemp, Amy Perfors, 2006 .

Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction

J. Tenenbaum, A. Torralba, L. Guibas, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Children Understand That Agents Maximize Expected Utilities

J. Tenenbaum, L. Schulz, J. Jara-Ettinger, 2017, Journal of experimental psychology. General.

Nonsense and Sensibility: Inferring Unseen Possibilities

J. Tenenbaum, Charles Kemp, Lauren A. Schmidt, 2006 .

Why blame Bob ? Probabilistic generative models , counterfactual reasoning , and blame attribution

J. Tenenbaum, Tobias Gerstenberg, T. Ullman, 2012 .

Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning

Joshua B. Tenenbaum, Kevin A. Smith, Kelsey R. Allen, 2019, Proceedings of the National Academy of Sciences.

The brain produces mind by modeling

J. Tenenbaum, R. Shiffrin, D. Bassett, 2020, Proceedings of the National Academy of Sciences.

The neural architecture of language: Integrative reverse-engineering converges on a model for predictive processing

Nancy Kanwisher, Carina Kauf, Martin Schrimpf, 2020 .

ComPhy: Compositional Physical Reasoning of Objects and Events from Videos

J. Tenenbaum, A. Torralba, Yunzhu Li, 2022, ICLR.

Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics

J. Tenenbaum, Tao Du, Hao Su, 2022, ICLR.

Spatial Attention and Temporal Expectation Under Timed Uncertainty Predictably Modulate Neuronal Responses in Monkey V1.

J. Tenenbaum, M. Sur, J. Schummers, 2015, Cerebral cortex.

Leveraging Language to Learn Program Abstractions and Search Heuristics

J. Tenenbaum, Jacob Andreas, Kevin Ellis, 2021, ICML.

Author ' s personal copy Inductive reasoning about causally transmitted properties q

J. Tenenbaum, Charles Kemp, Patrick Shafto, 1997 .

Quantifying availability through linguistic productivity

J. Tenenbaum, Kyle Mahowald, Timothy J. O’Donnell, 2014 .

Variadic Learning by Bayesian Nonparametric Deep Embedding

Kelsey R. Allen, Josh Tenenbaum, Evan Shelhamer, 2018 .

Identifying concept libraries from language about object structure

Robert D. Hawkins, Judith E. Fan, J. Tenenbaum, 2022, ArXiv.

Choosing the unimaginable: Social psychological factors in seeking transformative experiences

Joshua B. Tenenbaum, Marta Kryven, Laura Niemi, 2019, CogSci.

The Cognitive Mechanisms of Contractualist Moral Decision-Making

Joshua B. Tenenbaum, Fiery Cushman, Nicholas Chater, 2018, CogSci.

Predicting object and scene descriptions with an information-theoretic model of pragmatics

Joshua B. Tenenbaum, Antonio Torralba, Aude Oliva, 2010 .

Real-time inference of physical properties in dynamic scenes

Jiajun Wu, Joshua B. Tenenbaum, Kevin A. Smith, 2019, CogSci.

Unsupervised Segmentation in Real-World Images via Spelke Object Inference

Daniel L. K. Yamins, J. Tenenbaum, Jiajun Wu, 2022, 2205.08515.

Hierarchical Drift-Diffusion Model for Moral Dilemma: Understanding Reaction Times and Choices

Iyad Rahwan, Joshua B. Tenenbaum, Sohan Dsouza, 2018, CogSci.

Predicting the future as Bayesian inference: people combine prior knowledge with observations when estimating duration and extent.

Thomas L. Griffiths, Joshua B. Tenenbaum, J. Tenenbaum, 2011, Journal of experimental psychology. General.

What if everybody did that?: Universalization as a mechanism of moral decision-making

Joshua B. Tenenbaum, Max Kleiman-Weiner, Sydney Levine, 2019, CogSci.

Causal Structure in Conditional Reasoning

Tevye R. Krynski, J. Tenenbaum, 2004 .

Planning with Diffusion for Flexible Behavior Synthesis

S. Levine, J. Tenenbaum, Michael Janner, 2022, 2205.09991.

Visualizing and Understanding GANs

Bolei Zhou, Joshua B. Tenenbaum, Antonio Torralba, 2019, DGS@ICLR.

Efficient analysis-by-synthesis in vision: A computational framework, behavioral tests, and modeling neuronal representations

Joshua B. Tenenbaum, Tejas D. Kulkarni, Ilker Yildirim, 2015, Annual Meeting of the Cognitive Science Society.

34 Physical Object Repre sen ta tions for Perception and Cognition

Max H. Siegel, J. Tenenbaum, Ilker Yildirim, 2021 .

Predicting responsibility judgments from dispositional inferences and causal attributions

J. Tenenbaum, Tobias Gerstenberg, J. Halpern, 2019, Cognitive Psychology.

RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation

J. Tenenbaum, W. Matusik, Tao Du, 2022, ICLR.

Learning Neuro-Symbolic Skills for Bilevel Planning

J. Tenenbaum, L. Kaelbling, Tomas Lozano-Perez, 2022, ArXiv.

Bayesian Models of Conceptual Development: Learning as Building Models of the World

J. Tenenbaum, T. Ullman, 2020, Annual Review of Developmental Psychology.

Drawing out of Distribution with Neuro-Symbolic Generative Models

Tuan Anh Le, J. Tenenbaum, N. Siddharth, 2022, ArXiv.

PsychocompLA-2007 Psychocomputational Models of Human Language Acquisition

Charles D. Yang, J. Tenenbaum, P. Niyogi, 2007 .

Prompting Decision Transformer for Few-Shot Policy Generalization

J. Tenenbaum, Yuchen Lu, Shun Zhang, 2022, ICML.

Finding Fallen Objects Via Asynchronous Audio-Visual Integration

Josh H. McDermott, J. Tenenbaum, A. Torralba, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Learning Neuro-Symbolic Relational Transition Models for Bilevel Planning

J. Tenenbaum, L. Kaelbling, Tomas Lozano-Perez, 2021, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

CZ-GEM: A FRAMEWORK FOR DISENTANGLED REPRESENTATION LEARNING

Bernhard Egger, David D. Cox, Prasanna Sattigeri, 2019 .

Integrating physical reasoning and visual object recognition for fully occluded scene interpretation

Joshua B. Tenenbaum, Ilker Yildirim, Max H. Siegel, 2016, CogSci.

Emotion attributions echo the structure of people's intuitive theory of psychology

Joshua B. Tenenbaum, Rebecca Saxe, Max Kleiman-Weiner, 2019, CogSci.

Intuitive physics judgments guided by probabilistic dynamics model

Jessica B. Hamrick, Joshua B. Tenenbaum, Peter W. Battaglia, 2013 .

Inferring mass in complex physical scenes via probabilistic simulation

Thomas L. Griffiths, Jessica B. Hamrick, Joshua B. Tenenbaum, 2013, CogSci.

Look out, it's going to fall!: Does physical instability capture attention and lead to distraction?

Joshua B. Tenenbaum, Marta Kryven, Sholei Croom, 2019, CogSci.

Physical Inference for Object Perception in Complex Auditory Scenes

Joshua B. Tenenbaum, Josh H. McDermott, Max H. Siegel, 2018, Annual Meeting of the Cognitive Science Society.

Query-guided visual search

Roger Levy, Joshua B. Tenenbaum, Junyi Chu, 2019, CogSci.

Discovering a symbolic planning language from continuous experience

Joshua B. Tenenbaum, Tom Silver, Kelsey R. Allen, 2019, CogSci.

Intuitive mechanics in visual reasoning about complex scenes with unknown forces

Jessica B. Hamrick, Joshua B. Tenenbaum, Peter W. Battaglia, 2012 .

How recursive is language? A Bayesian exploration

Joshua B. Tenenbaum, Amy Perfors, Edward Gibson, 2010 .

From Amortised to Memoised Inference: Combining Wake-Sleep and Variational-Bayes for Unsupervised Few-Shot Program Learning

Joshua B. Tenenbaum, Luke B. Hewitt, Luke Hewitt, 2018 .

Neural representation of the intuitive physical dimension of mass

Nancy Kanwisher, Jason Fischer, Joshua B. Tenenbaum, 2018, Journal of Vision.

Fundamental Theory and Parallel Inference for Probabilistic Programming (10.3.1 Integrated Intelligence)

Joshua B. Tenenbaum, Vikash K. Mansinghka, J. Tenenbaum, 2017 .

Learning to Describe Scenes with Programs

Jiajun Wu, Joshua B. Tenenbaum, William T. Freeman, 2018, ICLR.

Children's sensitivity to informant's inductive efficiency and learner's epistemic states in pedagogical contexts

Joshua B. Tenenbaum, Patrick Shafto, Laura Schulz, 2012, CogSci.

Simplicity and Probability in Human Judgment

Jonathan S. Rosenfeld, Joshua B. Tenenbaum, Junyi Chu, 2019, CogSci.

Modeling the Perception of Intentions

Frank E. Pollick, David Pautler, Joshua B. Tenenbaum, 2012, Annual Meeting of the Cognitive Science Society.

Compositionality in rational analysis

Thomas L. Griffiths, Joshua B. Tenenbaum, Noah D. Goodman, 2008 .

The Evolution of Cooperation in Cognitively Flexible Agents

David G. Rand, David Rand, Joshua B. Tenenbaum, 2018, CogSci.

Plans or Outcomes: How Do We Attribute Intelligence to Others?

Joshua B. Tenenbaum, Marta Kryven, Tomer D. Ullman, 2021, Cogn. Sci..

Learning a novel rule-based conceptual system

Joshua B. Tenenbaum, Steve Piantadosi, Joshua Rule, 2019, CogSci.

Physics knowledge aids object perception in dynamic scenes

Jessica B. Hamrick, Joshua B. Tenenbaum, Peter W. Battaglia, 2012 .

Inductive Biases in the Evolution of Combinatorial Structure in Language

Roger Levy, Joshua B. Tenenbaum, Matthias Hofer, 2018, CogSci.

Heuristics, hacks, and habits: Boundedly optimal approaches to learning, reasoning and decision making

Jessica B. Hamrick, Joshua B. Tenenbaum, Eric Schulz, 2019, CogSci.

Optimal Predictions in Everyday

Joshua B. Tenenbaum, Cognition L. Griffiths, J. Tenenbaum, 2006 .

Evidence for an Intuitive Physics Engine in the Human Brain

Nancy Kanwisher, Jason Fischer, Joshua B. Tenenbaum, 2018, CogSci.

Author response: Invariant representations of mass in the human brain

Nancy Kanwisher, Joshua B. Tenenbaum, Sarah Schwettmann, 2019 .

Formal Approaches in Categorization: Nonparametric Bayesian models of categorization

Thomas L. Griffiths, Joshua B. Tenenbaum, Kevin Robert Canini, 2011 .

PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning pdf

Joshua B. Tenenbaum, Antonio Torralba, Yining Hong, 2021, NeurIPS.

C ONTACT P OINTS D ISCOVERY FOR S OFT -B ODY M A NIPULATIONS WITH D IFFERENTIABLE P HYSICS

J. Tenenbaum, Zhiao Huang, Sizhe Li, 2022 .

Strategies and representations in physical inference

Jessica B. Hamrick, Lance J. Rips, Joshua B. Tenenbaum, 2018, Annual Meeting of the Cognitive Science Society.

Learning sequential patterns from graphical programs

Joshua B. Tenenbaum, Eric Schulz, Anselm Rothe, 2020, CogSci.

Concepts are not beliefs, but having concepts is having beliefs

Joshua B. Tenenbaum, Fei Xu, Cristina M. Sorrentino, 1998, Behavioral and Brain Sciences.

Modeling Expertise with Neurally-Guided Bayesian Program Induction

Joshua B. Tenenbaum, Mathias Sablé-Meyer, Catherine Wong, 2019, CogSci.

DreamCoder: growing generalizable, interpretable knowledge with wake–sleep Bayesian program learning pdf

Armando Solar-Lezama, Joshua B. Tenenbaum, Catherine Wong, 2020, Philosophical Transactions of the Royal Society A.

Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning tasks

J. Tenenbaum, K. M. Collins, Catherine Wong, 2022, ArXiv.

DreamCoder software and data

Joshua B. Tenenbaum, Mathias Sablé-Meyer, Kevin Ellis, 2021, Artifact Digital Object Group.

When Is It Morally Acceptable to Break the Rules? A Preference-Based Approach

J. Tenenbaum, Kartik Talamadupula, Max Kleiman-Weiner, 2020 .

Computational ethics Trends in Cognitive Sciences

Jana Schaich Borg, Julian C. Jamison, J. Tenenbaum, 2022 .

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Gerard de Melo, Andrew M. Dai, Christopher D. Manning, 2022, ArXiv.

Running head : INTUITIVE PHYSICAL EXPERIMENTATION 1 Intuitive Experimentation in the Physical World

Neil R. Bramley, J. Tenenbaum, Tobias Gerstenberg, 2018 .

Learning Iterative Reasoning through Energy Minimization

J. Tenenbaum, Igor Mordatch, Yilun Du, 2022, ICML.

Seeing 3D Objects in a Single Image via Self-Supervised Static-Dynamic Disentanglement

J. Tenenbaum, W. Freeman, F. Durand, 2022, ArXiv.

Learning Symbolic Operators for Task and Motion Planning

Leslie Pack Kaelbling, Tom Silver, Rohan Chitnis, 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Compositional Visual Generation with Composable Diffusion Models

J. Tenenbaum, A. Torralba, Yilun Du, 2022, ECCV.

Logic, Probability, and Pragmatics in Syllogistic Reasoning

Noah D. Goodman, Michael Henry Tessler, J. Tenenbaum, 2022, Top. Cogn. Sci..

Graph approximations to geodesics on embedded manifolds

Vin de Silva, J. Tenenbaum, J. Langford, 2000 .

Unsupervised Segmentation in Real-World Images via Spelke Object Inference

Daniel L. K. Yamins, J. Tenenbaum, Jiajun Wu, 2022, ECCV.

A formal model of number word acquisition

Noah D. Goodman, J. Tenenbaum, S. Piantadosi, 2009 .

Look, Listen, and Act: Towards Audio-Visual Embodied Navigation

J. Tenenbaum, Boqing Gong, Jiajun Wu, 2019, 2020 IEEE International Conference on Robotics and Automation (ICRA).

W ATCH -A ND -H ELP : A C HALLENGE FOR S OCIAL P ER CEPTION AND H UMAN -AI C OLLABORATION

J. Tenenbaum, A. Torralba, S. Fidler, 2021 .

Abstract Interpretation for Generalized Heuristic Search in Model-Based Planning

Vikash K. Mansinghka, J. Tenenbaum, Tan Zhi-Xuan, 2022, ArXiv.

Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of Mind

Vikash K. Mansinghka, J. Tenenbaum, Nishad Gothoskar, 2022, ArXiv.

Synthesizing theories of human language with Bayesian program induction

J. Tenenbaum, Armando Solar-Lezama, Adam Albright, 2022, Nature Communications.

The neural architecture of theory-based reinforcement learning

Momchil S. Tomov, J. Tenenbaum, S. Gershman, 2022, Neuron.

Foundations of intuitive power analyses in children and adults

Kelsey R. Allen, J. Tenenbaum, L. Schulz, 2022, Nature Human Behaviour.

Computational Ethics

Jana Schaich Borg, Julian C. Jamison, J. Tenenbaum, 2022, HMD Praxis der Wirtschaftsinformatik.

Learning Neuro-Symbolic Skills for Bilevel Planning

J. Tenenbaum, L. Kaelbling, Tomas Lozano-Perez, 2022, ArXiv.

Mental Jenga: A counterfactual simulation model of physical support

Kevin A. Smith, J. Tenenbaum, Tobias Gerstenberg, 2022 .

Learning Iterative Reasoning through Energy Minimization Iterative Reasoning through Energy Minimization

J. Tenenbaum, Igor Mordatch, Yilun Du, 2022 .

Physion: Evaluating Physical Prediction from Vision in Humans and Machines

Kevin A. Smith, Judith E. Fan, Hsiau-Yu Fish Tung, 2021, NeurIPS Datasets and Benchmarks.

Unsupervised Discovery and Composition of Object Light Fields

J. Tenenbaum, F. Durand, Jiajun Wu, 2022, ArXiv.

Communicating Natural Programs to Humans and Machines

Michael Henry Tessler, J. Tenenbaum, Maxwell Nye, 2021, NeurIPS.

Keep it Simple : Language Acquisition Without Complex Bayesian Models

J. Tenenbaum, 2014 .

Retrospectives on the Embodied AI Workshop

Angel X. Chang, Devendra Singh Chaplot, Ali Farhadi, 2022, ArXiv.

Learning Neuro-Symbolic Skills for Bilevel Planning

J. Tenenbaum, L. Kaelbling, Tomas Lozano-Perez, 2022, CoRL.

3D Concept Grounding on Neural Fields

J. Tenenbaum, Yilun Du, Chuang Gan, 2022, NeurIPS.

Dangerous Ground: One-Year-Old Infants are Sensitive to Peril in Other Agents’ Action Plans

J. Tenenbaum, E. Spelke, T. Ullman, 2022, Open Mind.

Composing Ensembles of Pre-trained Models via Iterative Consensus

J. Tenenbaum, A. Torralba, Igor Mordatch, 2022, ICLR.

Learning Physical Dynamics with Subequivariant Graph Neural Networks

J. Tenenbaum, Jiachen Li, Hengbo Ma, 2022, NeurIPS.

What Would It Mean for a Machine to Have a Self?

J. Tenenbaum, T. Ullman, Julian De Freitas, 2022, SSRN Electronic Journal.

Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning

J. Tenenbaum, Pulkit Agrawal, Tianmin Shu, 2022, ICML.

Causal inference in Cue Combination

Ulrik R Beierholm, J. Tenenbaum, S. Quartz, 2007 .

A language of thought for the mental representation of geometric shapes

J. Tenenbaum, S. Dehaene, Kevin Ellis, 2022, Cognitive Psychology.

When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment

B. Schölkopf, J. Tenenbaum, Maarten Sap, 2022, NeurIPS.

Predicate Invention for Bilevel Planning

J. Tenenbaum, L. Kaelbling, Tomas Lozano-Perez, 2022, AAAI.

Top-Down Synthesis for Library Learning

J. Tenenbaum, Armando Solar-Lezama, Kevin Ellis, 2022, ArXiv.

Is Conditional Generative Modeling all you need for Decision-Making?

J. Tenenbaum, T. Jaakkola, Pulkit Agrawal, 2022, ArXiv.

Compositional Visual Generation with Composable Diffusion Models

J. Tenenbaum, A. Torralba, Yilun Du, 2022, ECCV.

On the Complexity of Bayesian Generalization

J. Tenenbaum, J. Hopcroft, Y. Wu, 2022, ICML.

NOPA: Neurally-guided Online Probabilistic Assistance for Building Socially Intelligent Home Assistants

J. Tenenbaum, A. Torralba, Xavier Puig, 2023, IEEE International Conference on Robotics and Automation.

Are Deep Neural Networks SMARTer than Second Graders?

Kevin A. Smith, J. Tenenbaum, A. Cherian, 2022, ArXiv.

Unsupervised Learning of Curved Manifolds

Vin de Silva, J. Tenenbaum, 2003 .

The Large-Scale Structure of Semantic Networks

Mark Steyvers, Josh Tenenbaum, J. Tenenbaum, 2005 .

Flexible social inference facilitates targeted social learning when rewards are not observable

Noah D. Goodman, Robert D. Hawkins, J. Tenenbaum, 2022, ArXiv.

What are you trying to tell me? A Bayesian model of how toddlers can simultaneously infer property extension and sampling processes

J. Tenenbaum, L. Schulz, Hyowon Gweon, 2009 .

Structured Priors for Structure Learning

Vikash K. Mansinghka, J. Tenenbaum, T. Griffiths, 2006, UAI.

Nonsense and Sensibility: Inferring Unseen Possibilities

J. Tenenbaum, Charles Kemp, Lauren A. Schmidt, 2006 .

The fine structure of surprise in intuitive physics: when, why, and how much?

Kevin A. Smith, J. Tenenbaum, E. Spelke, 2020, CogSci.

Learning and using relational theories : supporting material

Noah D. Goodman, J. Tenenbaum, Charles Kemp, 2007 .

Dissociating language and thought in large language models: a cognitive perspective

Anna A. Ivanova, J. Tenenbaum, N. Kanwisher, 2023, ArXiv.

ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation

Josh H. McDermott, Daniel L. K. Yamins, Julian De Freitas, 2020, NeurIPS Datasets and Benchmarks.

Learning Neural Acoustic Fields

M. Tarr, J. Tenenbaum, A. Torralba, 2022, NeurIPS.

3D Shape Perception Integrates Intuitive Physics and Analysis-by-Synthesis

Max H. Siegel, Amir Arsalan Soltani, J. Tenenbaum, 2023, ArXiv.

Three ideal observer models for rule learning in simple languages

Michael C. Frank, Joshua B. Tenenbaum, J. Tenenbaum, 2011, Cognition.

Modeling human performance in statistical word segmentation

Michael C. Frank, Thomas L. Griffiths, Joshua B. Tenenbaum, 2010, Cognition.

Principles of Generalization for Learning Sequential Structure in Language

Michael C. Frank, Joshua B. Tenenbaum, Micheal C. Frank, 2008 .

Towards common-sense reasoning via conditional simulation: legacies of Turing in Artificial Intelligence pdf

Joshua B. Tenenbaum, Daniel M. Roy, Cameron E. Freer, 2012, Turing's Legacy.

Finding Friend and Foe in Multi-Agent Games

Joshua B. Tenenbaum, David C. Parkes, Max Kleiman-Weiner, 2019, NeurIPS.

Theory of Minds: Understanding Behavior in Groups Through Inverse Planning

Joshua B. Tenenbaum, Michael L. Littman, Max Kleiman-Weiner, 2019, AAAI.

Sticking to the Evidence? A Behavioral and Computational Case Study of Micro-Theory Change in the Domain of Magnetism

Joshua B Tenenbaum, Elizabeth Bonawitz, Alison Gopnik, 2019, Cogn. Sci..

Computational rationality: A converging paradigm for intelligence in brains, minds, and machines

Samuel J. Gershman, Joshua B. Tenenbaum, Eric J. Horvitz, 2015, Science.

World model learning and inference

Yukie Nagai, Hiroaki Gomi, Tadahiro Taniguchi, 2021, Neural Networks.

Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense pdf

Joshua B. Tenenbaum, Song-Chun Zhu, Tao Gao, 2020, Engineering.

Physical Primitive Decomposition

Zhijian Liu, Jiajun Wu, Joshua B. Tenenbaum, 2018, ECCV.

Theory-Based Causal Inference

Thomas L. Griffiths, Joshua B. Tenenbaum, J. Tenenbaum, 2002, NIPS.

The neural architecture of language: Integrative modeling converges on predictive processing

Eghbal A. Hosseini, J. Tenenbaum, N. Kanwisher, 2020, Proceedings of the National Academy of Sciences.

Artificial Neural Networks Accurately Predict Language Processing in the Brain

Nancy Kanwisher, Carina Kauf, Martin Schrimpf, 2020 .

V1 Neurons Signal Acquisition of an Internal Representation of Stimulus Location

Joshua B Tenenbaum, Mriganka Sur, Jitendra Sharma, 2003, Science.

The logic of universalization guides moral judgment

Laura Schulz, Fiery Cushman, Joshua Tenenbaum, 2020, Proceedings of the National Academy of Sciences.

Learning to Learn Causal Models

Joshua B. Tenenbaum, Charles Kemp, Noah D. Goodman, 2010, Cogn. Sci..

A probabilistic model of cross-categorization

Joshua B. Tenenbaum, Charles Kemp, Vikash K. Mansinghka, 2011, Cognition.

The discovery of structural form

Charles Kemp, Joshua B Tenenbaum, J. Tenenbaum, 2008, Proceedings of the National Academy of Sciences.

Bayesian models of cognition

Charles Kemp, Thomas L. Griffiths, Joshua B. Tenenbaum, 2008 .

Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation pdf

Vincent Sitzmann, Joshua B. Tenenbaum, Andrea Tagliasacchi, 2021, 2022 International Conference on Robotics and Automation (ICRA).

Bayes and Darwin: How replicator populations implement Bayesian computations

J. Tenenbaum, E. Szathmáry, Hamza Giaffar, 2022, BioEssays : news and reviews in molecular, cellular and developmental biology.

The Omniglot challenge: a 3-year progress report

Joshua B. Tenenbaum, Ruslan Salakhutdinov, Brenden M. Lake, 2019, Current Opinion in Behavioral Sciences.

The Structure and Dynamics of Scientific Theories: A Hierarchical Bayesian Perspective*

Joshua B. Tenenbaum, Noah D. Goodman, Leah Henderson, 2010, Philosophy of Science.

Learnability of syntax 1 Running head : LEARNABILITY OF SYNTAX The learnability of abstract syntactic principles

J. Tenenbaum, A. Perfors, 2008 .

The acquisition of inductive constraints

Joshua B. Tenenbaum, Charles Kemp, J. Tenenbaum, 2008 .

Discovering hierarchical motion structure

Samuel J. Gershman, Frank Jäkel, Joshua B. Tenenbaum, 2016, Vision Research.

Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations

Shunyu Yao, Kevin A. Smith, Lingjie Mei, 2019, NeurIPS.

Structured Representations of Utility in Combinatorial Domains

Samuel J. Gershman, Joshua B. Tenenbaum, Jonathan Malmaud, 2017 .

Online learning of symbolic concepts

Samuel J. Gershman, Joshua B. Tenenbaum, Pratiksha Thaker, 2017 .

Indirect Evidence and the Poverty of the Stimulus: The Case of Anaphoric One

Joshua B. Tenenbaum, Amy Perfors, Terry Regier, 2009, Cogn. Sci..

Productivity and Reuse in Language

Joshua B. Tenenbaum, Noah D. Goodman, Timothy O'Donnell, 2011, CogSci.

Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need? pdf

Yue Wang, Joshua B. Tenenbaum, Phillip Isola, 2020, ECCV.

Meta-Learning for Semi-Supervised Few-Shot Classification pdf

Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel, 2018, ICLR.

OPEn: An Open-ended Physics Environment for Learning Without a Task

Joshua B. Tenenbaum, Antonio Torralba, Phillip Isola, 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

AGENT: A Benchmark for Core Psychological Reasoning

Chuang Gan, Joshua B. Tenenbaum, Kevin A. Smith, 2021, ICML.

CLEVRER: CoLlision Events for Video REpresentation and Reasoning

J. Tenenbaum, A. Torralba, Pushmeet Kohli, 2019, ICLR.

OGRE: An Object-based Generalization for Reasoning Environment

Kevin A. Smith, Kelsey R. Allen, L. V. D. Maaten, 2020 .

Noisy Newtons: Unifying process and dependency accounts of causal attribution

Joshua B. Tenenbaum, David A. Lagnado, Noah D. Goodman, 2012, CogSci.

Unsupervised Topic Modelling for Multi-Party Spoken Discourse

Thomas L. Griffiths, Joshua B. Tenenbaum, Konrad P. Körding, 2006, ACL.

Subjective randomness as statistical inference

Thomas L. Griffiths, Joshua B. Tenenbaum, Joseph L. Austerweil, 2018, Cognitive Psychology.

Unsupervised Learning by Program Synthesis

Armando Solar-Lezama, Joshua B. Tenenbaum, Kevin Ellis, 2015, NIPS.

From Algorithmic to Subjective Randomness

Thomas L. Griffiths, Joshua B. Tenenbaum, J. Tenenbaum, 2003, NIPS.

Unsupervised Learning of Compositional Energy Concepts pdf

Joshua B. Tenenbaum, Igor Mordatch, Shuang Li, 2021, NeurIPS.

Sparse multidimensional scaling using land-mark points

Joshua B. Tenenbaum, Vin de Silva, J. Tenenbaum, 2004 .

The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth

Joshua B. Tenenbaum, Mark Steyvers, J. Tenenbaum, 2001, Cogn. Sci..

Inverse Graphics with Probabilistic CAD Models pdf

Joshua B. Tenenbaum, Pushmeet Kohli, Vikash K. Mansinghka, 2014, ArXiv.

Learning Signal-Agnostic Manifolds of Neural Fields pdf

Vincent Sitzmann, Joshua B. Tenenbaum, Yilun Du, 2021, NeurIPS.

ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics

Jiancheng Liu, Wojciech Matusik, Jiajun Wu, 2018, 2019 International Conference on Robotics and Automation (ICRA).

Machine behaviour

Cynthia Breazeal, Iyad Rahwan, Michael P. Wellman, 2019, Nature.

A counterfactual simulation model of causal judgments for physical events.

Noah D. Goodman, J. Tenenbaum, Tobias Gerstenberg, 2021, Psychological review.

Learning Universal Policies via Text-Guided Video Generation

P. Abbeel, J. Tenenbaum, D. Schuurmans, 2023, ArXiv.

Music Gesture for Visual Sound Separation

Chuang Gan, Joshua B. Tenenbaum, Antonio Torralba, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Vector-based pedestrian navigation in cities

Paolo Santi, Carlo Ratti, Christian Bongiorno, 2021, Nature Computational Science.

Propagation Networks for Model-Based Control Under Partial Observation

Jiajun Wu, Joshua B. Tenenbaum, Antonio Torralba, 2018, 2019 International Conference on Robotics and Automation (ICRA).

Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing

Leslie Pack Kaelbling, Anurag Ajay, Jiajun Wu, 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Flexible Neural Representation for Physics Prediction

Daniel L. K. Yamins, Li Fei-Fei, Joshua B. Tenenbaum, 2018, NeurIPS.

CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data

Joshua B. Tenenbaum, Patrick Shafto, Vikash K. Mansinghka, 2015, J. Mach. Learn. Res..

Few-Shot Bayesian Imitation Learning with Logical Program Policies

Kelsey R. Allen, Alexander K. Lew, J. Tenenbaum, 2019, AAAI.

Multistability and Perceptual Inference

Joshua B. Tenenbaum, Ed Vul, Samuel Gershman, 2012, Neural Computation.

Adventures of human planners in Maze Search Task

Joshua B. Tenenbaum, Max Kleiman-Weiner, Marta Kryven, 2021 .

Growing knowledge culturally across generations to solve novel, complex tasks pdf

Joshua B. Tenenbaum, Michael Henry Tessler, Pedro A. Tsividis, 2021, ArXiv.

Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling

Jiajun Wu, Joshua B. Tenenbaum, William T. Freeman, 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

MarrNet: 3D Shape Reconstruction via 2.5D Sketches

Jiajun Wu, Joshua B. Tenenbaum, Yifan Wang, 2017, NIPS.

Learning and Long-Term Retention of Large-Scale Artificial Languages

Michael C. Frank, Joshua B. Tenenbaum, Edward Gibson, 2013, PloS one.

Compositionality in rational analysis: grammar-based induction for concept learning

Thomas L. Griffiths, Joshua B. Tenenbaum, Noah D. Goodman, 2008 .

Multiple timescales and uncertainty in motor adaptation

Joshua B. Tenenbaum, Konrad P. Körding, Reza Shadmehr, 2006, NIPS.

Multistability and Perceptual Inference Citation

J. Tenenbaum, S. Gershman, 2011 .

Perceptual Multistability as Markov Chain Monte Carlo Inference

Joshua B. Tenenbaum, Ed Vul, Samuel Gershman, 2009, NIPS.

Auditory scene analysis as Bayesian inference in sound source models

Joshua B. Tenenbaum, Josh H. McDermott, Luke B. Hewitt, 2018, CogSci.

Invariant representation of physical stability in the human brain

J. Tenenbaum, M. Cohen, N. Kanwisher, 2021, bioRxiv.

Explaining intuitive difficulty judgments by modeling physical effort and risk pdf

Joshua B. Tenenbaum, Grace Bennett-Pierre, Hyowon Gweon, 2019, CogSci.

Relational inductive bias for physical construction in humans and machines pdf

Jessica B. Hamrick, Joshua B. Tenenbaum, Kelsey R. Allen, 2018, CogSci.

Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning

Jiajun Wu, Joshua B. Tenenbaum, Joseph J. Lim, 2015, NIPS.

Physical predictions over time

Joshua B. Tenenbaum, Kevin A. Smith, Ed Vul, 2013, CogSci.

Causal inference in sensorimotor integration

Joshua B. Tenenbaum, Konrad P. Körding, Konrad Paul Kording, 2006, NIPS.

Representing and Learning a Large System of Number Concepts with Latent Predicate Networks

Joshua B. Tenenbaum, Joshua Rule, Eyal Dechter, 2015, CogSci.

Machine behaviour

Michael P. Wellman, Margaret E. Roberts, Isabel M. Kloumann, 2019, Nature.

Moral dynamics: Grounding moral judgment in intuitive physics and intuitive psychology

J. Tenenbaum, Tobias Gerstenberg, T. Ullman, 2021, Cognition.

Grounding Compositional Hypothesis Generation in Specific Instances

Joshua B. Tenenbaum, Fei Xu, Todd M. Gureckis, 2018, CogSci.

Efficient inverse graphics in biological face processing

Joshua B. Tenenbaum, Winrich A. Freiwald, Ilker Yildirim, 2018, Science Advances.

Toward the neural implementation of structure learning

Joshua B Tenenbaum, Samuel J Gershman, D Gowanlock R Tervo, 2016, Current Opinion in Neurobiology.

The neural architecture of language: Integrative reverse-engineering converges on a model for predictive processing

Eghbal A. Hosseini, J. Tenenbaum, N. Kanwisher, 2020 .

Theory-based causal induction.

Joshua B Tenenbaum, Thomas L Griffiths, J. Tenenbaum, 2009, Psychological review.

Learning Overhypotheses

J. Tenenbaum, Charles Kemp, A. Perfors, 2006 .

Bayesian vector analysis and the perception of hierarchical motion

Frank Jäkel, Joshua B. Tenenbaum, Samuel Gershman, 2013, CogSci.

Rational statistical inference: A critical component for word learning

Joshua B Tenenbaum, Fei Xu, J. Tenenbaum, 2001, Behavioral and Brain Sciences.

Joshua B. Tenenbaum, J. Tenenbaum, 1999, NIPS.

Inferring Structured Visual Concepts from Minimal Data

Peng Qian, Roger Levy, Joshua B. Tenenbaum, 2019, CogSci.

The logical primitives of thought: Empirical foundations for compositional cognitive models.

Noah D. Goodman, J. Tenenbaum, S. Piantadosi, 2016, Psychological review.

Beyond Boolean logic: exploring representation languages for learning complex concepts

Joshua B. Tenenbaum, Noah D. Goodman, Steven Piantadosi, 2010 .

Discovering Structure by Learning Sparse Graphs

Joshua B. Tenenbaum, Brenden M. Lake, J. Tenenbaum, 2010 .

Planning with Diffusion for Flexible Behavior Synthesis

S. Levine, J. Tenenbaum, Michael Janner, 2022, ICML.

Learning Compositional Rules via Neural Program Synthesis pdf

Armando Solar-Lezama, Joshua B. Tenenbaum, Brenden M. Lake, 2020, NeurIPS.

Elements of a stochastic 3D prediction engine in larval zebrafish prey capture

Joshua B. Tenenbaum, Florian Engert, Feras Saad, 2019, bioRxiv.

Compositional Visual Generation with Composable Diffusion Models

J. Tenenbaum, A. Torralba, Yilun Du, 2022, ECCV.

Linking Models of Theory of Mind and Measures of Human Brain Activity

J. Tenenbaum, R. Saxe, S. Houlihan, 2021 .

Neural Groundplans: Persistent Neural Scene Representations from a Single Image

J. Tenenbaum, W. Freeman, F. Durand, 2022, ICLR.

The Naïve Utility Calculus as a unified, quantitative framework for action understanding

Joshua B. Tenenbaum, Julian Jara-Ettinger, Laura E. Schulz, 2020, Cognitive Psychology.

GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling

Leslie Pack Kaelbling, Tom Silver, Rohan Chitnis, 2020, AAAI.

GLIB: Exploration via Goal-Literal Babbling for Lifted Operator Learning pdf

Leslie Pack Kaelbling, Tom Silver, Rohan Chitnis, 2020, ArXiv.

Running head : LEARNING OVERHYPOTHESES Learning overhypotheses with hierarchical Bayesian models

Joshua B. Tenenbaum, Amy Perfors, Charles Kemp, 2006 .

Foley Music: Learning to Generate Music from Videos

Chuang Gan, Peihao Chen, Joshua B. Tenenbaum, 2020, ECCV.

Residual Policy Learning pdf

Leslie Pack Kaelbling, Joshua B. Tenenbaum, Tom Silver, 2018, ArXiv.

GAN Dissection: Visualizing and Understanding Generative Adversarial Networks pdf

Bolei Zhou, Joshua B. Tenenbaum, Antonio Torralba, 2018, ICLR.

Map Induction: Compositional spatial submap learning for efficient exploration in novel environments

J. Tenenbaum, I. Fiete, Aidan Curtis, 2021, ICLR.

DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs

Li Fei-Fei, J. Tenenbaum, S. Du, 2019, IJCAI.

Dual Sequential Monte Carlo: Tunneling Filtering and Planning in Continuous POMDPs pdf

Yunbo Wang, Fei-Fei Li, Yuke Zhu, 2019, ArXiv.

Learning What is Where from Social Observations

Joshua B. Tenenbaum, Julian Jara-Ettinger, Chris L. Baker, 2012, CogSci.

Special Issue: Probabilistic models of cognition Probabilistic models of cognition: Conceptual foundations

J. Tenenbaum, A. Yuille, N. Chater, 2006 .

Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC

R. Fergus, Will Grathwohl, J. Tenenbaum, 2023, ArXiv.

Learning to Compose Visual Relations pdf

Joshua B. Tenenbaum, Antonio Torralba, Shuang Li, 2021, NeurIPS.

3D Shape Perception from Monocular Vision, Touch, and Shape Priors

Edward H. Adelson, Jiajun Wu, Joshua B. Tenenbaum, 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Learning to Infer and Execute 3D Shape Programs pdf

Jiajun Wu, Joshua B. Tenenbaum, William T. Freeman, 2019, ICLR.

Learning to Reconstruct Shapes from Unseen Classes

Jiajun Wu, Joshua B. Tenenbaum, Chengkai Zhang, 2018, NeurIPS.

Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface

Luke B. Hewitt, Tuan Anh Le, J. Tenenbaum, 2021, ICLR.

Exploring the Role of Representation in Models of Grammatical Category Acquisition

Joshua B. Tenenbaum, Richard N. Aslin, Elissa L. Newport, 2012, CogSci.

Self-Supervised Intrinsic Image Decomposition

Jiajun Wu, Joshua B. Tenenbaum, Tejas D. Kulkarni, 2017, NIPS.

Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding

Chuang Gan, Jiajun Wu, Joshua B. Tenenbaum, 2018, NeurIPS.

Single Image 3D Interpreter Network pdf

Yuandong Tian, Jiajun Wu, Joshua B. Tenenbaum, 2016, ECCV.

Factorial Learning by Clustering Features

Joshua B. Tenenbaum, Emanuel Todorov, E. Todorov, 1994, NIPS.

Are Deep Neural Networks SMARTer than Second Graders?

Kevin A. Smith, J. Tenenbaum, A. Cherian, 2022, ArXiv.

FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations

J. Tenenbaum, Jiayuan Mao, Chuang Gan, 2022, ICLR.

Visual Concept-Metaconcept Learning

Chuang Gan, Jiayuan Mao, Jiajun Wu, 2020, NeurIPS.

Modeling human intention inference in continuous 3D domains by inverse planning and body kinematics pdf

Hanbyul Joo, Marta Kryven, Tao Gao, 2021, ArXiv.

Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind pdf

Joshua B. Tenenbaum, Vikash K. Mansinghka, Tan Zhi-Xuan, 2021, ArXiv.

Plans or Outcomes: How Do We Attribute Intelligence to Others?

Joshua B. Tenenbaum, Tomer Ullman, Marta Kryven, 2021, Cogn. Sci..

Online Bayesian Goal Inference for Boundedly-Rational Planning Agents

Vikash K. Mansinghka, Jordyn L. Mann, J. Tenenbaum, 2020, NeurIPS.

Social Pragmatics: Preschoolers Rely on Commonsense Psychology to Resolve Referential Underspecification.

Julian Jara-Ettinger, Joshua B Tenenbaum, Holly Huey, 2019, Child development.

Ten-month-old infants infer the value of goals from the costs of actions

Joshua B. Tenenbaum, Elizabeth S. Spelke, Tomer Ullman, 2017, Science.

Interpreting actions by attributing compositional desires

Joshua B. Tenenbaum, Julian Jara-Ettinger, Joey Velez-Ginorio, 2017, CogSci.

Children’s understanding of the costs and rewards underlying rational action

Joshua B. Tenenbaum, Laura Schulz, Julian Jara-Ettinger, 2015, Cognition.

Beliefs about desires: Children's understanding of how knowledge and preference influence choice

Joshua B. Tenenbaum, Laura Schulz, Julian Jara-Ettinger, 2015, CogSci.

Learning to Infer Graphics Programs from Hand-Drawn Images

Armando Solar-Lezama, Joshua B. Tenenbaum, Daniel Ritchie, 2017, NeurIPS.

Learning Libraries of Subroutines for Neurally-Guided Bayesian Program Induction

Armando Solar-Lezama, Joshua B. Tenenbaum, Mathias Sablé-Meyer, 2018, NeurIPS.

ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models

Boris Katz, Josh Tenenbaum, Dan Gutfreund, 2019, NeurIPS.

Sticking to the Evidence? A computational and behavioral case study of micro-theory change in the domain of magnetism

Alison Gopnik, Joshua B. Tenenbaum, Elizabeth Baraff Bonawitz, 2012, 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL).

Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults

Thomas L. Griffiths, Alison Gopnik, Joshua B. Tenenbaum, 2011, Cogn. Sci..

Structure Learning in Human Causal Induction

Thomas L. Griffiths, Joshua B. Tenenbaum, J. Tenenbaum, 2000, NIPS.

Children’s exploratory play tracks the discriminability of hypotheses

Max H. Siegel, J. Tenenbaum, L. Schulz, 2021, Nature Communications.

Pure Reasoning in 12-Month-Old Infants as Probabilistic Inference

Edward Vul, Joshua B Tenenbaum, Vittorio Girotto, 2011, Science.

Go fishing! Responsibility judgments when cooperation breaks down

Joshua B. Tenenbaum, Julian Jara-Ettinger, Kelsey R. Allen, 2015, CogSci.

Inference of Intention and Permissibility in Moral Decision Making

Joshua B. Tenenbaum, Sydney Levine, Tobias Gerstenberg, 2015, CogSci.

Write, Execute, Assess: Program Synthesis with a REPL

Armando Solar-Lezama, Joshua B. Tenenbaum, Kevin Ellis, 2019, NeurIPS.

Neural Scene De-rendering

Jiajun Wu, Joshua B. Tenenbaum, Pushmeet Kohli, 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Learning Causal Laws

Joshua B. Tenenbaum, Sourabh Niyogi, J. Tenenbaum, 2003 .

Efficient Visual Heuristics in the Perception of Physical Object Properties

Vivian C. Paulun, J. Tenenbaum, R. Fleming, 2023, bioRxiv.

Object-Centric Diagnosis of Visual Reasoning pdf

Chuang Gan, Jiajun Wu, Joshua B. Tenenbaum, 2020, ArXiv.

Visual Dependency Transformers: Dependency Tree Emerges from Reversed Attention

J. Tenenbaum, Lijie Fan, Chuang Gan, 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Grammar-Based Grounded Lexicon Learning

J. Tenenbaum, R. Levy, Jiayuan Mao, 2022, Neural Information Processing Systems.

Subject Index to Volume 29

J. Tenenbaum, S. Sloman, D. Lagnado, 1981, Progress in Neuro-Psychopharmacology and Biological Psychiatry.

Bayesian Theory of Mind: Modeling Joint Belief-Desire Attribution

Joshua B. Tenenbaum, Rebecca Saxe, Chris L. Baker, 2011, CogSci.

When Is It Acceptable to Break the Rules? Knowledge Representation of Moral Judgement Based on Empirical Data pdf

Iyad Rahwan, Kartik Talamadupula, Nicholas Mattei, 2022, ArXiv.

3D Interpreter Networks for Viewer-Centered Wireframe Modeling

Yuandong Tian, Jiajun Wu, Joshua B. Tenenbaum, 2018, International Journal of Computer Vision.

Planning with Large Language Models for Code Generation

J. Tenenbaum, Chuang Gan, Mingyu Ding, 2023, ArXiv.

Top-Down Synthesis for Library Learning

J. Tenenbaum, Armando Solar-Lezama, Kevin Ellis, 2022, Proc. ACM Program. Lang..

A theory of learning to infer

Samuel J. Gershman, Joshua B. Tenenbaum, Eric Schulz, 2019, bioRxiv.

DreamCoder: bootstrapping inductive program synthesis with wake-sleep library learning

Armando Solar-Lezama, Joshua B. Tenenbaum, Mathias Sablé-Meyer, 2021, PLDI.

Representing Partial Programs with Blended Abstract Semantics

J. Tenenbaum, Jacob Andreas, Armando Solar-Lezama, 2020, ICLR.

Library learning for neurally-guided Bayesian program induction

Armando Solar-Lezama, Joshua B. Tenenbaum, Mathias Sablé-Meyer, 2018, NIPS 2018.

Bias reformulation for one-shot function induction

Stephen Muggleton, Joshua B. Tenenbaum, Eyal Dechter, 2014, ECAI.

Too Many Cooks: Bayesian Inference for Coordinating Multi-Agent Collaboration

Joshua B. Tenenbaum, David C. Parkes, James A. Evans, 2020, Top. Cogn. Sci..

Constructing Social Preferences From Anticipated Judgments: When Impartial Inequity is Fair and Why?

Joshua B. Tenenbaum, Alex Shaw, Max Kleiman-Weiner, 2017, CogSci.

Rational Inference of Beliefs and Desires From Emotional Expressions

Joshua B. Tenenbaum, Laura Schulz, Yang Wu, 2017, Cogn. Sci..

Bootstrapping in a language of thought: A formal model of conceptual change in number word learning

Joshua B. Tenenbaum, Noah D. Goodman, Steven Piantadosi, 2012 .

DensePhysNet: Learning Dense Physical Object Representations via Multi-step Dynamic Interactions pdf

Jiajun Wu, Joshua B. Tenenbaum, Shuran Song, 2019, Robotics: Science and Systems.

Bayesian collective learning emerges from heuristic social learning

J. Tenenbaum, T. Griffiths, A. Pentland, 2021, Cognition.

Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning pdf

Samuel J. Gershman, Joshua B. Tenenbaum, Andres Campero, 2021, ArXiv.

DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics

J. Tenenbaum, Tao Du, Hao Su, 2023, ICLR.

DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools

J. Tenenbaum, David Held, Yunzhu Li, 2022, ICLR.

PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics pdf

Joshua B. Tenenbaum, Hao Su, Yuanming Hu, 2021, ICLR.

SoftZoo: A Soft Robot Co-design Benchmark For Locomotion In Diverse Environments

J. Tenenbaum, D. Rus, Andrew Spielberg, 2023, ICLR.

Noisy Agents: Self-supervised Exploration by Predicting Auditory Events pdf

Xiaoyu Chen, Chuang Gan, Joshua B. Tenenbaum, 2020, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

ConceptFusion: Open-set Multimodal 3D Mapping

Krishna Murthy Jatavallabhula, Nikhil Varma Keetha, J. Tenenbaum, 2023, Robotics: Science and Systems.

Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language pdf

Joshua B. Tenenbaum, Ping Luo, Mingyu Ding, 2021, NeurIPS.

Grounding Physical Concepts of Objects and Events Through Dynamic Visual Reasoning pdf

Joshua B. Tenenbaum, Jiayuan Mao, Jiajun Wu, 2021, ICLR.

Entity Abstraction in Visual Model-Based Reinforcement Learning

John D. Co-Reyes, S. Levine, J. Tenenbaum, 2019, CoRL.

Generalized Planning in PDDL Domains with Pretrained Large Language Models

J. Tenenbaum, L. Kaelbling, Michael Katz, 2023, ArXiv.

Discovering State and Action Abstractions for Generalized Task and Motion Planning pdf

Leslie Pack Kaelbling, Joshua B. Tenenbaum, Tom Silver, 2021, AAAI.

Learning to solve complex tasks by growing knowledge culturally across generations

Joshua B. Tenenbaum, Noah D. Goodman, Michael Henry Tessler, 2021, 2107.13377.

The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark Towards Physically Realistic Embodied AI pdf

Joshua B. Tenenbaum, Antonio Torralba, James J DiCarlo, 2021, 2022 International Conference on Robotics and Automation (ICRA).

Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering pdf

Vincent Sitzmann, Joshua B. Tenenbaum, William T. Freeman, 2021, NeurIPS.

3D-Aware Scene Manipulation via Inverse Graphics

Shunyu Yao, Jiajun Wu, Joshua B. Tenenbaum, 2018, NeurIPS.

Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling pdf

Kai Xu, Bernhard Egger, David D. Cox, 2020, ArXiv.

MOST-GAN: 3D Morphable StyleGAN for Disentangled Face Image Manipulation pdf

Anoop Cherian, Joshua B. Tenenbaum, Tim K. Marks, 2021, AAAI.

3D Concept Learning and Reasoning from Multi-View Images

J. Tenenbaum, Yilun Du, Chuang Gan, 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Inferring human intent from video by sampling hierarchical plans

Joshua B. Tenenbaum, Tao Gao, Song-Chun Zhu, 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Robust Change Detection Based on Neural Descriptor Fields

J. Tenenbaum, J. Leonard, Yilun Du, 2022, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

3DP3: 3D Scene Perception via Probabilistic Programming pdf

Joshua B. Tenenbaum, Vikash K. Mansinghka, Dan Gutfreund, 2021, NeurIPS.

Probabilistic Video Prediction From Noisy Data With a Posterior Confidence

Yunbo Wang, Jiajun Wu, Joshua B. Tenenbaum, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Unsupervised Discovery of Parts, Structure, and Dynamics pdf

Chen Sun, Zhijian Liu, Jiajun Wu, 2019, ICLR.

See, feel, act: Hierarchical learning for complex manipulation skills with multisensory fusion

Jiajun Wu, Joshua B. Tenenbaum, Alberto Rodriguez, 2019, Science Robotics.

Unsupervised Discovery of 3D Physical Objects from Video pdf

Jiajun Wu, Kevin A. Smith, Joshua Tenenbaum, 2020, ICLR.

A Long Horizon Planning Framework for Manipulating Rigid Pointcloud Objects pdf

Pulkit Agrawal, Beomjoon Kim, Yilun Du, 2020, CoRL.

End-to-End Optimization of Scene Layout

Jiajun Wu, Joshua B. Tenenbaum, Zhoutong Zhang, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

The three pillars of machine programming

Armando Solar-Lezama, Regina Barzilay, Joshua B. Tenenbaum, 2018, MAPL@PLDI.

Learning Shape Priors for Single-View 3D Completion and Reconstruction pdf

Jiajun Wu, Joshua B. Tenenbaum, William T. Freeman, 2018, ECCV.

The Tools Challenge: Rapid Trial-and-Error Learning in Physical Problem Solving

Joshua B. Tenenbaum, Kevin A. Smith, Kelsey R. Allen, 2019, CogSci.

NeuSE: Neural SE(3)-Equivariant Embedding for Consistent Spatial Understanding with Objects

J. Tenenbaum, Yilun Du, Kurran Singh, 2023, ArXiv.

The Cognitive Architecture of Perceived Animacy: Intention, Attention, and Memory

Joshua B Tenenbaum, Ning Tang, Chris L. Baker, 2019, Cogn. Sci..

Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning pdf

Joshua B. Tenenbaum, Michael Henry Tessler, Brenden M. Lake, 2021, NeurIPS.

Rethink reporting of evaluation results in AI

Joel Z. Leibo, J. Tenenbaum, T. Ullman, 2023, Science.

Program Synthesis with Pragmatic Communication

J. Tenenbaum, Armando Solar-Lezama, Kevin Ellis, 2020, NeurIPS.

Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration pdf

Sanja Fidler, Joshua B. Tenenbaum, Antonio Torralba, 2020, ICLR.

Learning to Exploit Stability for 3D Scene Parsing

Zhijian Liu, Jiajun Wu, Joshua B. Tenenbaum, 2018, NeurIPS.

Synthesizing 3D Shapes via Modeling Multi-view Depth Maps and Silhouettes with Deep Generative Networks

Jiajun Wu, Joshua B. Tenenbaum, Haibin Huang, 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Evaluating statistical language models as pragmatic reasoners

J. Tenenbaum, Gabriel Grand, Benjamin Lipkin, 2023, ArXiv.

Inventing Relational State and Action Abstractions for Effective and Efficient Bilevel Planning

J. Tenenbaum, L. Kaelbling, Tomas Lozano-Perez, 2022, ArXiv.

3D Neural Embedding Likelihood for Robust Probabilistic Inverse Graphics

Vikash K. Mansinghka, J. Tenenbaum, D. George, 2023, 2302.03744.

Too Many Cooks: Coordinating Multi-agent Collaboration Through Inverse Planning

Joshua B. Tenenbaum, David C. Parkes, James A. Evans, 2020, AAMAS.

Stochastic Prediction of Multi-Agent Interactions from Partial Observations pdf

Chen Sun, Jiajun Wu, Joshua B. Tenenbaum, 2019, ICLR.

H-SAUR: Hypothesize, Simulate, Act, Update, and Repeat for Understanding Object Articulations from Interactions

Kevin A. Smith, Tim K. Marks, J. Tenenbaum, 2022, 2023 IEEE International Conference on Robotics and Automation (ICRA).

Discovery and usage of joint attention in images pdf

Joshua B. Tenenbaum, Shimon Ullman, Daniel Harari, 2018, ArXiv.

Learning with AMIGo: Adversarially Motivated Intrinsic Goals pdf

Joshua B. Tenenbaum, Edward Grefenstette, Tim Rocktäschel, 2020, ICLR.

Single-Shot Scene Reconstruction

Frédo Durand, Vincent Sitzmann, Joshua B. Tenenbaum, 2021, CoRL.

Measuring and modeling the perception of natural and unconstrained gaze in humans and machines pdf

Nancy Kanwisher, Joshua B. Tenenbaum, Shimon Ullman, 2016, ArXiv.

Tactile-Filter: Interactive Tactile Perception for Part Mating

Devesh K. Jha, J. Tenenbaum, H. Tung, 2023, ArXiv.

Modeling the acquisition of quantifier semantics: a case study in function word learnability

Joshua B. Tenenbaum, Noah D. Goodman, Steven T. Piantadosi, 2012 .

Online Developmental Science to Foster Innovation, Access, and Impact

Michael C. Frank, Candice M. Mills, K. Adolph, 2020, Trends in Cognitive Sciences.

Fragment Grammars: Exploring Computation and Reuse in Language

Joshua B. Tenenbaum, Noah D. Goodman, Timothy O'Donnell, 2009 .

When Computer Vision Gazes at Cognition pdf

Joshua B. Tenenbaum, Shimon Ullman, Tao Gao, 2014, ArXiv.

3D-IntPhys: Towards More Generalized 3D-grounded Visual Intuitive Physics under Challenging Scenes

Daniel L. K. Yamins, J. Tenenbaum, A. Torralba, 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

A Bayesian Framework for Cross-Situational Word-Learning

Michael C. Frank, Joshua B. Tenenbaum, Noah D. Goodman, 2007, NIPS.

Leveraging Unstructured Statistical Knowledge in a Probabilistic Language of Thought

Michael Henry Tessler, Vikash K. Mansinghka, Alexander K. Lew, 2020, CogSci.

What happened? Reconstructing the past through vision and sound

Joshua B. Tenenbaum, Tobias Gerstenberg, Max H. Siegel, 2018, CogSci.

Natural science: Active learning in dynamic physical microworlds

Joshua B. Tenenbaum, Tobias Gerstenberg, Neil Bramley, 2016, CogSci.

Learning list concepts through program induction

Joshua B. Tenenbaum, Steve Piantadosi, Eric Schulz, 2018, bioRxiv.

Modeling Semantic Cognition as Logical Dimensionality Reduction

Yarden Katz, Kristian Kersting, Joshua B. Tenenbaum, 2008 .

Learning and using relational theories

Joshua B. Tenenbaum, Charles Kemp, Noah D. Goodman, 2007, NIPS.

Logical Rule Induction and Theory Learning Using Neural Theorem Proving pdf

Sebastian Riedel, Joshua B. Tenenbaum, Tim Klinger, 2018, ArXiv.

Probabilistic Adaptation of Text-to-Video Models

P. Abbeel, J. Tenenbaum, D. Schuurmans, 2023, ArXiv.

Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model

George A. Alvarez, Michael C. Frank, Joshua B. Tenenbaum, 2009, NIPS.

Joint inferences of belief and desire from facial expressions

Joshua B. Tenenbaum, Laura Schulz, Yang Wu, 2014, CogSci.

A rational model of syntactic bootstrapping

Roger Levy, Joshua B. Tenenbaum, Jon Gauthier, 2019, CogSci.

Inferring the Future by Imagining the Past

J. Tenenbaum, Jonathan Ragan-Kelley, Tzu-Mao Li, 2023, ArXiv.

Modeling human intuitions about liquid flow with particle-based simulation

Joshua B. Tenenbaum, Peter W. Battaglia, Ilker Yildirim, 2018, PLoS Comput. Biol..

Learning Neural Constitutive Laws From Motion Observations for Generalizable PDE Dynamics

Peter Yichen Chen, J. Tenenbaum, W. Matusik, 2023, ArXiv.

Error-Driven Stochastic Search for Theories and Concepts

Joshua B. Tenenbaum, Owen Lewis, Santiago Perez, 2014, CogSci.

At Human Speed: Deep Reinforcement Learning with Action Delay pdf

Joshua B. Tenenbaum, Vlad Firoiu, Tina Ju, 2018, ArXiv.

Assessing the Perceived Predictability of Functions

Joshua B. Tenenbaum, Samuel Gershman, Maarten Speekenbrink, 2015, CogSci.

Intrinsically Motivated Exploration as Empowerment

J. Tenenbaum, S. Gershman, Eric Schulz, 2022 .

A Computational Model of Commonsense Moral Decision Making

Iyad Rahwan, Joshua B. Tenenbaum, Sohan Dsouza, 2018, AIES.

Unsupervised Compositional Concepts Discovery with Text-to-Image Generative Models

J. Tenenbaum, A. Torralba, Yilun Du, 2023, 2306.05357.

Improving Factuality and Reasoning in Language Models through Multiagent Debate

Yilun Du, Igor Mordatch, J. Tenenbaum, 2023, ArXiv.

Communicating Natural Programs to Humans and Machines

Michael Henry Tessler, J. Tenenbaum, Maxwell Nye, 2021, NeurIPS.

Human collective intelligence as distributed Bayesian inference pdf

Alex Pentland, Yaniv Altshuler, Joshua B. Tenenbaum, 2016, ArXiv.

Arengiya Anyainya Aidmeniya Aburliya Adardiya Agngiya Aweniya Amaidya Abmarliya Awaadya Anguriya Adiaya Angeliya Algyeliya Adniadya Aleriya Umbaidya Anowadya Muriya Angenduriya Amburniya Andungiya Aneriya Aiyenga Undyaidya Gnaldena

J. Tenenbaum, T. Griffiths, Charles Kemp, 2004 .

Structured models of semantic cognition

Joshua B. Tenenbaum, Charles Kemp, J. Tenenbaum, 2008, Behavioral and Brain Sciences.

Continuity of Discourse Provides Information for Word Learning

Michael C. Frank, Joshua B. Tenenbaum, Noah D. Goodman, 2007 .

Learning Neural Constitutive Laws From Motion Observations for Generalizable PDE Dynamics

Peter Yichen Chen, J. Tenenbaum, W. Matusik, 2023, ICML.

Emotion prediction as computation over a generative theory of mind

Luke B. Hewitt, J. Tenenbaum, R. Saxe, 2023, Philosophical Transactions of the Royal Society A.

Dynamical Causal Learning

Thomas L. Griffiths, Joshua B. Tenenbaum, David Danks, 2002, NIPS.

The Role of Causal Models in Statistical Reasoning

Joshua B. Tenenbaum, Tevya R. Krynski, J. Tenenbaum, 2003 .

A generative model of people's intuitive theory of emotions: inverse planning in rich social games

Joshua B. Tenenbaum, Rebecca Saxe, Max Kleiman-Weiner, 2018, CogSci.

Outcome or Strategy? A Bayesian Model of Intelligence Attribution

Joshua B. Tenenbaum, Tomer Ullman, Marta Kryven, 2016, CogSci.

Wins above replacement: Responsibility attributions as counterfactual replacements

Joshua B. Tenenbaum, David A. Lagnado, Tomer Ullman, 2014, CogSci.

PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception pdf

Joshua B. Tenenbaum, Tianmin Shu, Andrei Barbu, 2021, AAAI.

AAAI Press Formatting Instructions for Authors Using LaTeX -- A Guide

J. Tenenbaum, Andrei Barbu, Tianmin Shu, 2021 .

Data-Efficient Learning for Complex and Real-Time Physical Problem Solving Using Augmented Simulation

Joshua B. Tenenbaum, Kevin A. Smith, Devesh K. Jha, 2021, IEEE Robotics and Automation Letters.

Modeling Human Understanding of Complex Intentional Action with a Bayesian Nonparametric Subgoal Model

Ryo Nakahashi, Joshua B. Tenenbaum, Chris L. Baker, 2015, AAAI.

Lifelong learning of cognitive strategies for physical problem-solving: the effect of embodied experience

Kevin A. Smith, Kelsey R. Allen, J. Tenenbaum, 2022, bioRxiv.

Meta-strategy learning in physical problem-solving: the effect of embodied experience

Kevin A. Smith, Kelsey R. Allen, J. Tenenbaum, 2021 .

Invariant representations of mass in the human brain

Nancy Kanwisher, Joshua B Tenenbaum, Sarah Schwettmann, 2019, eLife.

Combining Functional and Automata Synthesis to Discover Causal Reactive Programs

J. Tenenbaum, Armando Solar-Lezama, Ria Das, 2023, Proc. ACM Program. Lang..

A large-scale benchmark for few-shot program induction and synthesis

Leslie Pack Kaelbling, Joshua B. Tenenbaum, Ferran Alet, 2021, ICML.

Noether Networks: Meta-Learning Useful Conserved Quantities pdf

Chelsea Finn, Ferran Alet, Joshua Tenenbaum, 2021, NeurIPS.

“Structured statistical models of inductive reasoning”: Correction.

J. Tenenbaum, Charles Kemp, 2009 .

Incorporating Rich Social Interactions Into MDPs

J. Tenenbaum, Andrei Barbu, Tianmin Shu, 2021, 2022 International Conference on Robotics and Automation (ICRA).

Emergent Collective Sensing in Human Groups

Alex Pentland, Joshua B. Tenenbaum, Noah D. Goodman, 2015, CogSci.

Word learning and the acquisition of syntactic-semantic overhypotheses pdf

Roger Levy, Joshua B. Tenenbaum, Jon Gauthier, 2018, CogSci.

Multi-Plane Program Induction with 3D Box Priors pdf

Jiajun Wu, Noah Snavely, Bill Freeman, 2020, NeurIPS.

Perspective Plane Program Induction From a Single Image

Jiajun Wu, Joshua B. Tenenbaum, William T. Freeman, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Natively probabilistic computation

Joshua B. Tenenbaum, Vikash K. Mansinghka, J. Tenenbaum, 2009 .

Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision

J. Tenenbaum, W. Freeman, F. Durand, 2023, ArXiv.

Is Conditional Generative Modeling all you need for Decision-Making?

J. Tenenbaum, T. Jaakkola, Pulkit Agrawal, 2022, ICLR.

Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC

R. Fergus, Will Grathwohl, J. Tenenbaum, 2023, ICML.

Inferring the Goals of Communicating Agents from Actions and Instructions

Vikash K. Mansinghka, J. Tenenbaum, Tan Zhi-Xuan, 2023, ArXiv.

Acting as Inverse Inverse Planning

J. Tenenbaum, Jonathan Ragan-Kelley, Tzu-Mao Li, 2023, SIGGRAPH.

Tiptoeing around it: Inference from absence in potentially offensive speech

Monica A. Gates, Joshua B. Tenenbaum, Kevin A. Smith, 2018, CogSci.

Mental jenga: A counterfactual simulation model of causal judgments about physical support.

Kevin A. Smith, J. Tenenbaum, Tobias Gerstenberg, 2023, Journal of experimental psychology. General.

The Variational Homoencoder: Learning to learn high capacity generative models from few examples

Joshua B. Tenenbaum, Tommi S. Jaakkola, Andreea Gane, 2018, UAI.

Program-Guided Image Manipulators

J. Tenenbaum, W. Freeman, Jiajun Wu, 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

Dynamic Modeling of Hand-Object Interactions via Tactile Sensing

Wojciech Matusik, Joshua B. Tenenbaum, Antonio Torralba, 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

D EX D EFORM : D EXTEROUS D EFORMABLE O BJECT M A - NIPULATION WITH H UMAN D EMONSTRATIONS AND D IFFERENTIABLE P HYSICS

J. Tenenbaum, Tao Du, Hao Su, 2023 .

Evaluating Language Models for Mathematics through Interactions

Katherine M. Collins, Yuhuai Wu, J. Tenenbaum, 2023, arXiv.org.

Embodied Concept Learner: Self-supervised Learning of Concepts and Mapping through Instruction Following

J. Tenenbaum, Chuang Gan, Ping Luo, 2023, CoRL.

Building 3D Generative Models from Minimal Data

J. Tenenbaum, B. Egger, Skylar Sutherland, 2022, International Journal of Computer Vision.

The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology (Trends in Cognitive Sciences 20, 589–604; July 19, 2016)

Joshua B. Tenenbaum, Julian Jara-Ettinger, Hyowon Gweon, 2016, Trends in Cognitive Sciences.

Learning abstract structure for drawing by efficient motor program induction

Joshua B. Tenenbaum, Marta Kryven, Kevin Ellis, 2020, NeurIPS.

Integrating identification and perception: A case study of familiar and unfamiliar face processing

Joshua B. Tenenbaum, Kelsey R. Allen, Ilker Yildirim, 2016, CogSci.

Big Data and Little Learners

Charles Yang, Joshua B. Tenenbaum, Linda B. Smith, 2017, CogSci.

What can the brain teach us about building artificial intelligence?

Julian N. Marewski, Daniela Link, Piotr M. Patrzyk, 2016, Behavioral and Brain Sciences.

Building Cooperative Embodied Agents Modularly with Large Language Models

J. Tenenbaum, Yilun Du, Tianmin Shu, 2023, ArXiv.

MarrNet : 3 D Shape Reconstruction via 2 . 5 D Sketches

J. Tenenbaum, Tianfan Xue, Jiajun Wu, 2017 .

The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision

J. Tenenbaum, Pushmeet Kohli, Jiajun Wu, 2019, ICLR.

Learning Physical Graph Representations from Visual Scenes pdf

Li Fei-Fei, Jiajun Wu, Joshua B. Tenenbaum, 2020, NeurIPS.

Probability, programs, and the mind: Building structured Bayesian models of cognition

Joshua B. Tenenbaum, Noah D. Goodman, J. Tenenbaum, 2014, CogSci.

Unsupervised Discovery and Composition of Object Light Fields

J. Tenenbaum, F. Durand, Jiajun Wu, 2022, ArXiv.

A Morphable Face Albedo Model

Bernhard Egger, Bernard Tiddeman, Hannah M. Dee, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Neurally-Guided Structure Inference

Jiajun Wu, Joshua B. Tenenbaum, Jiayuan Mao, 2019, ICML.

Word Segmentation as Word Learning : Integrating Meaning Learning with Distributional Cues to Segmentation

Michael C. Frank, Joshua B. Tenenbaum, Edward Gibson, 2007 .

Causal and compositional generative models in online perception

Joshua B. Tenenbaum, Christian Wallraven, Ilker Yildirim, 2017, CogSci.

One-shot learning of generative speech concepts

James R. Glass, Joshua B. Tenenbaum, Brenden M. Lake, 2014, CogSci.

Building Machines that Learn and Think Like People

J. Tenenbaum, 2018, AAMAS.

Learning to act by integrating mental simulations and physical experiments

Joshua B. Tenenbaum, Kevin A. Smith, Samuel Gershman, 2018, bioRxiv.

Infinite Dynamic Bayesian Networks

Joshua B. Tenenbaum, Nicholas Roy, Finale Doshi-Velez, 2011, ICML.

Pure Reasoning in 12-Month-Old Infants as Probabilistic Inference

J. Tenenbaum, E. Vul, V. Girotto, 2011, Science.

Theory-Based Induction

Joshua B. Tenenbaum, Charles Kemp, J. Tenenbaum, 2003 .

Seeing Tree Structure from Vibration pdf

Jiajun Wu, Joshua B. Tenenbaum, William T. Freeman, 2018, ECCV.

On the Expressiveness and Generalization of Hypergraph Neural Networks

J. Tenenbaum, L. Kaelbling, Jiayuan Mao, 2023, ArXiv.

From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought

Noah D. Goodman, Vikash K. Mansinghka, Alexander K. Lew, 2023, ArXiv.

How many observations is one generic worth?

Michael Henry Tessler, J. Tenenbaum, Sophie Bridgers, 2020, Annual Meeting of the Cognitive Science Society.

The Neuro-Symbolic Inverse Planning Engine (NIPE): Modeling Probabilistic Social Inferences from Linguistic Inputs

Katherine M. Collins, J. Tenenbaum, Adrian Weller, 2023, arXiv.org.

Linking Emergent and Natural Languages via Corpus Transfer

J. Tenenbaum, Mo Yu, Yang Zhang, 2022, ICLR.

Informative communication in word production and word learning

Michael C. Frank, Joshua B. Tenenbaum, Noah D. Goodman, 2009 .

Prospective search time estimates reveal the strengths and limits of internal models of visual search.

Max H. Siegel, J. Tenenbaum, M. Mazor, 2023, Journal of experimental psychology. General.

Flexible social inference facilitates targeted social learning when rewards are not observable

Noah D. Goodman, Robert D. Hawkins, J. Tenenbaum, 2022, Nature Human Behaviour.

Learning to Compose Visual Relations

J. Tenenbaum, A. Torralba, Nan Liu, 2021 .

Grounding Physical Concepts of Objects and Events Through Dynamic Visual Reasoning

Kwan-Yee Kenneth Wong, J. Tenenbaum, Jiayuan Mao, 2021, ICLR.

Planning with Sequence Models through Iterative Energy Minimization

J. Tenenbaum, Yilun Du, P. Vela, 2023, International Conference on Learning Representations.

Learning Rational Subgoals from Demonstrations and Instructions

J. Tenenbaum, L. Kaelbling, Tomas Lozano-Perez, 2023, AAAI.

Identity-Expression Ambiguity in 3D Morphable Face Models

Joshua Tenenbaum, Safa C. Medin, Skylar Sutherland, 2021, 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021).

Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing

J. Tenenbaum, L. Kaelbling, Maria Bauzá, 2018, IROS.

Learning Task Decomposition with Ordered Memory Policy Network pdf

Joshua B. Tenenbaum, Aaron C. Courville, Aaron Courville, 2021, ICLR.

Few-Shot Bayesian Imitation Learning with Logic over Programs pdf

Leslie Pack Kaelbling, Joshua B. Tenenbaum, Tom Silver, 2019, ArXiv.

Thinking and Guessing: Bayesian and Empirical Models of How Humans Search

Joshua B. Tenenbaum, Tomer Ullman, Marta Kryven, 2017, CogSci.

Combining Physical Simulators and Object-Based Networks for Control

Jiajun Wu, Nima Fazeli, Maria Bauza, 2019, 2019 International Conference on Robotics and Automation (ICRA).

in Adv. in Neural Info. Proc. Systems, volume 9, MIT Press, 1997.

J. Tenenbaum, W. Freeman, 1996 .

Learning annotated hierarchies from relational data

Joshua B. Tenenbaum, Charles Kemp, Daniel M. Roy, 2006, NIPS.

Augmenting Policy Learning with Routines Discovered from a Single Demonstration pdf

Chuang Gan, Jiajun Wu, Xiaoxiao Guo, 2020, AAAI.

Approaching human 3D shape perception with neurally mappable models

J. Tenenbaum, N. Kanwisher, Thomas P. O’Connell, 2023, ArXiv.

Generalization , Similarity , and Bayesian Inference

J. Tenenbaum, T. Griffiths, 2000 .

Surprise! Infants consider possible bases of generalization for a single input example.

LouAnn Gerken, Josh Tenenbaum, Colin Dawson, 2014, Developmental science.

Unsupervised Compositional Concepts Discovery with Text-to-Image Generative Models

J. Tenenbaum, A. Torralba, Yilun Du, 2023, 2023 IEEE/CVF International Conference on Computer Vision (ICCV).

Combining causal and similarity-based reasoning

Joshua B. Tenenbaum, Charles Kemp, Patrick Shafto, 2006, NIPS.

Evolutionary implementation of Bayesian computations

Joshua B. Tenenbaum, Dániel Czégel, István Zachar, 2019, bioRxiv.

Inferring Relational Potentials in Interacting Systems

J. Tenenbaum, M. Sznaier, Yilun Du, 2023, ICML.

DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model

Vikash K. Mansinghka, J. Tenenbaum, Yasemin Bekiroglu, 2022, 2022 International Conference on Robotics and Automation (ICRA).

Pure reasoning in 12-month-old infants as probabilistic inference.

J. Tenenbaum, E. Vul, V. Girotto, 2011, Science.

Non�?Parametric Bayesian Inference of Strategies in Repeated Games

Joshua B. Tenenbaum, Max Kleiman-Weiner, Penghui Zhou, 2018, The Econometrics Journal.

Planning with Large Language Models for Code Generation

J. Tenenbaum, Chuang Gan, Mingyu Ding, 2023, ICLR.

The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence

James A. Evans, Felipe S. Abrahão, Christopher K. I. Williams, 2023, ArXiv.

Beating the World's Best at Super Smash Bros. with Deep Reinforcement Learning

William F. Whitney, J. Tenenbaum, Vlad Firoiu, 2017, 1702.06230.

McGovern Institute for Brain Research

Mark T. Harnett, J. Tenenbaum, D. Wolpert, 2005 .

Program-Guided Image Manipulators

Jiajun Wu, Joshua B. Tenenbaum, William T. Freeman, 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

Sampling for Bayesian Program Learning

Armando Solar-Lezama, Joshua B. Tenenbaum, Kevin Ellis, 2016, NIPS.

Encoding higher-order structure in visual working memory: A probabilistic model

Timothy F. Brady, J. Tenenbaum, 2010 .

Joshua B. Tenenbaum, Samuel Gershman, J. Tenenbaum, 2015, CogSci.

Learning style translation for the lines of a drawing

Joshua B. Tenenbaum, William T. Freeman, Egon C. Pasztor, 2003, TOGS.

Empowerment contributes to exploration behaviour in a creative video game

J. Tenenbaum, S. Gershman, Eric Schulz, 2023, Nature Human Behaviour.

Intuitive experimentation in the physical world

Neil R. Bramley, J. Tenenbaum, Tobias Gerstenberg, 2018, Cognitive Psychology.

Modeling human performance in statistical word segmentation

Michael C. Frank, J. Tenenbaum, T. Griffiths, 2010, Cognition.

Theory-based causal induction.

J. Tenenbaum, T. Griffiths, 2009, Psychological review.

The discovery of structural form

J. Tenenbaum, Charles Kemp, 2008, Proceedings of the National Academy of Sciences.

Learning overhypotheses with hierarchical Bayesian models.

J. Tenenbaum, Charles Kemp, A. Perfors, 2007, Developmental science.

Topics in Semantic Representation

J. Tenenbaum, T. Griffiths, M. Steyvers, 2007 .

Structured Priors for Structure Learning

Shan Gao, Zhi Geng, Vikash K. Mansinghka, 2006, UAI.

Lucky or clever? From expectations to responsibility judgments

J. Tenenbaum, Tobias Gerstenberg, T. Ullman, 2018, Cognition.

Alex Pentland, Joshua B. Tenenbaum, Peter Krafft, 2014, ArXiv.

DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools

J. Tenenbaum, David Held, Yunzhu Li, 2022, ICLR.

A critical period for second language acquisition: Evidence from 2/3 million English speakers

Joshua K. Hartshorne, J. Tenenbaum, S. Pinker, 2018, Cognition.

The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth

J. Tenenbaum, M. Steyvers, 2005, Cogn. Sci..

A Rational Analysis of Rule-Based Concept Learning

Noah D. Goodman, J. Tenenbaum, T. Griffiths, 2008, Cogn. Sci..

Learning to Learn Causal Models

Noah D. Goodman, J. Tenenbaum, Charles Kemp, 2010, Cogn. Sci..

A counterfactual simulation model of causal judgments for physical events.

Noah D. Goodman, J. Tenenbaum, Tobias Gerstenberg, 2020, Psychological review.

Eye-Tracking Causality

Noah D. Goodman, J. Tenenbaum, Tobias Gerstenberg, 2017, Psychological science.

Black boxes: Hypothesis testing via indirect perceptual evidence

Joshua B. Tenenbaum, Laura Schulz, Max H. Siegel, 2014, CogSci.

Probabilistic Programming Bots in Intuitive Physics Game Play

J. Tenenbaum, Fahad Alhasoun, Sarah Alnegheimish, 2021, AAAI.

Towards Human-Level Learning of Complex Physical Puzzles pdf

Joshua B. Tenenbaum, Kevin A. Smith, Devesh K. Jha, 2020, ArXiv.

Probabilistic models of cognition: exploring representations and inductive biases

J. Tenenbaum, T. Griffiths, Charles Kemp, 2010, Trends in Cognitive Sciences.

Journal of Experimental Psychology : General Predicting the Future as Bayesian Inference : People Combine Prior Knowledge With Observations When Estimating Duration and Extent

J. Tenenbaum, T. Griffiths, 2011 .

NeuSE: Neural SE(3)-Equivariant Embedding for Consistent Spatial Understanding with Objects

J. Tenenbaum, Yilun Du, Kurran Singh, 2023, Robotics: Science and Systems.

Using Physical Theories to Infer Hidden Causal Structure

J. Tenenbaum, T. Griffiths, Elizabeth Baraff, 2004 .

3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation

Vikash K. Mansinghka, J. Tenenbaum, Guangyao Zhou, 2023, 2302.03744.

Neural Amortized Inference for Nested Multi-agent Reasoning

J. Tenenbaum, Tianmin Shu, Yen-Ling Kuo, 2023, ArXiv.

NOPA: Neurally-guided Online Probabilistic Assistance for Building Socially Intelligent Home Assistants

J. Tenenbaum, A. Torralba, Xavier Puig, 2023, 2023 IEEE International Conference on Robotics and Automation (ICRA).

Evidence that the Brain’s Physics Engine Runs Forward Simulations of What will Happen Next

Nancy Kanwisher, Josh Tenenbaum, Michael A. Cohen, 2020 .

Sensitivity to Sampling in Bayesian Word Learning

J. Tenenbaum, Fei Xu, Publishing Blackwell, 2022 .

Modeling Human Performance on Statistical Word Segmentation Tasks

Michael C. Frank, Vikash K. Mansinghka, J. Tenenbaum, 2007 .

Dynamic Infinite Relational Model for Time-varying Relational Data Analysis

Naonori Ueda, Tomoharu Iwata, Joshua B. Tenenbaum, 2010, NIPS.

Is Conditional Generative Modeling all you need for Decision-Making?

J. Tenenbaum, T. Jaakkola, Pulkit Agrawal, 2022, ICLR.

Neurocomputational Modeling of Human Physical Scene Understanding

Jiajun Wu, Joshua B. Tenenbaum, Kevin A. Smith, 2018 .

Why blame Bob ? Probabilistic generative models , counterfactual reasoning , and blame attribution

J. Tenenbaum, Tobias Gerstenberg, T. Ullman, 2012 .

Probabilistic generative models for counterfactual reasoning and blame attribution

Joshua B. Tenenbaum, John McCoy, Tomer Ullman, 2012, CogSci.

Building 3D Morphable Models from a Single Scan

Joshua Tenenbaum, Skylar Sutherland, Bernhard Egger, 2020, 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).

Hypothesis-Space Constraints in Causal Learning

Joshua B. Tenenbaum, Laura Schulz, Pedro Tsividis, 2015, CogSci.

Approaching human 3D shape perception with neurally mappable models

J. Tenenbaum, N. Kanwisher, Thomas P. O’Connell, 2023, ArXiv.

Vector-based pedestrian navigation in cities

J. Tenenbaum, A. Rizzo, C. Ratti, 2021, Nature Computational Science.

Theory Acquisition as Stochastic Search

Joshua B. Tenenbaum, Noah D. Goodman, Tomer Ullman, 2010 .

Author ' s personal copy Bootstrapping in a language of thought : A formal model of numerical concept learning

Noah D. Goodman, J. Tenenbaum, S. Piantadosi, 1997 .

Three ideal observer models for rule learning in simple languages

Michael C. Frank, J. Tenenbaum, 2011, Cognition.

HandMeThat: Human-Robot Communication in Physical and Social Environments

J. Tenenbaum, Jiayuan Mao, Yanming Wan, 2023, NeurIPS.

Are Deep Neural Networks SMARTer Than Second Graders?

Kevin A. Smith, J. Tenenbaum, A. Cherian, 2022, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Indirect Evidence and the Poverty of the Stimulus: The Case of Anaphoric One

J. Tenenbaum, T. Regier, Stephani Foraker, 2009, Cogn. Sci..