Joshua B. Tenenbaum

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

One-shot Learning and Classification in Children

Joshua B. Tenenbaum, Brenden M. Lake, Eliza Kosoy, 2017, CogSci.

Learning What is Where from Social Observations

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

Modeling Human Ad Hoc Coordination

Alex Pentland, Joshua B. Tenenbaum, Peter Krafft, 2016, AAAI.

Visual Grounding of Learned Physical Models pdf

Daniel L. K. Yamins, Jiajun Wu, Joshua B. Tenenbaum, 2020, ICML.

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

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

Efficient inverse graphics in biological face processing

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

Representing Partial Programs with Blended Abstract Semantics pdf

Armando Solar-Lezama, Jacob Andreas, Joshua B. Tenenbaum, 2020, ArXiv.

Multistability and Perceptual Inference

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

Modeling human intuitions about liquid flow with particle-based simulation

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

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

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

Error-Driven Stochastic Search for Theories and Concepts

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

Wins above replacement: Responsibility attributions as counterfactual replacements

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

Learning sequential patterns from graphical programs

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

Global Versus Local Methods in Nonlinear Dimensionality Reduction

Joshua B. Tenenbaum, Vin de Silva, J. Tenenbaum, 2002, NIPS.

Context-Sensitive Induction

Joshua B. Tenenbaum, Charles Kemp, Patrick Shafto, 2005 .

Learning to Learn Visual Object Categories by Integrating Deep Learning with Hierarchical Bayes

Joshua B. Tenenbaum, Andres Campero, Andrew Francl, 2017, CogSci.

Beyond Boolean logic: exploring representation languages for learning complex concepts

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

Learning to learn generative programs with Memoised Wake-Sleep pdf

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

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

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

Dynamic Infinite Relational Model for Time-varying Relational Data Analysis

Naonori Ueda, Tomoharu Iwata, Joshua B. Tenenbaum, 2010, NIPS.

Learning individual words and learning about words simultaneously

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

Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids pdf

Jiajun Wu, Joshua B. Tenenbaum, Antonio Torralba, 2018, ICLR.

Interpreting actions by attributing compositional desires

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

Library learning for neurally-guided Bayesian program induction

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

Modelling Relational Data using Bayesian Clustered Tensor Factorization

Joshua B. Tenenbaum, Ruslan Salakhutdinov, Ilya Sutskever, 2009, NIPS.

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

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

A probabilistic model of cross-categorization

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

Hierarchical Topic Models and the Nested Chinese Restaurant Process

Thomas L. Griffiths, Michael I. Jordan, Joshua B. Tenenbaum, 2003, NIPS.

Assessing the Perceived Predictability of Functions

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

Seeing Tree Structure from Vibration pdf

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

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.

Integrating Topics and Syntax

Thomas L. Griffiths, Joshua B. Tenenbaum, David M. Blei, 2004, NIPS.

Learning to Exploit Stability for 3D Scene Parsing

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

Learning to Reconstruct Shapes from Unseen Classes

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

Principles of Generalization for Learning Sequential Structure in Language

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

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..

DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs

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

3D-Aware Scene Manipulation via Inverse Graphics

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

Human-level concept learning through probabilistic program induction

Joshua B. Tenenbaum, Ruslan Salakhutdinov, Brenden M. Lake, 2015, Science.

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).

Learning Structured Preferences

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

Residual Policy Learning pdf

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

Understanding "almost": Empirical and computational studies of near misses

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

Factorial Learning by Clustering Features

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

Learning and using relational theories

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

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

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

Learning with AMIGo: Adversarially Motivated Intrinsic Goals pdf

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

Word learning and the acquisition of syntactic-semantic overhypotheses pdf

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

CLEVRER: CoLlision Events for Video REpresentation and Reasoning pdf

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

A rational model of syntactic bootstrapping

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

Productivity and Reuse in Language

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

On the Units of GANs (Extended Abstract)

Joshua B. Tenenbaum, William T. Freeman, David Bau, 2019 .

Probing the Compositionality of Intuitive Functions

Joshua B. Tenenbaum, David Duvenaud, Samuel Gershman, 2016, NIPS.

Dimensionality Reduction via Program Induction

Joshua B. Tenenbaum, Eyal Dechter, Kevin Ellis, 2015, AAAI Spring Symposia.

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

Learning Structured Generative Concepts

Joshua B. Tenenbaum, Noah D. Goodman, Andreas Stuhlmüller, 2010 .

Semi-Supervised Learning with Trees

Thomas L. Griffiths, Joshua B. Tenenbaum, Charles Kemp, 2003, NIPS.

One-shot learning by inverting a compositional causal process

Joshua B. Tenenbaum, Ruslan Salakhutdinov, Brenden M. Lake, 2013, NIPS.

Inference of Intention and Permissibility in Moral Decision Making

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

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

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

Responsibility judgments in voting scenarios

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

Outcome or Strategy? A Bayesian Model of Intelligence Attribution

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

Separating Style and Content with Bilinear Models

Joshua B. Tenenbaum, William T. Freeman, J. Tenenbaum, 2000, Neural Computation.

Learning to share visual appearance for multiclass object detection

Joshua B. Tenenbaum, Antonio Torralba, Ruslan Salakhutdinov, 2011, CVPR 2011.

ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation pdf

Chuang Gan, Joshua B. Tenenbaum, David Cox, 2020, ArXiv.

Modeling Social Common Sense for Seamless Human Machine Teaming: Inverting the Intuitive Game Engine with Probabilistic Programming

Joshua B. Tenenbaum, Chris I. Baker, Tao Gao, 2017 .

Learning style translation for the lines of a drawing

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

Exploiting compositionality to explore a large space of model structures

Joshua B. Tenenbaum, William T. Freeman, Ruslan Salakhutdinov, 2012, UAI.

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

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

One-shot learning of generative speech concepts

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

Exact and Approximate Sampling by Systematic Stochastic Search

Joshua B. Tenenbaum, Daniel M. Roy, Vikash K. Mansinghka, 2009, AISTATS.

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

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

Structured cognitive representations and complex inference in neural systems

Alexandre Pouget, Peter Dayan, Joshua B. Tenenbaum, 2013, CogSci.

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

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

One-Shot Learning with a Hierarchical Nonparametric Bayesian Model

Joshua B. Tenenbaum, Antonio Torralba, Ruslan Salakhutdinov, 2011, ICML Unsupervised and Transfer Learning.

Church: a language for generative models

Joshua B. Tenenbaum, Noah D. Goodman, Daniel M. Roy, 2008, UAI.

A First Step in Combining Cognitive Event Features and Natural Language Representations to Predict Emotions pdf

Joshua B. Tenenbaum, Rebecca Saxe, Bjarke Felbo, 2017, ArXiv.

Auditory scene analysis as Bayesian inference in sound source models

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

The Role of Causal Models in Statistical Reasoning

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

The acquisition of inductive constraints

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

Deep Audio Priors Emerge From Harmonic Convolutional Networks

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

Concept learning as motor program induction: A large-scale empirical study

Joshua B. Tenenbaum, Ruslan Salakhutdinov, Brenden M. Lake, 2012, CogSci.

Learning Systems of Concepts with an Infinite Relational Model

Thomas L. Griffiths, Naonori Ueda, Joshua B. Tenenbaum, 2006, AAAI.

Sparse multidimensional scaling using land-mark points

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

Learning abstract structure for drawing by efficient motor program induction

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

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

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

A Unified Approach to Abductive Inference

Henry Kautz, Thomas G. Dietterich, Joshua B. Tenenbaum, 2014 .

Learning to Learn with Compound HD Models

Joshua B. Tenenbaum, Antonio Torralba, Ruslan Salakhutdinov, 2011, NIPS.

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

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

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).

Learning to Share and Hide Intentions using Information Regularization

Joshua B. Tenenbaum, Matthew Botvinick, Max Kleiman-Weiner, 2018, NeurIPS.

Understanding Visual Concepts with Continuation Learning pdf

Joshua B. Tenenbaum, William F. Whitney, Tejas D. Kulkarni, 2016, ArXiv.

Learning annotated hierarchies from relational data

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

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

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

Unsupervised Learning of Latent Physical Properties Using Perception-Prediction Networks

Jiajun Wu, Joshua B. Tenenbaum, David Zheng, 2018, UAI.

Sampling for Bayesian Program Learning

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

Formal Approaches in Categorization: Nonparametric Bayesian models of categorization

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

Building Theories of the World: Human and Machine Learning Perspectives

Joshua B. Tenenbaum, 2008, ILP.

Simulation and heuristics in flexible tool use

Joshua B. Tenenbaum, Kevin A. Smith, Kelsey R. Allen, 2017, CogSci.

The Role of Causal Models in Reasoning Under Uncertainty

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

Fragment Grammars: Exploring Computation and Reuse in Language

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

Learning list concepts through program induction

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

One and Done? Optimal Decisions From Very Few Samples

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

Learning to Learn Causal Models

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

Modeling Human Performance on Statistical Word Segmentation Tasks

Michael C. Frank, Thomas L. Griffiths, Joshua B. Tenenbaum, 2007 .

Learning to Describe Scenes with Programs

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

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

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

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

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

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

Marc Toussaint, Joshua B. Tenenbaum, Kevin A. Smith, 2019, IJCAI.

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).

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

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

Logical Rule Induction and Theory Learning Using Neural Theorem Proving pdf

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

Relational inductive bias for physical construction in humans and machines pdf

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

Coalescing the Vapors of Human Experience into a Viable and Meaningful Comprehension

Joshua B. Tenenbaum, Samuel Gershman, Tomer Ullman, 2016, CogSci.

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

Joshua B. Tenenbaum, Ardavan Saeedi, Karthik Narasimhan, 2016, NIPS.

From counterfactual simulation to causal judgment

Joshua B. Tenenbaum, David A. Lagnado, Noah D. Goodman, 2014, CogSci.

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

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

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

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

Causal and compositional generative models in online perception

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

Discovering a symbolic planning language from continuous experience

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

Compositional Inductive Biases in Function Learning

Samuel J. Gershman, Joshua B. Tenenbaum, David Duvenaud, 2016, bioRxiv.

Structure Discovery in Nonparametric Regression through Compositional Kernel Search pdf

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

The Isomap Algorithm and Topological Stability

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

Computational Models of Intuitive Physics

Joshua B. Tenenbaum, David A. Lagnado, Kenneth D. Forbus, 2012, CogSci.

Theory Acquisition as Stochastic Search

Joshua B. Tenenbaum, Noah D. Goodman, Tomer Ullman, 2010 .

Subjective randomness as statistical inference

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

Query-guided visual search

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

Theory-Based Induction

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

Nonparametric Bayesian Policy Priors for Reinforcement Learning

Joshua B. Tenenbaum, Nicholas Roy, Finale Doshi-Velez, 2010, NIPS.

Simplicity and Probability in Human Judgment

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

Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs

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

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

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

Grammar-based object representations in a scene parsing task

Joshua B. Tenenbaum, Virginia Savova, Frank Jaekel, 2009 .

Theory of Minds: Understanding Behavior in Groups Through Inverse Planning

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

Not so innocent: Reasoning about costs, competence, and culpability in very early childhood

Joshua B. Tenenbaum, Julian Jara-Ettinger, Laura Schulz, 2013, CogSci.

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 .

Infinite Dynamic Bayesian Networks

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

Learning as program induction

Joshua B. Tenenbaum, Fei Xu, Eric Schulz, 2018, CogSci.

DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs

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

Learning the Structure of Similarity

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

Causal inference in sensorimotor integration

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

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

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

Inverse Graphics with Probabilistic CAD Models pdf

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

Flexible Neural Representation for Physics Prediction

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

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

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

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).

Real-time inference of physical properties in dynamic scenes

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

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

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

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

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

Inferring mass in complex physical scenes via probabilistic simulation

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

GAN Dissection: Visualizing and Understanding Generative Adversarial Networks pdf

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

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

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

Learning physical theories from dynamical scenes

Joshua B. Tenenbaum, Noah D. Goodman, Tomer Ullman, 2014, CogSci.

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

Joshua B. Tenenbaum, William F. Whitney, Vlad Firoiu, 2017, ArXiv.

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

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

Hypothesis-Space Constraints in Causal Learning

Joshua B. Tenenbaum, Laura Schulz, Pedro Tsividis, 2015, CogSci.

A Computational Model for Combinatorial Generalization in Physical Perception from Sound

Chuang Gan, Jiajun Wu, Joshua B. Tenenbaum, 2019 .

Preschoolers and Infants Calibrate Persistence from Adult Models

Joshua B. Tenenbaum, Laura Schulz, Julia A. Leonard, 2017, CogSci.

3D Interpreter Networks for Viewer-Centered Wireframe Modeling

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

Object-Centric Diagnosis of Visual Reasoning pdf

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

A Probabilistic Model of Meetings That Combines Words and Discourse Features

Thomas L. Griffiths, Joshua B. Tenenbaum, Konrad P. Körding, 2008, IEEE Transactions on Audio, Speech, and Language Processing.

Discovering Structure by Learning Sparse Graphs - eScholarship

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

The Omniglot challenge: a 3-year progress report

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

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..

Feature-based Joint Planning and Norm Learning in Collaborative Games

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

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

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

Inferring Structured Visual Concepts from Minimal Data

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

Discovery and usage of joint attention in images pdf

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

Tiptoeing around it: Inference from absence in potentially offensive speech

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

Help or Hinder: Bayesian Models of Social Goal Inference

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

Grounding Compositional Hypothesis Generation in Specific Instances

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

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

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

Inverting Audio-Visual Simulation for Shape and Material Perception

Jiajun Wu, Joshua B. Tenenbaum, William T. Freeman, 2018, CVPR Workshops.

Evolutionary implementation of Bayesian computations

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

A theory of learning to infer

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

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

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

Single Image 3D Interpreter Network pdf

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

Human collective intelligence as distributed Bayesian inference pdf

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

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

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

Joint inferences of belief and desire from facial expressions

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

Author response: Invariant representations of mass in the human brain

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

A Rational Analysis of Rule-Based Concept Learning

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

From Algorithmic to Subjective Randomness

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

At Human Speed: Deep Reinforcement Learning with Action Delay pdf

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

Noisy Newtons: Unifying process and dependency accounts of causal attribution

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

Learning a novel rule-based conceptual system

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

Edge replacement and nonindependence in causation

Joshua B. Tenenbaum, David M. Sobel, David W. Buchanan, 2010 .

The Neuro-Symbolic Concept Learner: Interpreting Scenes Words and Sentences from Natural Supervision pdf

Chuang Gan, Jiajun Wu, Joshua B. Tenenbaum, 2019, ICLR.

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

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

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

Latent Predicate Networks: Concept Learning with Probabilistic Context-Sensitive Grammars

Joshua B. Tenenbaum, Joshua Rule, Eyal Dechter, 2015, AAAI Spring Symposia.

Language & common sense: Integrating across psychology, linguistics, and computer science

Joshua B. Tenenbaum, Joshua K. Hartshorne, 2015, CogSci.

What happened? Reconstructing the past through vision and sound

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

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

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

Modeling human performance in statistical word segmentation

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

Neurally-Guided Structure Inference

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

Structured models of semantic cognition

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

Minimal Nativism: How does cognitive development get off the ground?

Joshua B. Tenenbaum, Shimon Ullman, Noah D. Goodman, 2013, CogSci.

Cooperative Social Intelligence: Understanding and Acting with Other

Joshua B. Tenenbaum, Yibiao Zhao, Max Kleiman-Weiner, 2017, CogSci.

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

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

Modeling the Perception of Intentions

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

Bayesian Modeling of Human Concept Learning

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

Rational Inference of Beliefs and Desires From Emotional Expressions

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

Theory-Based Causal Inference

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

Using Vocabulary Knowledge in Bayesian Multinomial Estimation

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

Bayesian Theory of Mind: Modeling Joint Belief-Desire Attribution

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

When circumstances change, update your pronouns

Joshua B. Tenenbaum, Joshua K. Hartshorne, Tobias Gerstenberg, 2019, CogSci.

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).

Natively probabilistic computation

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

The Three Pillars of Machine-Based Programming pdf

Armando Solar-Lezama, Regina Barzilay, Joshua B. Tenenbaum, 2018, ArXiv.

Learning Physical Graph Representations from Visual Scenes pdf

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

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

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

Human Learning in Atari

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

Compositionality in rational analysis

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

Bias reformulation for one-shot function induction

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

Higher order inference in verb argument structure acquisition

Joshua B. Tenenbaum, Amy Perfors, Elizabeth Wonnacott, 2008 .

A Computational Model of Commonsense Moral Decision Making

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

Forward Physics: How people learn and generalize novel dynamical models

Joshua B. Tenenbaum, Noah D. Goodman, Tomer Ullman, 2011, CogSci.

Learning Cross-cutting Systems of Categories - eScholarship

Joshua B. Tenenbaum, Patrick Shafto, Matthew Gordon, 2006 .

Emergent Collective Sensing in Human Groups

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

Humans predict liquid dynamics using probabilistic simulation

Joshua B. Tenenbaum, Peter W. Battaglia, Ilker Yildirim, 2015, CogSci.

Online Bayesian Goal Inference for Boundedly-Rational Planning Agents pdf

Joshua B. Tenenbaum, Tom Silver, Vikash K. Mansinghka, 2020, NeurIPS.

Bayesian vector analysis and the perception of hierarchical motion

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

Non�?Parametric Bayesian Inference of Strategies in Repeated Games

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

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

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

Probabilistic generative models for counterfactual reasoning and blame attribution

Joshua B. Tenenbaum, John McCoy, Tomer Ullman, 2012, CogSci.

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).

Acquiring and Exploiting Rich Causal Models for Robust Decision Making

Leslie Pack Kaelbling, Joshua B. Tenenbaum, Michael L. Littman, 2012 .

Few-Shot Bayesian Imitation Learning with Logic over Programs pdf

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

Parametric Embedding for Class Visualization

Thomas L. Griffiths, Naonori Ueda, Tomoharu Iwata, 2004, Neural Computation.

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

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

Discovering Latent Classes in Relational Data

Thomas L. Griffiths, Joshua B. Tenenbaum, Charles Kemp, 2004 .

Computational, Cognitive, and Neural Models of Decision-making Biases

Nick Chater, Peter Dayan, Joshua B. Tenenbaum, 2012, CogSci.

The Evolution of Cooperation in Cognitively Flexible Agents

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

The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology

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

Learning to Infer Program Sketches pdf

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

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.

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).

Learning Causal Laws

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

The Cognitive Mechanisms of Contractualist Moral Decision-Making

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

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.

Running head : LEARNING OVERHYPOTHESES Learning overhypotheses with hierarchical Bayesian models

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

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.

Bayesian Models of Inductive Generalization

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

Learning Libraries of Subroutines for Neurally-Guided Bayesian Program Induction

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

The effects of object motion observations on physical prediction

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

Information Selection in Noisy Environments with Large Action Spaces

Joshua B. Tenenbaum, Laura Schulz, Samuel Gershman, 2014, CogSci.

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.

Visualizing and Understanding Generative Adversarial Networks (Extended Abstract) pdf

Bolei Zhou, Joshua B. Tenenbaum, Antonio Torralba, 2019, ArXiv.

Softstar: Heuristic-Guided Probabilistic Inference

Joshua B. Tenenbaum, Patrick Lucey, Brenden M. Lake, 2015, NIPS.

Computational Creativity: Generating new objects with a hierarchical Bayesian model

Joshua B. Tenenbaum, Brenden M. Lake, 2014, 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.

Strategies and representations in physical inference

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

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

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

Probabilistic internal physics models guide judgments about object dynamics

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

Risk and Regret of Hierarchical Bayesian Learners pdf

Joshua B. Tenenbaum, Jonathan H. Huggins, J. Tenenbaum, 2015, ICML.

Unsupervised Learning by Program Synthesis

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

Draping an Elephant: Uncovering Children's Reasoning About Cloth-Covered Objects

Joshua B. Tenenbaum, Elizabeth S. Spelke, Tomer Ullman, 2019, CogSci.

Learning to Infer Graphics Programs from Hand-Drawn Images

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

Discovering Syntactic Hierarchies

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

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.

Structure Learning in Human Causal Induction

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

Learning with Hierarchical-Deep Models

Joshua B. Tenenbaum, Antonio Torralba, Ruslan Salakhutdinov, 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Faulty Towers: A hypothetical simulation model of physical support

Joshua B. Tenenbaum, Kevin A. Smith, Liang Zhou, 2017, CogSci.

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

Chuang Gan, Jiajun Wu, Joshua B. Tenenbaum, 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

Physical Primitive Decomposition

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

Compositional inductive biases in function learning

Samuel J. Gershman, Joshua B. Tenenbaum, David Duvenaud, 2016, Cognitive Psychology.

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

Joshua B. Tenenbaum, Tomer Ullman, 2020 .

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.

The Naïve Utility Calculus unifies spatial and statistical routes to preference

Joshua B. Tenenbaum, Laura Schulz, Julian Jara-Ettinger, 2016, CogSci.

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.

Stochastic Prediction of Multi-Agent Interactions from Partial Observations pdf

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

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

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

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

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

Combining causal and similarity-based reasoning

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

Abstract knowledge guides search and prediction in novel situations

Joshua B. Tenenbaum, Chris I. Baker, Patrick Shafto, 2009 .

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.

Perceptual Multistability as Markov Chain Monte Carlo Inference

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

Discovering hierarchical motion structure

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

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).

Evidence for an Intuitive Physics Engine in the Human Brain

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

Some specifics about generalization

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

Modeling Semantic Cognition as Logical Dimensionality Reduction

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

Tracking hidden objects with efficient physical prediction

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

Self-Supervised Intrinsic Image Decomposition

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

The Infinite Latent Events Model

Joshua B. Tenenbaum, Noah D. Goodman, Daniel M. Roy, 2009, UAI.

Learning bilinear models for two-factor problems in vision

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

Bayesian Approaches to Cognitive Modeling

Joshua B. Tenenbaum, Michael C. Mozer, 2000 .

Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

Jiajun Wu, Joshua B. Tenenbaum, Chengkai Zhang, 2016, NIPS.

Informative communication in word production and word learning

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

Neural representation of the intuitive physical dimension of mass

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

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).

Write, Execute, Assess: Program Synthesis with a REPL

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

A Compositional Object-Based Approach to Learning Physical Dynamics pdf

Joshua B. Tenenbaum, Antonio Torralba, Tomer Ullman, 2016, ICLR.

Visualizing and Understanding GANs

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

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

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

The three pillars of machine programming

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

Natural science: Active learning in dynamic physical microworlds

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

Towards Human-Level Learning of Complex Physical Puzzles pdf

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

Bayesian Models of Inductive Learning

Thomas L. Griffiths, Joshua B. Tenenbaum, Charles Kemp, 2006 .

Dynamical Causal Learning

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

Analyzing the view dependence of population codes in inferior temporal cortex

Joshua B. Tenenbaum, Emanuela Bricolo, 1997 .

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.

Neural Scene De-rendering

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

Black boxes: Hypothesis testing via indirect perceptual evidence

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

Extending Rationality

Joshua B. Tenenbaum, Emmanuel M. Pothos, James M. Yearsley, 2019, CogSci.

Big Data and Little Learners

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

Modeling Expertise with Neurally-Guided Bayesian Program Induction

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

Bayesian Policy Search with Policy Priors

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

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).

Physical predictions over time

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

Concepts are not beliefs, but having concepts is having beliefs

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

Infinite Mixture Prototypes for Few-Shot Learning

Joshua B. Tenenbaum, Kelsey R. Allen, Evan Shelhamer, 2019, ICML.

Learning to Infer and Execute 3D Shape Programs pdf

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

Foley Music: Learning to Generate Music from Videos

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

Intuitive psychophysics: Children's exploratory play quantitatively tracks the discriminability of alternative hypotheses

Joshua B. Tenenbaum, Laura Schulz, Max H. Siegel, 2017, CogSci.

One shot learning of simple visual concepts

Joshua B. Tenenbaum, Ruslan Salakhutdinov, Brenden M. Lake, 2011, CogSci.

Automatic Construction and Natural-Language Description of Nonparametric Regression Models

Joshua B. Tenenbaum, Zoubin Ghahramani, Roger B. Grosse, 2014, AAAI.

Rational Process Models

Thomas L. Griffiths, Roger Levy, Joshua B. Tenenbaum, 2009 .

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

Sanja Fidler, Joshua B. Tenenbaum, Tianmin Shu, 2020, ArXiv.

Unsupervised Topic Modelling for Multi-Party Spoken Discourse

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

Multiple timescales and uncertainty in motor adaptation

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

Inductive Biases in the Evolution of Combinatorial Structure in Language

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

Shape and Material from Sound

Jiajun Wu, Joshua B. Tenenbaum, Bill Freeman, 2017, NIPS.

Bayesian models of human action understanding

Joshua B. Tenenbaum, Rebecca Saxe, Chris L. Baker, 2005, NIPS.

Unsupervised Discovery of Parts, Structure, and Dynamics pdf

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

Neurocomputational Modeling of Human Physical Scene Understanding

Jiajun Wu, Joshua B. Tenenbaum, Kevin A. Smith, 2018 .

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

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

MarrNet: 3D Shape Reconstruction via 2.5D Sketches

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

Intuitive mechanics in visual reasoning about complex scenes with unknown forces

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

When Computer Vision Gazes at Cognition pdf

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

Intuitive physics judgments guided by probabilistic dynamics model

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

Optimal Predictions in Everyday

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

The Emergence of Organizing Structure in Conceptual Representation

Neil D. Lawrence, Joshua B. Tenenbaum, Brenden M. Lake, 2018, Cognitive science.

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).

Learning Compositional Rules via Neural Program Synthesis pdf

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

Productivity and Reuse in Language: a Developmental Study

Joshua B. Tenenbaum, Noah D. Goodman, Jesse Snedeker, 2011, CogSci.

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

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

Building machines that learn and think like people

Joshua B. Tenenbaum, Brenden M. Lake, Tomer D. Ullman, 2016, Behavioral and Brain Sciences.

Moral Dynamics: A Computational Model of Moral Judgment

Joshua B. Tenenbaum, Samuel Gershman, Tomer Ullman, 2018, CogSci.

Physics 101: Learning Physical Object Properties from Unlabeled Videos

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

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).

How, whether, why: Causal judgments as counterfactual contrasts

Joshua B. Tenenbaum, David A. Lagnado, Noah D. Goodman, 2015, CogSci.

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

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

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 .

The Development of Joint Belief-Desire Inferences

Joshua B. Tenenbaum, Rebecca Saxe, Hilary Richardson, 2012, CogSci.

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..

Choosing the unimaginable: Social psychological factors in seeking transformative experiences

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

Inferring causal networks from observations and interventions

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

Theory-Based Bayesian Models of Inductive Inference

Joshua B. Tenenbaum, 2010 .

Go fishing! Responsibility judgments when cooperation breaks down

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

Unsupervised Discovery of 3D Physical Objects from Video pdf

Joshua B. Tenenbaum, Kevin A. Smith, Yilun Du, 2021, ICLR 2021 Poster.

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

Joshua B. Tenenbaum, Kwan-Yee Kenneth Wong, Jiajun Wu, 2021, ICLR 2021 Poster.

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

Joshua B. Tenenbaum, Chuang Gan, Zhiao Huang, 2021, ICLR 2021 Spotlight.

Representing Partial Programs with Blended Abstract Semantics pdf

Joshua B. Tenenbaum, Jacob Andreas, Maxwell Nye, 2021, ICLR 2021 Poster.

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

Joshua B. Tenenbaum, Sanja Fidler, Xavier Puig, 2021, ICLR 2021 Spotlight.

Learning Task Decomposition with Ordered Memory Policy Network pdf

Joshua B. Tenenbaum, Chuang Gan, Aaron Courville, 2021, ICLR 2021 Poster.

Learning with AMIGo: Adversarially Motivated Intrinsic Goals pdf

Joshua B. Tenenbaum, Tim Rocktäschel, Andres Campero, 2021, ICLR 2021 Poster.

DreamCoder software and data

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

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.

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.

Learning Signal-Agnostic Manifolds of Neural Fields pdf

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

Temporal and Object Quantification Networks

Leslie Pack Kaelbling, Joshua B. Tenenbaum, Jiayuan Mao, 2021, IJCAI.

Single-Shot Scene Reconstruction

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

3DP3: 3D Scene Perception via Probabilistic Programming pdf

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

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

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

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

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

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

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

AGENT: A Benchmark for Core Psychological Reasoning

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

Learning with AMIGo: Adversarially Motivated Intrinsic Goals pdf

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

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.

Learning Task Decomposition with Ordered Memory Policy Network pdf

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

Physion: Evaluating Physical Prediction from Vision in Humans and Machines pdf

Nancy Kanwisher, Li Fei-Fei, Joshua B. Tenenbaum, 2021, ArXiv.

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).

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).

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

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

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

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

Augmenting Policy Learning with Routines Discovered from a Single Demonstration

Joshua B. Tenenbaum, Zelin Zhao, Xiaoxiao Guo, 2021, AAAI.

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

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

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

Samuel J. Gershman, Joshua B. Tenenbaum, Kevin Ellis, 2021, ArXiv.

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).

Communicating Natural Programs to Humans and Machines pdf

Joshua B. Tenenbaum, Michael Henry Tessler, Marta Kryven, 2021, ArXiv.

Incorporating Rich Social Interactions Into MDPs pdf

Joshua B. Tenenbaum, Tianmin Shu, Dan Gutfreund, 2021, ArXiv.

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

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

Leveraging Language to Learn Program Abstractions and Search Heuristics

Jacob Andreas, Joshua B. Tenenbaum, Kevin Ellis, 2021, ICML.

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

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

Learning Neuro-Symbolic Relational Transition Models for Bilevel Planning pdf

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

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

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

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

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

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

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

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

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

Adventures of human planners in Maze Search Task

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

Learning to solve complex tasks by growing knowledge culturally across generations

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

Learning to Compose Visual Relations pdf

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

Unsupervised Learning of Compositional Energy Concepts pdf

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

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

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

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

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

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

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

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).

Online learning of symbolic concepts

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

A Generative Theory of Similarity

Joshua B. Tenenbaum, Charles Kemp, J. Tenenbaum, 2005 .

Three ideal observer models for rule learning in simple languages

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

Inferring the intentional states of autonomous virtual agents

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

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.

Continuity of Discourse Provides Information for Word Learning

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

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

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

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

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

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

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

A Computational Model for Combinatorial Generalization in Physical Auditory Perception

Chuang Gan, Jiajun Wu, Joshua B. Tenenbaum, 2019 .

Inductive reasoning about causally transmitted properties

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

Lucky or clever? From expectations to responsibility judgments

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

Physics knowledge aids object perception in dynamic scenes

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

Inductive Reasoning: Theory-Based Bayesian Models of Inductive Reasoning

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

Chapter Six – Categorization as Causal Explanation: Discounting and Augmenting in a Bayesian Framework

Joshua B. Tenenbaum, Daniel M. Oppenheimer, Tevye R. Krynski, 2013 .

ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation pdf

Chuang Gan, Joshua B. Tenenbaum, Jonas Kubilius, 2020, ArXiv.