Factorial Learning by Clustering Features. - Joshua B. Tenenbaum; Emanuel Todorov (NIPS, 1994)

Learning the Structure of Similarity. - Joshua B. Tenenbaum (NIPS, 1995)

Separating Style and Content. - Joshua B. Tenenbaum; William T. Freeman (NIPS, 1996)

Learning bilinear models for two-factor problems in vision. - William T. Freeman; Joshua B. Tenenbaum (CVPR, 1997)

Mapping a Manifold of Perceptual Observations. - Joshua B. Tenenbaum (NIPS, 1997)

Bayesian Modeling of Human Concept Learning. - Joshua B. Tenenbaum (NIPS, 1998)

Rules and Similarity in Concept Learning. - Joshua B. Tenenbaum (NIPS, 1999)

Structure Learning in Human Causal Induction. - Joshua B. Tenenbaum; Thomas L. Griffiths 001 (NIPS, 2000)

Separating Style and Content with Bilinear Models. - Joshua B. Tenenbaum; William T. Freeman (Neural Comput., 2000)

Using Vocabulary Knowledge in Bayesian Multinomial Estimation. - Thomas L. Griffiths 001; Joshua B. Tenenbaum (NIPS, 2001)

Dynamical Causal Learning. - David Danks; Thomas L. Griffiths 001; Joshua B. Tenenbaum (NIPS, 2002)

Bayesian Models of Inductive Generalization. - Neville E. Sanjana; Joshua B. Tenenbaum (NIPS, 2002)

Global Versus Local Methods in Nonlinear Dimensionality Reduction. - Vin de Silva; Joshua B. Tenenbaum (NIPS, 2002)

Theory-Based Causal Inference. - Joshua B. Tenenbaum; Thomas L. Griffiths 001 (NIPS, 2002)

Hierarchical Topic Models and the Nested Chinese Restaurant Process. - David M. Blei; Thomas L. Griffiths 001; Michael I. Jordan; Joshua B. Tenenbaum (NIPS, 2003)

From Algorithmic to Subjective Randomness. - Thomas L. Griffiths 001; Joshua B. Tenenbaum (NIPS, 2003)

Semi-Supervised Learning with Trees. - Charles Kemp; Thomas L. Griffiths 001; Sean Stromsten; Joshua B. Tenenbaum (NIPS, 2003)

Inferring causal networks from observations and interventions. - Mark Steyvers; Joshua B. Tenenbaum; Eric-Jan Wagenmakers; Ben Blum (Cogn. Sci., 2003)

Learning style translation for the lines of a drawing. - William T. Freeman; Joshua B. Tenenbaum; Egon C. Pasztor (ACM Trans. Graph., 2003)

Integrating Topics and Syntax. - Thomas L. Griffiths 001; Mark Steyvers; David M. Blei; Joshua B. Tenenbaum (NIPS, 2004)

Children's causal inferences from indirect evidence: Backwards blocking and Bayesian reasoning in preschoolers. - David M. Sobel; Joshua B. Tenenbaum; Alison Gopnik (Cogn. Sci., 2004)

Bayesian models of human action understanding. - Chris L. Baker; Joshua B. Tenenbaum; Rebecca Saxe (NIPS, 2005)

The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth. - Mark Steyvers; Joshua B. Tenenbaum (Cogn. Sci., 2005)

Learning Systems of Concepts with an Infinite Relational Model. - Charles Kemp; Joshua B. Tenenbaum; Thomas L. Griffiths 001; Takeshi Yamada; Naonori Ueda (AAAI, 2006)

Unsupervised Topic Modelling for Multi-Party Spoken Discourse. - Matthew Purver; Konrad P. Körding; Thomas L. Griffiths 001; Joshua B. Tenenbaum (ACL, 2006)

Combining causal and similarity-based reasoning. - Charles Kemp; Patrick Shafto; Allison Berke; Joshua B. Tenenbaum (NIPS, 2006)

Causal inference in sensorimotor integration. - Konrad P. Körding; Joshua B. Tenenbaum (NIPS, 2006)

Multiple timescales and uncertainty in motor adaptation. - Konrad P. Körding; Joshua B. Tenenbaum; Reza Shadmehr (NIPS, 2006)

Learning annotated hierarchies from relational data. - Daniel M. Roy 001; Charles Kemp; Vikash Mansinghka 001; Joshua B. Tenenbaum (NIPS, 2006)

Structured Priors for Structure Learning. - Vikash K. Mansinghka; Charles Kemp; Thomas L. Griffiths 001; Joshua B. Tenenbaum (UAI, 2006)

A Bayesian Framework for Cross-Situational Word-Learning. - Michael C. Frank; Noah D. Goodman; Joshua B. Tenenbaum (NIPS, 2007)

Learning and using relational theories. - Charles Kemp; Noah D. Goodman; Joshua B. Tenenbaum (NIPS, 2007)

AClass: A simple, online, parallelizable algorithm for probabilistic classification. - Vikash K. Mansinghka; Daniel M. Roy 001; Ryan Rifkin; Joshua B. Tenenbaum (AISTATS, 2007)

Parametric Embedding for Class Visualization. - Tomoharu Iwata; Kazumi Saito; Naonori Ueda; Sean Stromsten; Thomas L. Griffiths 001; Joshua B. Tenenbaum (Neural Comput., 2007)

Building Theories of the World: Human and Machine Learning Perspectives. - Joshua B. Tenenbaum (ILP, 2008)

Church: a language for generative models. - Noah D. Goodman; Vikash K. Mansinghka; Daniel M. Roy 001; Kallista A. Bonawitz; Joshua B. Tenenbaum (UAI, 2008)

A Rational Analysis of Rule-Based Concept Learning. - Noah D. Goodman; Joshua B. Tenenbaum; Jacob Feldman; Thomas L. Griffiths 001 (Cogn. Sci., 2008)

The discovery of structural form. - Charles Kemp; Joshua B. Tenenbaum (Proc. Natl. Acad. Sci. USA, 2008)

A Probabilistic Model of Meetings That Combines Words and Discourse Features. - Mike Dowman; Virginia Savova; Thomas L. Griffiths 001; Konrad P. Körding; Joshua B. Tenenbaum; Matthew Purver (IEEE Trans. Speech Audio Process., 2008)

Perceptual Multistability as Markov Chain Monte Carlo Inference. - Samuel Gershman; Ed Vul; Joshua B. Tenenbaum (NIPS, 2009)

Modelling Relational Data using Bayesian Clustered Tensor Factorization. - Ilya Sutskever; Ruslan Salakhutdinov; Joshua B. Tenenbaum (NIPS, 2009)

Help or Hinder: Bayesian Models of Social Goal Inference. - Tomer D. Ullman; Chris L. Baker; Owen Macindoe; Owain Evans; Noah D. Goodman; Joshua B. Tenenbaum (NIPS, 2009)

Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model. - Ed Vul; Michael C. Frank; George A. Alvarez; Joshua B. Tenenbaum (NIPS, 2009)

The Infinite Latent Events Model. - David Wingate; Noah D. Goodman; Daniel M. Roy 001; Joshua B. Tenenbaum (UAI, 2009)

Indirect Evidence and the Poverty of the Stimulus: The Case of Anaphoric One. - Stephani Foraker; Terry Regier; Naveen Khetarpal; Amy Perfors; Joshua B. Tenenbaum (Cogn. Sci., 2009)

Exact and Approximate Sampling by Systematic Stochastic Search. - Vikash Mansinghka 001; Daniel M. Roy 001; Eric Jonas; Joshua B. Tenenbaum (AISTATS, 2009)

Nonparametric Bayesian Policy Priors for Reinforcement Learning. - Finale Doshi-Velez; David Wingate; Nicholas Roy; Joshua B. Tenenbaum (NIPS, 2010)

Dynamic Infinite Relational Model for Time-varying Relational Data Analysis. - Katsuhiko Ishiguro; Tomoharu Iwata; Naonori Ueda; Joshua B. Tenenbaum (NIPS, 2010)

Learning to Learn Causal Models. - Charles Kemp; Noah D. Goodman; Joshua B. Tenenbaum (Cogn. Sci., 2010)

Bayesian Theory of Mind: Modeling Joint Belief-Desire Attribution. - Chris L. Baker; Rebecca Saxe; Joshua B. Tenenbaum (CogSci, 2011)

Probabilistic internal physics models guide judgments about object dynamics. - Jessica B. Hamrick; Peter W. Battaglia; Joshua B. Tenenbaum (CogSci, 2011)

One shot learning of simple visual concepts. - Brenden M. Lake; Ruslan Salakhutdinov; Jason Gross; Joshua B. Tenenbaum (CogSci, 2011)

Productivity and Reuse in Language. - Timothy J. O'Donnell; Jesse Snedeker; Joshua B. Tenenbaum; Noah D. Goodman (CogSci, 2011)

Productivity and Reuse in Language: a Developmental Study. - Timothy J. O'Donnell; Jesse Snedeker; Joshua B. Tenenbaum; Noah D. Goodman (CogSci, 2011)

Forward Physics: How people learn and generalize novel dynamical models. - Tomer D. Ullman; Noah D. Goodman; Josh Tenenbaum 001 (CogSci, 2011)

Learning individual words and learning about words simultaneously. - Sylvia Yuan; Amy Perfors; Josh Tenenbaum 001; Fei Xu (CogSci, 2011)

Learning to share visual appearance for multiclass object detection. - Ruslan Salakhutdinov; Antonio Torralba 001; Joshua B. Tenenbaum (CVPR, 2011)

Infinite Dynamic Bayesian Networks. - Finale Doshi; David Wingate; Joshua B. Tenenbaum; Nicholas Roy (ICML, 2011)

Bayesian Policy Search with Policy Priors. - David Wingate; Noah D. Goodman; Daniel M. Roy 001; Leslie Pack Kaelbling; Joshua B. Tenenbaum (IJCAI, 2011)

Learning to Learn with Compound HD Models. - Ruslan Salakhutdinov; Joshua B. Tenenbaum; Antonio Torralba 001 (NIPS, 2011)

Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults. - Thomas L. Griffiths 001; David M. Sobel; Joshua B. Tenenbaum; Alison Gopnik (Cogn. Sci., 2011)

Computational Models of Intuitive Physics. - Peter W. Battaglia; Tomer D. Ullman; Joshua B. Tenenbaum; Adam Sanborn; Kenneth D. Forbus; Tobias Gerstenberg; David A. Lagnado (CogSci, 2012)

Noisy Newtons: Unifying process and dependency accounts of causal attribution. - Tobias Gerstenberg; Noah D. Goodman; David A. Lagnado; Joshua B. Tenenbaum (CogSci, 2012)

Children's sensitivity to informant's inductive efficiency and learner's epistemic states in pedagogical contexts. - Hyowon Gweon; Patrick Shafto; Joshua B. Tenenbaum; Laura Schulz (CogSci, 2012)

Learning What is Where from Social Observations. - Julian Jara-Ettinger; Chris L. Baker; Joshua B. Tenenbaum (CogSci, 2012)

Concept learning as motor program induction: A large-scale empirical study. - Brenden M. Lake; Ruslan Salakhutdinov; Joshua B. Tenenbaum (CogSci, 2012)

Computational, Cognitive, and Neural Models of Decision-making Biases. - Jonathan Malmaud; Joshua B. Tenenbaum; Peter Dayan; Laurence T. Maloney; Edward Vul; Nick Chater (CogSci, 2012)

Probabilistic generative models for counterfactual reasoning and blame attribution. - John McCoy; Tomer D. Ullman; Andreas Stuhlmüller; Tobias Gerstenberg; Joshua B. Tenenbaum (CogSci, 2012)

Exploring the Role of Representation in Models of Grammatical Category Acquisition. - Ting Qian; Patricia Reeder; Richard N. Aslin; Joshua B. Tenenbaum; Elissa L. Newport (CogSci, 2012)

The Development of Joint Belief-Desire Inferences. - Hilary Richardson; Chris L. Baker; Joshua B. Tenenbaum; Rebecca Saxe (CogSci, 2012)

Modeling the Perception of Intentions. - Barbara Tversky; Shimon Ullman; Dare A. Baldwin; Frank E. Pollick; Joshua B. Tenenbaum; Tao Gao 004; Peter C. Pantelis; David Pautler (CogSci, 2012)

Sticking to the Evidence? A computational and behavioral case study of micro-theory change in the domain of magnetism. - Elizabeth Bonawitz; Tomer D. Ullman; Alison Gopnik; Joshua B. Tenenbaum (ICDL-EPIROB, 2012)

Exploiting compositionality to explore a large space of model structures. - Roger B. Grosse; Ruslan Salakhutdinov; William T. Freeman; Joshua B. Tenenbaum (UAI, 2012)

Towards common-sense reasoning via conditional simulation: legacies of Turing in Artificial Intelligence - Cameron E. Freer; Daniel M. Roy 001; Joshua B. Tenenbaum (CoRR, 2012)

One-Shot Learning with a Hierarchical Nonparametric Bayesian Model. - Ruslan Salakhutdinov; Joshua B. Tenenbaum; Antonio Torralba 001 (ICML Unsupervised and Transfer Learning, 2012)

Multistability and Perceptual Inference. - Samuel Gershman; Edward Vul; Joshua B. Tenenbaum (Neural Comput., 2012)

Bayesian vector analysis and the perception of hierarchical motion. - Samuel Gershman; Frank Jäkel; Joshua B. Tenenbaum (CogSci, 2013)

Structured cognitive representations and complex inference in neural systems. - Samuel Gershman; Joshua B. Tenenbaum; Alexandre Pouget; Matthew M. Botvinick; Peter Dayan (CogSci, 2013)

Inferring mass in complex physical scenes via probabilistic simulation. - Jessica B. Hamrick; Peter W. Battaglia; Thomas L. Griffiths 001; Joshua B. Tenenbaum (CogSci, 2013)

Not so innocent: Reasoning about costs, competence, and culpability in very early childhood. - Julian Jara-Ettinger; Joshua B. Tenenbaum; Laura Schulz (CogSci, 2013)

Physical predictions over time. - Kevin A. Smith 001; Eyal Dechter; Joshua B. Tenenbaum; Edward Vul (CogSci, 2013)

Minimal Nativism: How does cognitive development get off the ground? - Tomer D. Ullman; Joshua B. Tenenbaum; Noah D. Goodman; Shimon Ullman; Elizabeth S. Spelke (CogSci, 2013)

Structure Discovery in Nonparametric Regression through Compositional Kernel Search. - David Duvenaud; James Robert Lloyd; Roger B. Grosse; Joshua B. Tenenbaum; Zoubin Ghahramani (ICML (3), 2013)

Bootstrap Learning via Modular Concept Discovery. - Eyal Dechter; Jonathan Malmaud; Ryan P. Adams; Joshua B. Tenenbaum (IJCAI, 2013)

One-shot learning by inverting a compositional causal process. - Brenden M. Lake; Ruslan Salakhutdinov; Joshua B. Tenenbaum (NIPS, 2013)

Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs. - Vikash K. Mansinghka; Tejas D. Kulkarni; Yura N. Perov; Joshua B. Tenenbaum (NIPS, 2013)

Learning with Hierarchical-Deep Models. - Ruslan Salakhutdinov; Joshua B. Tenenbaum; Antonio Torralba 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2013)

Towards common-sense reasoning via conditional simulation: legacies of Turing in Artificial Intelligence. - Cameron E. Freer; Daniel M. Roy 001; Joshua B. Tenenbaum (Turing's Legacy, 2014)

Automatic Construction and Natural-Language Description of Nonparametric Regression Models. - James Robert Lloyd; David Duvenaud; Roger B. Grosse; Joshua B. Tenenbaum; Zoubin Ghahramani (AAAI, 2014)

From counterfactual simulation to causal judgment. - Tobias Gerstenberg; Noah D. Goodman; David A. Lagnado; Joshua B. Tenenbaum (CogSci, 2014)

Wins above replacement: Responsibility attributions as counterfactual replacements. - Tobias Gerstenberg; Tomer D. Ullman; Max Kleiman-Weiner; David A. Lagnado; Joshua B. Tenenbaum (CogSci, 2014)

Probability, programs, and the mind: Building structured Bayesian models of cognition. - Noah D. Goodman; Joshua B. Tenenbaum (CogSci, 2014)

I'd do anything for a cookie (but I won't do that): Children's understanding of the costs and rewards underlying rational action. - Julian Jara-Ettinger; Hyowon Gweon; Joshua B. Tenenbaum; Laura Schulz (CogSci, 2014)

One-shot learning of generative speech concepts. - Brenden M. Lake; Chia-ying Lee; James R. Glass; Joshua B. Tenenbaum (CogSci, 2014)

Computational Creativity: Generating new objects with a hierarchical Bayesian model. - Brenden M. Lake; Josh Tenenbaum 001 (CogSci, 2014)

Error-Driven Stochastic Search for Theories and Concepts. - Owen Lewis; Santiago Perez; Joshua B. Tenenbaum (CogSci, 2014)

Black boxes: Hypothesis testing via indirect perceptual evidence. - Max H. Siegel; Rachel Magid; Joshua B. Tenenbaum; Laura Schulz (CogSci, 2014)

Information Selection in Noisy Environments with Large Action Spaces. - Pedro Tsividis; Samuel Gershman; Joshua B. Tenenbaum; Laura Schulz (CogSci, 2014)

Learning physical theories from dynamical scenes. - Tomer D. Ullman; Andreas Stuhlmüller; Noah D. Goodman; Joshua B. Tenenbaum (CogSci, 2014)

Joint inferences of belief and desire from facial expressions. - Yang Wu; Chris L. Baker; Joshua B. Tenenbaum; Laura Schulz (CogSci, 2014)

Bias reformulation for one-shot function induction. - Dianhuan Lin; Eyal Dechter; Kevin Ellis; Joshua B. Tenenbaum; Stephen H. Muggleton (ECAI, 2014)

One and Done? Optimal Decisions From Very Few Samples. - Edward Vul; Noah D. Goodman; Thomas L. Griffiths 001; Joshua B. Tenenbaum (Cogn. Sci., 2014)

When Computer Vision Gazes at Cognition. - Tao Gao 004; Daniel Harari; Joshua B. Tenenbaum; Shimon Ullman (CoRR, 2014)

Popularity and Performance: A Large-Scale Study. - Peter M. Krafft; Julia Zheng; Erez Shmueli; Nicolás Della Penna; Joshua B. Tenenbaum; Sandy Pentland (CoRR, 2014)

Inverse Graphics with Probabilistic CAD Models. - Tejas D. Kulkarni; Vikash K. Mansinghka; Pushmeet Kohli; Joshua B. Tenenbaum (CoRR, 2014)

Latent Predicate Networks: Concept Learning with Probabilistic Context-Sensitive Grammars. - Eyal Dechter; Joshua S. Rule; Joshua B. Tenenbaum (AAAI Spring Symposia, 2015)

Dimensionality Reduction via Program Induction. - Kevin Ellis; Eyal Dechter; Joshua B. Tenenbaum (AAAI Spring Symposia, 2015)

Go fishing! Responsibility judgments when cooperation breaks down. - Kelsey R. Allen; Julian Jara-Ettinger; Tobias Gerstenberg; Max Kleiman-Weiner; Joshua B. Tenenbaum (CogSci, 2015)

Humans predict liquid dynamics using probabilistic simulation. - Christopher Bates; Peter W. Battaglia; Ilker Yildirim; Joshua B. Tenenbaum (CogSci, 2015)

Phrase similarity in humans and machines. - Samuel Gershman; Joshua B. Tenenbaum (CogSci, 2015)

How, whether, why: Causal judgments as counterfactual contrasts. - Tobias Gerstenberg; Noah D. Goodman; David A. Lagnado; Joshua B. Tenenbaum (CogSci, 2015)

Responsibility judgments in voting scenarios. - Tobias Gerstenberg; Joseph Y. Halpern; Joshua B. Tenenbaum (CogSci, 2015)

Language & common sense: Integrating across psychology, linguistics, and computer science. - Joshua K. Hartshorne; Joshua B. Tenenbaum (CogSci, 2015)

Beliefs about desires: Children's understanding of how knowledge and preference influence choice. - Julian Jara-Ettinger; Emily Lydic; Joshua B. Tenenbaum; Laura Schulz (CogSci, 2015)

The naïve utility calculus: Joint inferences about the costs and rewards of actions. - Julian Jara-Ettinger; Laura Schulz; Joshua B. Tenenbaum (CogSci, 2015)

Inference of Intention and Permissibility in Moral Decision Making. - Max Kleiman-Weiner; Tobias Gerstenberg; Sydney Levine; Joshua B. Tenenbaum (CogSci, 2015)

Emergent Collective Sensing in Human Groups. - Peter M. Krafft; Robert X. D. Hawkins; Alex Pentland; Noah D. Goodman; Joshua B. Tenenbaum (CogSci, 2015)

Representing and Learning a Large System of Number Concepts with Latent Predicate Networks. - Joshua S. Rule; Eyal Dechter; Joshua B. Tenenbaum (CogSci, 2015)

Assessing the Perceived Predictability of Functions. - Eric Schulz; Joshua B. Tenenbaum; David N. Reshef; Maarten Speekenbrink; Samuel Gershman (CogSci, 2015)

Hypothesis-Space Constraints in Causal Learning. - Pedro Tsividis; Joshua B. Tenenbaum; Laura Schulz (CogSci, 2015)

Children's ability to infer beliefs and desires from emotional reactions. - Yang Wu; Chris L. Baker; Josh Tenenbaum 001; Laura Schulz (CogSci, 2015)

Efficient analysis-by-synthesis in vision: A computational framework, behavioral tests, and modeling neuronal representations. - Ilker Yildirim; Tejas D. Kulkarni; Winrich Freiwald; Joshua B. Tenenbaum (CogSci, 2015)

Picture: A probabilistic programming language for scene perception. - Tejas D. Kulkarni; Pushmeet Kohli; Joshua B. Tenenbaum; Vikash Mansinghka 001 (CVPR, 2015)

Risk and Regret of Hierarchical Bayesian Learners. - Jonathan H. Huggins; Joshua B. Tenenbaum (ICML, 2015)

Unsupervised Learning by Program Synthesis. - Kevin Ellis; Armando Solar-Lezama; Joshua B. Tenenbaum (NIPS, 2015)

Deep Convolutional Inverse Graphics Network. - Tejas D. Kulkarni; William F. Whitney; Pushmeet Kohli; Joshua B. Tenenbaum (NIPS, 2015)

Softstar: Heuristic-Guided Probabilistic Inference. - Mathew Monfort; Brenden M. Lake; Brian D. Ziebart; Patrick Lucey; Joshua B. Tenenbaum (NIPS, 2015)

Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning. - Jiajun Wu 001; Ilker Yildirim; Joseph J. Lim; Bill Freeman; Joshua B. Tenenbaum (NIPS, 2015)

Modeling Human Ad Hoc Coordination. - Peter M. Krafft; Chris L. Baker; Alex Pentland; Joshua B. Tenenbaum (AAAI, 2016)

Modeling Human Understanding of Complex Intentional Action with a Bayesian Nonparametric Subgoal Model. - Ryo Nakahashi; Chris L. Baker; Joshua B. Tenenbaum (AAAI, 2016)

Physics 101: Learning Physical Object Properties from Unlabeled Videos. - Jiajun Wu 001; Joseph J. Lim; Hongyi Zhang; Joshua B. Tenenbaum; William T. Freeman (BMVC, 2016)

Integrating identification and perception: A case study of familiar and unfamiliar face processing. - Kelsey R. Allen; Ilker Yildirim; Josh Tenenbaum 001 (CogSci, 2016)

Natural science: Active learning in dynamic physical microworlds. - Neil Bramley; Tobias Gerstenberg; Joshua B. Tenenbaum (CogSci, 2016)

Understanding "almost": Empirical and computational studies of near misses. - Tobias Gerstenberg; Josh Tenenbaum 001 (CogSci, 2016)

Feature-based Joint Planning and Norm Learning in Collaborative Games. - Mark K. Ho; James MacGlashan; Amy Greenwald; Michael L. Littman; Elizabeth Hilliard; Carl Trimbach; Stephen Brawner; Josh Tenenbaum 001; Max Kleiman-Weiner; Joseph L. Austerweil (CogSci, 2016)

The Naïve Utility Calculus unifies spatial and statistical routes to preference. - Julian Jara-Ettinger; Felix Sun; Laura Schulz; Josh Tenenbaum 001 (CogSci, 2016)

Coordinate to cooperate or compete: Abstract goals and joint intentions in social interaction. - Max Kleiman-Weiner; Mark K. Ho; Joseph L. Austerweil; Michael L. Littman; Josh Tenenbaum 001 (CogSci, 2016)

Outcome or Strategy? A Bayesian Model of Intelligence Attribution. - Marta Kryven; Tomer D. Ullman; William Cowan; Josh Tenenbaum 001 (CogSci, 2016)

Coalescing the Vapors of Human Experience into a Viable and Meaningful Comprehension. - Tomer D. Ullman; Max H. Siegel; Josh Tenenbaum 001; Samuel Gershman (CogSci, 2016)

Integrating physical reasoning and visual object recognition for fully occluded scene interpretation. - Ilker Yildirim; Max H. Siegel; Josh Tenenbaum 001 (CogSci, 2016)

A Comparative Evaluation of Approximate Probabilistic Simulation and Deep Neural Networks as Accounts of Human Physical Scene Understanding. - Renqiao Zhang; Jiajun Wu 001; Chengkai Zhang; William T. Freeman; Joshua B. Tenenbaum (CogSci, 2016)

Single Image 3D Interpreter Network. - Jiajun Wu 001; Tianfan Xue; Joseph J. Lim; Yuandong Tian; Joshua B. Tenenbaum; Antonio Torralba 001; William T. Freeman (ECCV (6), 2016)

Inferring human intent from video by sampling hierarchical plans. - Steven Holtzen; Yibiao Zhao; Tao Gao 004; Joshua B. Tenenbaum; Song-Chun Zhu (IROS, 2016)

Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling. - Jiajun Wu 001; Chengkai Zhang; Tianfan Xue; Bill Freeman; Josh Tenenbaum 001 (NIPS, 2016)

Sampling for Bayesian Program Learning. - Kevin Ellis; Armando Solar-Lezama; Josh Tenenbaum 001 (NIPS, 2016)

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation. - Tejas D. Kulkarni; Karthik Narasimhan; Ardavan Saeedi; Josh Tenenbaum 001 (NIPS, 2016)

Probing the Compositionality of Intuitive Functions. - Eric Schulz; Josh Tenenbaum 001; David Duvenaud; Maarten Speekenbrink; Samuel J. Gershman (NIPS, 2016)

Measuring and modeling the perception of natural and unconstrained gaze in humans and machines. - Daniel Harari; Tao Gao 004; Nancy Kanwisher; Joshua B. Tenenbaum; Shimon Ullman (CoRR, 2016)

Human collective intelligence as distributed Bayesian inference. - Peter M. Krafft; Julia Zheng; Wei Pan 002; Nicolás Della Penna; Yaniv Altshuler; Erez Shmueli; Joshua B. Tenenbaum; Alex Pentland (CoRR, 2016)

The Emergence of Organizing Structure in Conceptual Representation. - Brenden M. Lake; Neil D. Lawrence; Joshua B. Tenenbaum (CoRR, 2016)

Building Machines That Learn and Think Like People. - Brenden M. Lake; Tomer D. Ullman; Joshua B. Tenenbaum; Samuel J. Gershman (CoRR, 2016)

Understanding Visual Concepts with Continuation Learning. - William F. Whitney; Michael Chang 003; Tejas D. Kulkarni; Joshua B. Tenenbaum (CoRR, 2016)

CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data. - Vikash Mansinghka 001; Patrick Shafto; Eric Jonas; Cap Petschulat; Max Gasner; Joshua B. Tenenbaum (J. Mach. Learn. Res., 2016)

Human Learning in Atari. - Pedro Tsividis; Thomas Pouncy; Jaqueline L. Xu; Joshua B. Tenenbaum; Samuel J. Gershman (AAAI Spring Symposia, 2017)

Simulation and heuristics in flexible tool use. - Kelsey R. Allen; Kevin A. Smith 001; Josh Tenenbaum 001 (CogSci, 2017)

Learning to Learn Visual Object Categories by Integrating Deep Learning with Hierarchical Bayes. - Andres Campero; Andrew Francl; Joshua B. Tenenbaum (CogSci, 2017)

Faulty Towers: A hypothetical simulation model of physical support. - Tobias Gerstenberg; Liang Zhou; Kevin A. Smith 001; Joshua B. Tenenbaum (CogSci, 2017)

Constructing Social Preferences From Anticipated Judgments: When Impartial Inequity is Fair and Why? - Max Kleiman-Weiner; Alex Shaw; Josh Tenenbaum 001 (CogSci, 2017)

Cooperative Social Intelligence: Understanding and Acting with Other. - Max Kleiman-Weiner; Yibiao Zhao; Josh Tenenbaum 001 (CogSci, 2017)

One-shot Learning and Classification in Children. - Eliza Kosoy; Brenden M. Lake; Josh Tenenbaum 001 (CogSci, 2017)

Thinking and Guessing: Bayesian and Empirical Models of How Humans Search. - Marta Kryven; Tomer D. Ullman; William Cowan; Josh Tenenbaum 001 (CogSci, 2017)

Preschoolers and Infants Calibrate Persistence from Adult Models. - Julia A. Leonard; Max Kleiman-Weiner; Yuna Lee; Josh Tenenbaum 001; Laura Schulz (CogSci, 2017)

What's worth the effort: Ten-month-old infants infer the value of goals from the costs of actions. - Shari Liu; Tomer D. Ullman; Josh Tenenbaum 001; Elizabeth S. Spelke (CogSci, 2017)

Intuitive psychophysics: Children's exploratory play quantitatively tracks the discriminability of alternative hypotheses. - Rachel Magid; Max H. Siegel; Josh Tenenbaum 001; Laura Schulz (CogSci, 2017)

Big Data and Little Learners. - John C. Trueswell; Linda B. Smith; Josh Tenenbaum 001; Charles Yang 001 (CogSci, 2017)

Interpreting actions by attributing compositional desires. - Joey Velez-Ginorio; Max H. Siegel; Joshua B. Tenenbaum; Julian Jara-Ettinger (CogSci, 2017)

Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints. - Ilker Yildirim; Tobias Gerstenberg; Basil Saeed; Marc Toussaint; Josh Tenenbaum 001 (CogSci, 2017)

Causal and compositional generative models in online perception. - Ilker Yildirim; Michael Janner; Mario Belledonne; Christian Wallraven; Winrich Freiwald; Josh Tenenbaum 001 (CogSci, 2017)

Workshop proposal: Deep Learning in Computational Cognitive Science. - Ilker Yildirim; Josh Tenenbaum 001 (CogSci, 2017)

Neural Scene De-rendering. - Jiajun Wu 001; Joshua B. Tenenbaum; Pushmeet Kohli (CVPR, 2017)

Synthesizing 3D Shapes via Modeling Multi-view Depth Maps and Silhouettes with Deep Generative Networks. - Amir Arsalan Soltani; Haibin Huang; Jiajun Wu 001; Tejas D. Kulkarni; Joshua B. Tenenbaum (CVPR, 2017)

Generative Modeling of Audible Shapes for Object Perception. - Zhoutong Zhang; Jiajun Wu 001; Qiujia Li; Zhengjia Huang; James Traer; Josh H. McDermott; Joshua B. Tenenbaum; William T. Freeman (ICCV, 2017)

A Compositional Object-Based Approach to Learning Physical Dynamics. - Michael Chang 003; Tomer D. Ullman; Antonio Torralba 001; Joshua B. Tenenbaum (ICLR (Poster), 2017)

Learning to See Physics via Visual De-animation. - Jiajun Wu 001; Erika Lu; Pushmeet Kohli; Bill Freeman; Josh Tenenbaum 001 (NIPS, 2017)

MarrNet: 3D Shape Reconstruction via 2.5D Sketches. - Jiajun Wu 001; Yifan Wang 011; Tianfan Xue; Xingyuan Sun; Bill Freeman; Josh Tenenbaum 001 (NIPS, 2017)

Self-Supervised Intrinsic Image Decomposition. - Michael Janner; Jiajun Wu 001; Tejas D. Kulkarni; Ilker Yildirim; Josh Tenenbaum 001 (NIPS, 2017)

Shape and Material from Sound. - Zhoutong Zhang; Qiujia Li; Zhengjia Huang; Jiajun Wu 001; Josh Tenenbaum 001; Bill Freeman (NIPS, 2017)

Beating the World's Best at Super Smash Bros. with Deep Reinforcement Learning. - Vlad Firoiu; William F. Whitney; Joshua B. Tenenbaum (CoRR, 2017)

A First Step in Combining Cognitive Event Features and Natural Language Representations to Predict Emotions. - Andres Campero; Bjarke Felbo; Joshua B. Tenenbaum; Rebecca Saxe (CoRR, 2017)

A Computational Model of Commonsense Moral Decision Making. - Richard Kim; Max Kleiman-Weiner; Andrés Abeliuk; Edmond Awad; Sohan Dsouza; Joshua B. Tenenbaum; Iyad Rahwan (AIES, 2018)

Building Machines that Learn and Think Like People. - Josh Tenenbaum 001 (AAMAS, 2018)

Grounding Compositional Hypothesis Generation in Specific Instances. - Neil Bramley; Anselm Rothe; Josh Tenenbaum 001; Fei Xu; Todd M. Gureckis (CogSci, 2018)

Learning as program induction. - Neil Bramley; Eric Schulz; Fei Xu; Josh Tenenbaum 001 (CogSci, 2018)

Auditory scene analysis as Bayesian inference in sound source models. - Maddie Cusimano; Luke B. Hewitt; Josh Tenenbaum 001; Josh H. McDermott (CogSci, 2018)

Learning to act by integrating mental simulations and physical experiments. - Ishita Dasgupta 001; Kevin A. Smith 001; Eric Schulz; Josh Tenenbaum 001; Samuel Gershman (CogSci, 2018)

Tiptoeing around it: Inference from absence in potentially offensive speech. - Monica A. Gates; Tess L. Veuthey; Michael Henry Tessler; Kevin A. Smith 001; Tobias Gerstenberg; Laurie Bayet; Josh Tenenbaum 001 (CogSci, 2018)

Word learning and the acquisition of syntactic-semantic overhypotheses. - Jon Gauthier; Roger Levy; Josh Tenenbaum 001 (CogSci, 2018)

What happened? Reconstructing the past through vision and sound. - Tobias Gerstenberg; Max H. Siegel; Josh Tenenbaum 001 (CogSci, 2018)

Relational inductive bias for physical construction in humans and machines. - Jessica B. Hamrick; Kelsey R. Allen; Victor Bapst; Tina Zhu; Kevin R. McKee; Josh Tenenbaum 001; Peter W. Battaglia (CogSci, 2018)

Inductive Biases in the Evolution of Combinatorial Structure in Language. - Matthias Hofer 002; Josh Tenenbaum 001; Roger Levy (CogSci, 2018)

A generative model of people's intuitive theory of emotions: inverse planning in rich social games. - Sean Dae Houlihan; Max Kleiman-Weiner; Josh Tenenbaum 001; Rebecca Saxe (CogSci, 2018)

Hierarchical Drift-Diffusion Model for Moral Dilemma: Understanding Reaction Times and Choices. - Richard Kim; Niccolo Pescetelli; Max Kleiman-Weiner; Edmond Awad; Sohan Dsouza; Josh Tenenbaum 001; Iyad Rahwan (CogSci, 2018)

The Evolution of Cooperation in Cognitively Flexible Agents. - Max Kleiman-Weiner; Alejandro Vientós; David Rand; Josh Tenenbaum 001 (CogSci, 2018)

The Cognitive Mechanisms of Contractualist Moral Decision-Making. - Sydney Levine; Max Kleiman-Weiner; Nicholas Chater; Fiery Cushman; Josh Tenenbaum 001 (CogSci, 2018)

Learning list concepts through program induction. - Joshua S. Rule; Eric Schulz; Steven T. Piantadosi; Josh Tenenbaum 001 (CogSci, 2018)

Evidence for an Intuitive Physics Engine in the Human Brain. - Sarah Schwettmann; Jason Fischer; Josh Tenenbaum 001; Nancy Kanwisher (CogSci, 2018)

Physical Inference for Object Perception in Complex Auditory Scenes. - Max H. Siegel; Josh Tenenbaum 001; Josh H. McDermott (CogSci, 2018)

Strategies and representations in physical inference. - Kevin A. Smith 001; Josh Tenenbaum 001; Erin M. Anderson; Susan J. Hespos; Lance J. Rips; Chaz Firestone; Jessica B. Hamrick (CogSci, 2018)

Moral Dynamics: A Computational Model of Moral Judgment. - Felix Sosa; Tomer D. Ullman; Samuel Gershman; Josh Tenenbaum 001; Tobias Gerstenberg (CogSci, 2018)

Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling. - Xingyuan Sun; Jiajun Wu 001; Xiuming Zhang; Zhoutong Zhang; Chengkai Zhang; Tianfan Xue; Joshua B. Tenenbaum; William T. Freeman (CVPR, 2018)

Inverting Audio-Visual Simulation for Shape and Material Perception. - Zhoutong Zhang; Jiajun Wu 001; Qiujia Li; Zhengjia Huang; Joshua B. Tenenbaum; William T. Freeman (CVPR Workshops, 2018)

Physical Primitive Decomposition. - Zhijian Liu; William T. Freeman; Joshua B. Tenenbaum; Jiajun Wu 001 (ECCV (12), 2018)

Learning Shape Priors for Single-View 3D Completion And Reconstruction. - Jiajun Wu 001; Chengkai Zhang; Xiuming Zhang; Zhoutong Zhang; William T. Freeman; Joshua B. Tenenbaum (ECCV (11), 2018)

Seeing Tree Structure from Vibration. - Tianfan Xue; Jiajun Wu 001; Zhoutong Zhang; Chengkai Zhang; Joshua B. Tenenbaum; William T. Freeman (ECCV (9), 2018)

Meta-Learning for Semi-Supervised Few-Shot Classification. - Mengye Ren; Eleni Triantafillou; Sachin Ravi; Jake Snell; Kevin Swersky; Joshua B. Tenenbaum; Hugo Larochelle; Richard S. Zemel (ICLR (Poster), 2018)

Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing. - Anurag Ajay; Jiajun Wu 001; Nima Fazeli; Maria Bauzá 001; Leslie Pack Kaelbling; Joshua B. Tenenbaum; Alberto Rodriguez 003 (IROS, 2018)

3D Shape Perception from Monocular Vision, Touch, and Shape Priors. - Shaoxiong Wang; Jiajun Wu 001; Xingyuan Sun; Wenzhen Yuan 001; William T. Freeman; Joshua B. Tenenbaum; Edward H. Adelson (IROS, 2018)

End-to-End Differentiable Physics for Learning and Control. - Filipe de Avila Belbute-Peres; Kevin A. Smith 001; Kelsey R. Allen; Josh Tenenbaum 001; J. Zico Kolter (NeurIPS, 2018)

Learning to Exploit Stability for 3D Scene Parsing. - Yilun Du; Zhijian Liu; Hector Basevi; Ales Leonardis; Bill Freeman; Josh Tenenbaum 001; Jiajun Wu 001 (NeurIPS, 2018)

Learning Libraries of Subroutines for Neurally-Guided Bayesian Program Induction. - Kevin Ellis; Lucas Morales; Mathias Sablé-Meyer; Armando Solar-Lezama; Josh Tenenbaum 001 (NeurIPS, 2018)

Learning to Infer Graphics Programs from Hand-Drawn Images. - Kevin Ellis; Daniel Ritchie; Armando Solar-Lezama; Josh Tenenbaum 001 (NeurIPS, 2018)

Flexible neural representation for physics prediction. - Damian Mrowca; Chengxu Zhuang; Elias Wang; Nick Haber; Li Fei-Fei 001; Josh Tenenbaum 001; Daniel L. K. Yamins (NeurIPS, 2018)

Learning to Share and Hide Intentions using Information Regularization. - Daniel Strouse; Max Kleiman-Weiner; Josh Tenenbaum 001; Matthew M. Botvinick; David J. Schwab (NeurIPS, 2018)

3D-Aware Scene Manipulation via Inverse Graphics. - Shunyu Yao; Tzu-Ming Harry Hsu; Jun-Yan Zhu; Jiajun Wu 001; Antonio Torralba 001; Bill Freeman; Josh Tenenbaum 001 (NeurIPS, 2018)

Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding. - Kexin Yi; Jiajun Wu 001; Chuang Gan 001; Antonio Torralba 001; Pushmeet Kohli; Josh Tenenbaum 001 (NeurIPS, 2018)

Learning to Reconstruct Shapes from Unseen Classes. - Xiuming Zhang; Zhoutong Zhang; Chengkai Zhang; Josh Tenenbaum 001; Bill Freeman; Jiajun Wu 001 (NeurIPS, 2018)

Visual Object Networks: Image Generation with Disentangled 3D Representations. - Jun-Yan Zhu; Zhoutong Zhang; Chengkai Zhang; Jiajun Wu 001; Antonio Torralba 001; Josh Tenenbaum 001; Bill Freeman (NeurIPS, 2018)

The three pillars of machine programming. - Justin Gottschlich; Armando Solar-Lezama; Nesime Tatbul; Michael Carbin; Martin C. Rinard; Regina Barzilay; Saman P. Amarasinghe; Joshua B. Tenenbaum; Tim Mattson (MAPL@PLDI, 2018)

Differentiable Physics and Stable Modes for Tool-Use and Manipulation Planning. - Marc Toussaint; Kelsey R. Allen; Kevin A. Smith 001; Joshua B. Tenenbaum (Robotics: Science and Systems, 2018)

The Variational Homoencoder: Learning to learn high capacity generative models from few examples. - Luke B. Hewitt; Maxwell I. Nye; Andreea Gane; Tommi S. Jaakkola; Joshua B. Tenenbaum (UAI, 2018)

Unsupervised Learning of Latent Physical Properties Using Perception-Prediction Networks. - David Zheng; Vinson Luo; Jiajun Wu 001; Joshua B. Tenenbaum (UAI, 2018)

Rational Inference of Beliefs and Desires From Emotional Expressions. - Yang Wu; Chris L. Baker; Joshua B. Tenenbaum; Laura Schulz (Cogn. Sci., 2018)

Blaming humans in autonomous vehicle accidents: Shared responsibility across levels of automation. - Edmond Awad; Sydney Levine; Max Kleiman-Weiner; Sohan Dsouza; Joshua B. Tenenbaum; Azim Shariff; Jean-François Bonnefon; Iyad Rahwan (CoRR, 2018)

The Three Pillars of Machine-Based Programming. - Justin Gottschlich; Armando Solar-Lezama; Nesime Tatbul; Michael Carbin; Martin C. Rinard; Regina Barzilay; Saman P. Amarasinghe; Joshua B. Tenenbaum; Tim Mattson (CoRR, 2018)

Discovery and usage of joint attention in images. - Daniel Harari; Joshua B. Tenenbaum; Shimon Ullman (CoRR, 2018)

Logical Rule Induction and Theory Learning Using Neural Theorem Proving. - Andres Campero; Aldo Pareja; Tim Klinger; Josh Tenenbaum 001; Sebastian Riedel 001 (CoRR, 2018)

At Human Speed: Deep Reinforcement Learning with Action Delay. - Vlad Firoiu; Tina Ju; Josh Tenenbaum 001 (CoRR, 2018)

Visual Object Networks: Image Generation with Disentangled 3D Representation. - Jun-Yan Zhu; Zhoutong Zhang; Chengkai Zhang; Jiajun Wu 001; Antonio Torralba 001; Joshua B. Tenenbaum; William T. Freeman (CoRR, 2018)

Residual Policy Learning. - Tom Silver; Kelsey R. Allen; Josh Tenenbaum 001; Leslie Pack Kaelbling (CoRR, 2018)

3D Interpreter Networks for Viewer-Centered Wireframe Modeling. - Jiajun Wu 001; Tianfan Xue; Joseph J. Lim; Yuandong Tian; Joshua B. Tenenbaum; Antonio Torralba 001; William T. Freeman (Int. J. Comput. Vis., 2018)

Theory of Minds: Understanding Behavior in Groups through Inverse Planning. - Michael Shum; Max Kleiman-Weiner; Michael L. Littman; Joshua B. Tenenbaum (AAAI, 2019)

Rapid Trial-and-Error Learning in Physical Problem Solving. - Kelsey R. Allen; Kevin A. Smith 001; Josh Tenenbaum 001 (CogSci, 2019)

Simplicity and Probability in Human Judgment. - Tyler Brooke-Wilson; Jonathan S. Rosenfeld; Matthias Hofer 002; Junyi Chu; Josh Tenenbaum 001 (CogSci, 2019)

Query-guided visual search. - Junyi Chu; Jon Gauthier; Roger Levy; Josh Tenenbaum 001; Laura Schulz (CogSci, 2019)

Heuristics, hacks, and habits: Boundedly optimal approaches to learning, reasoning and decision making. - Ishita Dasgupta 001; Eric Schulz; Jessica B. Hamrick; Josh Tenenbaum 001 (CogSci, 2019)

A rational model of syntactic bootstrapping. - Jon Gauthier; Roger Levy; Josh Tenenbaum 001 (CogSci, 2019)

When circumstances change, update your pronouns. - Joshua K. Hartshorne; Mariela V. Jennings; Tobias Gerstenberg; Josh Tenenbaum 001 (CogSci, 2019)

Emotion attributions echo the structure of people's intuitive theory of psychology. - Sean Dae Houlihan; Max Kleiman-Weiner; Josh Tenenbaum 001; Rebecca Saxe (CogSci, 2019)

Look out, it's going to fall!: Does physical instability capture attention and lead to distraction? - Marta Kryven; Sholei Croom; Brian J. Scholl; Josh Tenenbaum 001 (CogSci, 2019)

Choosing the unimaginable: Social psychological factors in seeking transformative experiences. - Marta Kryven; Laura Niemi; Laurie Paul; Josh Tenenbaum 001 (CogSci, 2019)

What if everybody did that?: Universalization as a mechanism of moral decision-making. - Sydney Levine; Max Kleiman-Weiner; Laura Schulz; Josh Tenenbaum 001; Fiery Cushman (CogSci, 2019)

Discovering a symbolic planning language from continuous experience. - João Loula; Tom Silver; Kelsey R. Allen; Josh Tenenbaum 001 (CogSci, 2019)

Extending Rationality. - Emmanuel M. Pothos; Jerome R. Busemeyer; Timothy J. Pleskac; James M. Yearsley; Josh Tenenbaum 001; Noah D. Goodman; Michael Henry Tessler; Tom Griffiths 001; Falk Lieder; Ralph Hertwig; Thorsten Pachur; Christina Leuker; et al. (CogSci, 2019)

Inferring Structured Visual Concepts from Minimal Data. - Peng Qian; Luke B. Hewitt; Josh Tenenbaum 001; Roger Levy (CogSci, 2019)

Learning a novel rule-based conceptual system. - Joshua S. Rule; Josh Tenenbaum 001; Steven T. Piantadosi (CogSci, 2019)

Real-time inference of physical properties in dynamic scenes. - Kevin Smith 003; Mario Belledonne; Ilker Yildirim; Jiajun Wu 001; Josh Tenenbaum 001 (CogSci, 2019)

Draping an Elephant: Uncovering Children's Reasoning About Cloth-Covered Objects. - Tomer D. Ullman; Eliza Kosoy; Ilker Yildirim; Amir Arsalan Soltani; Max H. Siegel; Josh Tenenbaum 001; Elizabeth S. Spelke (CogSci, 2019)

Modeling Expertise with Neurally-Guided Bayesian Program Induction. - Catherine Wong; Kevin Ellis; Mathias Sablé-Meyer; Josh Tenenbaum 001 (CogSci, 2019)

The effects of object motion observations on physical prediction. - Moyuru Yamada; Kevin A. Smith 001; Josh Tenenbaum 001 (CogSci, 2019)

Explaining intuitive difficulty judgments by modeling physical effort and risk. - Ilker Yildirim; Basil Saeed; Grace Bennett-Pierre; Tobias Gerstenberg; Josh Tenenbaum 001; Hyowon Gweon (CogSci, 2019)

Entity Abstraction in Visual Model-Based Reinforcement Learning. - Rishi Veerapaneni; John D. Co-Reyes; Michael Chang 003; Michael Janner; Chelsea Finn; Jiajun Wu 001; Joshua B. Tenenbaum; Sergey Levine (CoRL, 2019)

Program-Guided Image Manipulators. - Xiuming Zhang; Jiayuan Mao; Yikai Li; William T. Freeman; Joshua B. Tenenbaum; Jiajun Wu 001 (ICCV, 2019)

GAN Dissection: Visualizing and Understanding Generative Adversarial Networks. - David Bau; Jun-Yan Zhu; Hendrik Strobelt; Bolei Zhou; Joshua B. Tenenbaum; William T. Freeman; Antonio Torralba 001 (ICLR (Poster), 2019)

Visualizing and Understanding GANs. - David Bau; Jun-Yan Zhu; Hendrik Strobelt; Bolei Zhou; Joshua B. Tenenbaum; William T. Freeman; Antonio Torralba 001 (DGS@ICLR, 2019)

Reasoning About Physical Interactions with Object-Oriented Prediction and Planning. - Michael Janner; Sergey Levine; William T. Freeman; Joshua B. Tenenbaum; Chelsea Finn; Jiajun Wu 001 (ICLR (Poster), 2019)

Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids. - Yunzhu Li; Jiajun Wu 001; Russ Tedrake; Joshua B. Tenenbaum; Antonio Torralba 001 (ICLR (Poster), 2019)

Learning to Describe Scenes with Programs. - Yunchao Liu; Zheng Wu; Daniel Ritchie; William T. Freeman; Joshua B. Tenenbaum; Jiajun Wu 001 (ICLR (Poster), 2019)

The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision. - Jiayuan Mao; Chuang Gan 001; Pushmeet Kohli; Joshua B. Tenenbaum; Jiajun Wu 001 (ICLR, 2019)

Stochastic Prediction of Multi-Agent Interactions from Partial Observations. - Chen Sun 002; Per Karlsson; Jiajun Wu 001; Joshua B. Tenenbaum; Kevin Murphy 002 (ICLR (Poster), 2019)

Learning to Infer and Execute 3D Shape Programs. - Yonglong Tian; Andrew Luo; Xingyuan Sun; Kevin Ellis; William T. Freeman; Joshua B. Tenenbaum; Jiajun Wu 001 (ICLR (Poster), 2019)

Unsupervised Discovery of Parts, Structure, and Dynamics. - Zhenjia Xu; Zhijian Liu; Chen Sun 002; Kevin Murphy 002; William T. Freeman; Joshua B. Tenenbaum; Jiajun Wu 001 (ICLR (Poster), 2019)

Infinite Mixture Prototypes for Few-shot Learning. - Kelsey R. Allen; Evan Shelhamer; Hanul Shin; Joshua B. Tenenbaum (ICML, 2019)

Neurally-Guided Structure Inference. - Sidi Lu; Jiayuan Mao; Joshua B. Tenenbaum; Jiajun Wu 001 (ICML, 2019)

Learning to Infer Program Sketches. - Maxwell I. Nye; Luke B. Hewitt; Joshua B. Tenenbaum; Armando Solar-Lezama (ICML, 2019)

Combining Physical Simulators and Object-Based Networks for Control. - Anurag Ajay; Maria Bauzá 001; Jiajun Wu 001; Nima Fazeli; Joshua B. Tenenbaum; Alberto Rodriguez 003; Leslie Pack Kaelbling (ICRA, 2019)

ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics. - Yuanming Hu; Jiancheng Liu; Andrew Spielberg; Joshua B. Tenenbaum; William T. Freeman; Jiajun Wu 001; Daniela Rus; Wojciech Matusik (ICRA, 2019)

Propagation Networks for Model-Based Control Under Partial Observation. - Yunzhu Li; Jiajun Wu 001; Jun-Yan Zhu; Joshua B. Tenenbaum; Antonio Torralba 001; Russ Tedrake (ICRA, 2019)

Differentiable Physics and Stable Modes for Tool-Use and Manipulation Planning - Extended Abtract. - Marc Toussaint; Kelsey R. Allen; Kevin A. Smith 001; Joshua B. Tenenbaum (IJCAI, 2019)

ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models. - Andrei Barbu; David Mayo; Julian Alverio; William Luo; Christopher Wang; Dan Gutfreund; Josh Tenenbaum 001; Boris Katz (NeurIPS, 2019)

Write, Execute, Assess: Program Synthesis with a REPL. - Kevin Ellis; Maxwell I. Nye; Yewen Pu; Felix Sosa; Josh Tenenbaum 001; Armando Solar-Lezama (NeurIPS, 2019)

Visual Concept-Metaconcept Learning. - Chi Han; Jiayuan Mao; Chuang Gan 001; Josh Tenenbaum 001; Jiajun Wu 001 (NeurIPS, 2019)

Finding Friend and Foe in Multi-Agent Games. - Jack Serrino; Max Kleiman-Weiner; David C. Parkes; Josh Tenenbaum 001 (NeurIPS, 2019)

Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations. - Kevin Smith 003; Lingjie Mei; Shunyu Yao; Jiajun Wu 001; Elizabeth S. Spelke; Josh Tenenbaum 001; Tomer D. Ullman (NeurIPS, 2019)

DensePhysNet: Learning Dense Physical Object Representations Via Multi-Step Dynamic Interactions. - Zhenjia Xu; Jiajun Wu 001; Andy Zeng 001; Joshua B. Tenenbaum; Shuran Song (Robotics: Science and Systems, 2019)

Sticking to the Evidence? A Behavioral and Computational Case Study of Micro-Theory Change in the Domain of Magnetism. - Elizabeth Bonawitz; Tomer D. Ullman; Sophie Bridgers; Alison Gopnik; Joshua B. Tenenbaum (Cogn. Sci., 2019)

The Cognitive Architecture of Perceived Animacy: Intention, Attention, and Memory. - Tao Gao 004; Chris L. Baker; Ning Tang; Haokui Xu; Joshua B. Tenenbaum (Cogn. Sci., 2019)

Visualizing and Understanding Generative Adversarial Networks (Extended Abstract). - David Bau; Jun-Yan Zhu; Hendrik Strobelt; Bolei Zhou; Joshua B. Tenenbaum; William T. Freeman; Antonio Torralba 001 (CoRR, 2019)

The Omniglot Challenge: A 3-Year Progress Report. - Brenden M. Lake; Ruslan Salakhutdinov; Joshua B. Tenenbaum (CoRR, 2019)

Few-Shot Bayesian Imitation Learning with Logic over Programs. - Tom Silver; Kelsey R. Allen; Alex K. Lew; Leslie Pack Kaelbling; Josh Tenenbaum 001 (CoRR, 2019)

The Tools Challenge: Rapid Trial-and-Error Learning in Physical Problem Solving. - Kelsey R. Allen; Kevin A. Smith 001; Joshua B. Tenenbaum (CoRR, 2019)

Dual Sequential Monte Carlo: Tunneling Filtering and Planning in Continuous POMDPs. - Yunbo Wang; Bo Liu 042; Jiajun Wu 001; Yuke Zhu; Simon S. Du; Li Fei-Fei 001; Joshua B. Tenenbaum (CoRR, 2019)

Machine behaviour. - Iyad Rahwan; Manuel Cebrián; Nick Obradovich; Josh C. Bongard; Jean-François Bonnefon; Cynthia Breazeal; Jacob W. Crandall; Nicholas A. Christakis; Iain D. Couzin; Matthew O. Jackson; Nicholas R. Jennings; Ece Kamar; et al. (Nat., 2019)

Modeling human intuitions about liquid flow with particle-based simulation. - Christopher Bates; Ilker Yildirim; Joshua B. Tenenbaum; Peter W. Battaglia (PLoS Comput. Biol., 2019)

See, feel, act: Hierarchical learning for complex manipulation skills with multisensory fusion. - Miquel Oller; Jiajun Wu 001; Z. Wu; Joshua B. Tenenbaum; Alberto Rodriguez 003 (Sci. Robotics, 2019)

Few-Shot Bayesian Imitation Learning with Logical Program Policies. - Tom Silver; Kelsey R. Allen; Alex K. Lew; Leslie Pack Kaelbling; Josh Tenenbaum 001 (AAAI, 2020)

The Origins of Common Sense in Humans and Machines. - Kevin Smith 003; Eliza Kosoy; Alison Gopnik; Deepak Pathak; Alan Fern; Josh Tenenbaum 001; Tomer D. Ullman (CogSci, 2020)

The fine structure of surprise in intuitive physics: when, why, and how much? - Kevin Smith 003; Lingjie Mei; Shunyu Yao; Jiajun Wu 001; Elizabeth S. Spelke; Josh Tenenbaum 001; Tomer D. Ullman (CogSci, 2020)

Abstract strategy learning underlies flexible transfer in physical problem solving. - Kelsey R. Allen; Kevin Smith 003; Ulyana Piterbarg; Robert Chen; Josh Tenenbaum 001 (CogSci, 2020)

Perceiving unseen objects. - Katherine M. Collins; Josh Tenenbaum 001; Kevin Smith 003 (CogSci, 2020)

Inverse Rendering Best Explains Face Perception Under Extreme Illuminations. - Bernhard Egger 001; Max H. Siegel; Riya Arora; Amir Arsalan Soltani; Ilker Yildirim; Josh Tenenbaum 001 (CogSci, 2020)

Jointly learning motion verbs and frame semantics from natural language and grounded scenes. - Jon Gauthier; Jiayuan Mao; Tianmin Shu; Roger Levy; Josh Tenenbaum 001 (CogSci, 2020)

Leveraging Unstructured Statistical Knowledge in a Probabilistic Language of Thought. - Alexander K. Lew; Michael Henry Tessler; Vikash Mansinghka 001; Josh Tenenbaum 001 (CogSci, 2020)

A Task and Motion Approach to the Development of Planning. - João Loula; Kelsey R. Allen; Josh Tenenbaum 001 (CogSci, 2020)

Humans measure algorithmic complexity to guide engagement with event sequences. - Gal Raz; Setayesh Radkani; Josh Tenenbaum 001; Rebecca Saxe (CogSci, 2020)

Learning sequential patterns from graphical programs. - Anselm Rothe; Eric Schulz; Mathias Sablé-Meyer; Josh Tenenbaum 001; Azzurra Ruggeri (CogSci, 2020)

Social Learning with Sparse Belief Samples. - Rabih Salhab; Amir Ajorlou; Ali Jadbabaie; Josh Tenenbaum 001 (CogSci, 2020)

Adventures in Flatland: Perceiving Social Interactions Under Physical Dynamics. - Tianmin Shu; Marta Kryven; Tomer D. Ullman; Josh Tenenbaum 001 (CogSci, 2020)

Learning a Generative Model of Human Faces Through Inverse Rendering. - Skylar Sutherland; Bernhard Egger 001; Josh Tenenbaum 001 (CogSci, 2020)

How many observations is one generic worth? - Michael Henry Tessler; Sophie Bridgers; Josh Tenenbaum 001 (CogSci, 2020)

Too many cooks: Coordinating multi-agent collaboration through inverse planning. - Sarah A. Wu; Rose E. Wang; James A. Evans; Josh Tenenbaum 001; David C. Parkes; Max Kleiman-Weiner (CogSci, 2020)

A Long Horizon Planning Framework for Manipulating Rigid Pointcloud Objects. - Anthony Simeonov; Yilun Du; Beomjoon Kim; Francois Robert Hogan; Joshua B. Tenenbaum; Pulkit Agrawal 001; Alberto Rodriguez 003 (CoRL, 2020)

Music Gesture for Visual Sound Separation. - Chuang Gan 001; Deng Huang; Hang Zhao; Joshua B. Tenenbaum; Antonio Torralba 001 (CVPR, 2020)

Perspective Plane Program Induction From a Single Image. - Yikai Li; Jiayuan Mao; Xiuming Zhang; William T. Freeman; Joshua B. Tenenbaum; Jiajun Wu 001 (CVPR, 2020)

End-to-End Optimization of Scene Layout. - Andrew Luo; Zhoutong Zhang; Jiajun Wu 001; Joshua B. Tenenbaum (CVPR, 2020)

A Morphable Face Albedo Model. - William A. P. Smith; Alassane Seck; Hannah Dee; Bernard Tiddeman; Joshua B. Tenenbaum; Bernhard Egger 001 (CVPR, 2020)

Probabilistic Video Prediction From Noisy Data With a Posterior Confidence. - Yunbo Wang; Jiajun Wu 001; Mingsheng Long; Joshua B. Tenenbaum (CVPR, 2020)

Foley Music: Learning to Generate Music from Videos. - Chuang Gan 001; Deng Huang; Peihao Chen; Joshua B. Tenenbaum; Antonio Torralba 001 (ECCV (11), 2020)

Rethinking Few-Shot Image Classification: A Good Embedding is All You Need? - Yonglong Tian; Yue Wang 041; Dilip Krishnan; Joshua B. Tenenbaum; Phillip Isola (ECCV (14), 2020)

CLEVRER: Collision Events for Video Representation and Reasoning. - Kexin Yi; Chuang Gan 001; Yunzhu Li; Pushmeet Kohli; Jiajun Wu 001; Antonio Torralba 001; Joshua B. Tenenbaum (ICLR, 2020)

Deep Audio Priors Emerge From Harmonic Convolutional Networks. - Zhoutong Zhang; Yunyun Wang; Chuang Gan 001; Jiajun Wu 001; Joshua B. Tenenbaum; Antonio Torralba 001; William T. Freeman (ICLR, 2020)

Visual Grounding of Learned Physical Models. - Yunzhu Li; Toru Lin; Kexin Yi; Daniel Bear; Daniel Yamins; Jiajun Wu 001; Joshua B. Tenenbaum; Antonio Torralba 001 (ICML, 2020)

Look, Listen, and Act: Towards Audio-Visual Embodied Navigation. - Chuang Gan 001; Yiwei Zhang; Jiajun Wu 001; Boqing Gong; Joshua B. Tenenbaum (ICRA, 2020)

Accurate Vision-based Manipulation through Contact Reasoning. - Alina Kloss; Maria Bauzá 001; Jiajun Wu 001; Joshua B. Tenenbaum; Alberto Rodriguez 003; Jeannette Bohg (ICRA, 2020)

DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs. - Yunbo Wang; Bo Liu 042; Jiajun Wu 001; Yuke Zhu; Simon S. Du; Li Fei-Fei 001; Joshua B. Tenenbaum (IJCAI, 2020)

Learning constraint-based planning models from demonstrations. - João Loula; Kelsey R. Allen; Tom Silver; Josh Tenenbaum 001 (IROS, 2020)

Learning Physical Graph Representations from Visual Scenes. - Daniel Bear; Chaofei Fan; Damian Mrowca; Yunzhu Li; Seth Alter; Aran Nayebi; Jeremy Schwartz; Li Fei-Fei 001; Jiajun Wu 001; Josh Tenenbaum 001; Daniel L. K. Yamins (NeurIPS, 2020)

Multi-Plane Program Induction with 3D Box Priors. - Yikai Li; Jiayuan Mao; Xiuming Zhang; Bill Freeman; Josh Tenenbaum 001; Noah Snavely; Jiajun Wu 001 (NeurIPS, 2020)

Learning Compositional Rules via Neural Program Synthesis. - Maxwell I. Nye; Armando Solar-Lezama; Josh Tenenbaum 001; Brenden M. Lake (NeurIPS, 2020)

Program Synthesis with Pragmatic Communication. - Yewen Pu; Kevin Ellis; Marta Kryven; Josh Tenenbaum 001; Armando Solar-Lezama (NeurIPS, 2020)

Learning abstract structure for drawing by efficient motor program induction. - Lucas Yanan Tian; Kevin Ellis; Marta Kryven; Josh Tenenbaum 001 (NeurIPS, 2020)

Online Bayesian Goal Inference for Boundedly Rational Planning Agents. - Tan Zhi-Xuan; Jordyn L. Mann; Tom Silver; Josh Tenenbaum 001; Vikash Mansinghka 001 (NeurIPS, 2020)

Learning to learn generative programs with Memoised Wake-Sleep. - Luke B. Hewitt; Tuan Anh Le 001; Joshua B. Tenenbaum (UAI, 2020)

GLIB: Exploration via Goal-Literal Babbling for Lifted Operator Learning. - Rohan Chitnis; Tom Silver; Joshua B. Tenenbaum; Leslie Pack Kaelbling; Tomás Lozano-Pérez (CoRR, 2020)

Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense. - Yixin Zhu 001; Tao Gao 004; Lifeng Fan; Siyuan Huang 001; Mark Edmonds; Hangxin Liu; Feng Gao 013; Chi Zhang 017; Siyuan Qi; Ying Nian Wu; Joshua B. Tenenbaum; Song-Chun Zhu (CoRR, 2020)

DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning. - Kevin Ellis; Catherine Wong; Maxwell I. Nye; Mathias Sablé-Meyer; Luc Cary; Lucas Morales; Luke B. Hewitt; Armando Solar-Lezama; Joshua B. Tenenbaum (CoRR, 2020)

ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. - Chuang Gan 001; Jeremy Schwartz; Seth Alter; Martin Schrimpf; James Traer; Julian De Freitas; Jonas Kubilius; Abhishek Bhandwaldar; Nick Haber; Megumi Sano; Kuno Kim; Elias Wang; et al. (CoRR, 2020)

Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration. - Xavier Puig; Tianmin Shu; Shuang Li 013; Zilin Wang; Joshua B. Tenenbaum; Sanja Fidler; Antonio Torralba 001 (CoRR, 2020)

Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling. - Akash Srivastava; Yamini Bansal; Yukun Ding; Cole L. Hurwitz; Kai Xu 016; Bernhard Egger 001; Prasanna Sattigeri; Josh Tenenbaum 001; David D. Cox; Dan Gutfreund (CoRR, 2020)

Learning Online Data Association. - Yilun Du; Joshua B. Tenenbaum; Tomás Lozano-Pérez; Leslie Pack Kaelbling (CoRR, 2020)

Towards Human-Level Learning of Complex Physical Puzzles. - Kei Ota; Devesh K. Jha; Diego Romeres; Jeroen van Baar; Kevin A. Smith 001; Takayuki Semitsu; Tomoaki Oiki; Alan Sullivan; Daniel Nikovski; Joshua B. Tenenbaum (CoRR, 2020)

Object-Centric Diagnosis of Visual Reasoning. - Jianwei Yang; Jiayuan Mao; Jiajun Wu 001; Devi Parikh; David D. Cox; Joshua B. Tenenbaum; Chuang Gan 001 (CoRR, 2020)

Augmenting Policy Learning with Routines Discovered from a Demonstration. - Zelin Zhao; Chuang Gan 001; Jiajun Wu 001; Xiaoxiao Guo; Joshua B. Tenenbaum (CoRR, 2020)

GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling. - Rohan Chitnis; Tom Silver; Joshua B. Tenenbaum; Leslie Pack Kaelbling; Tomás Lozano-Pérez (AAAI, 2021)

PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception. - Aviv Netanyahu; Tianmin Shu; Boris Katz; Andrei Barbu; Joshua B. Tenenbaum (AAAI, 2021)

Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks. - Tom Silver; Rohan Chitnis; Aidan Curtis; Joshua B. Tenenbaum; Tomás Lozano-Pérez; Leslie Pack Kaelbling (AAAI, 2021)

Augmenting Policy Learning with Routines Discovered from a Single Demonstration. - Zelin Zhao; Chuang Gan 001; Jiajun Wu 001; Xiaoxiao Guo; Joshua B. Tenenbaum (AAAI, 2021)

Learning Evolved Combinatorial Symbols with a Neuro-symbolic Generative Model. - Matthias Hofer 002; Tuan Anh Le 001; Roger Levy; Josh Tenenbaum 001 (CogSci, 2021)

LARC: Language annotated Abstraction and Reasoning Corpus. - Samuel Acquaviva; Yewen Pu; Maxwell I. Nye; Catherine Wong; Michael Henry Tessler; Josh Tenenbaum 001 (CogSci, 2021)

Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind. - Arwa Alanqary; Gloria Z. Lin; Joie Le; Tan Zhi-Xuan; Vikash Mansinghka 001; Josh Tenenbaum 001 (CogSci, 2021)

Meta-strategy learning in physical problem-solving: the effect of embodied experience. - Kelsey R. Allen; Kevin A. Smith 001; Laura-Ashleigh Bird; Josh Tenenbaum 001; Tamar R. Makin; Dorothy Cowie (CogSci, 2021)

Using Games to Understand Intelligence. - Franziska Brändle; Kelsey R. Allen; Josh Tenenbaum 001; Eric Schulz (CogSci, 2021)

Explore, Exploit, Create: Inventing goals in play. - Sophia Diggs-Galligan; Junyi Chu; Josh Tenenbaum 001; Laura Schulz (CogSci, 2021)

Core knowledge objects in reasoning and language use for highly abstract inductive tasks. - Gabrielle E. Ecanow; Catherine Wong; Samuel Acquaviva; Yewen Pu; Marta Kryven; Josh Tenenbaum 001 (CogSci, 2021)

Explaining the Gestalt principle of common fate as amortized inference. - Yoni Friedman; Tuan Anh Le 001; Bernhard Egger 001; Max H. Siegel; Josh Tenenbaum 001 (CogSci, 2021)

The Omniglot Jr. challenge; Can a model achieve child-level character generation and classification? - Eliza Kosoy; Masha Belyi; Charlie Snell; Brenden M. Lake; Josh Tenenbaum 001; Alison Gopnik (CogSci, 2021)

Engineering and reverse-engineering morality. - Sydney Levine; Fiery Cushman; Iyad Rahwan; Josh Tenenbaum 001 (CogSci, 2021)

Combining rules and simulation to explain infant physical learning. - João Loula; Kelsey R. Allen; Josh Tenenbaum 001 (CogSci, 2021)

Growing knowledge culturally across generations to solve novel, complex task. - Michael Henry Tessler; Pedro Tsividis; Jason Madeano; Brin Harper; Josh Tenenbaum 001 (CogSci, 2021)

Language as a bootstrap for compositional visual reasoning. - Catherine Wong; Yoni Friedman; Jacob Andreas; Josh Tenenbaum 001 (CogSci, 2021)

Modeling human planning in a life-like search-and-rescue mission. - Zhutian Yang; Marta Kryven; Howard E. Shrobe; Josh Tenenbaum 001 (CogSci, 2021)

Seeing in the dark: Testing deep neural network and analysis-by-synthesis accounts of 3D shape perception with highly degraded images. - Hakan Yilmaz; Gargi Singh; Bernhard Egger 001; Josh Tenenbaum 001; Ilker Yildirim (CogSci, 2021)

Unpacking the computations of human spatial search under uncertainty: noisy utility maximization, discounting, and probability warping. - Suhyoun Yu; Marta Kryven; Josh Tenenbaum 001; Max Kleiman-Weiner (CogSci, 2021)

Single-Shot Scene Reconstruction. - Sergey Zakharov; Rares Andrei Ambrus; Vitor Guizilini; Dennis Park; Wadim Kehl; Frédo Durand; Joshua B. Tenenbaum; Vincent Sitzmann; Jiajun Wu 001; Adrien Gaidon (CoRL, 2021)

Identity-Expression Ambiguity in 3D Morphable Face Models. - Bernhard Egger 001; Skylar Sutherland; Safa C. Medin; Joshua B. Tenenbaum (FG, 2021)

Neural Radiance Flow for 4D View Synthesis and Video Processing. - Yilun Du; Yinan Zhang; Hong-Xing Yu; Joshua B. Tenenbaum; Jiajun Wu 001 (ICCV, 2021)

Building 3D Morphable Models from a Single Scan. - Skylar Sutherland; Bernhard Egger 001; Josh Tenenbaum 001 (ICCVW, 2021)

Learning with AMIGo: Adversarially Motivated Intrinsic Goals. - Andres Campero; Roberta Raileanu; Heinrich Küttler; Joshua B. Tenenbaum; Tim Rocktäschel; Edward Grefenstette (ICLR, 2021)

Grounding Physical Concepts of Objects and Events Through Dynamic Visual Reasoning. - Zhenfang Chen; Jiayuan Mao; Jiajun Wu 001; Kwan-Yee Kenneth Wong; Joshua B. Tenenbaum; Chuang Gan 001 (ICLR, 2021)

Unsupervised Discovery of 3D Physical Objects from Video. - Yilun Du; Kevin A. Smith 001; Tomer D. Ullman; Joshua B. Tenenbaum; Jiajun Wu 001 (ICLR, 2021)

PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics. - Zhiao Huang; Yuanming Hu; Tao Du 001; Siyuan Zhou; Hao Su 001; Joshua B. Tenenbaum; Chuang Gan 001 (ICLR, 2021)

Learning Task Decomposition with Ordered Memory Policy Network. - Yuchen Lu; Yikang Shen; Siyuan Zhou; Aaron C. Courville; Joshua B. Tenenbaum; Chuang Gan 001 (ICLR, 2021)

Representing Partial Programs with Blended Abstract Semantics. - Maxwell I. Nye; Yewen Pu; Matthew Bowers; Jacob Andreas; Joshua B. Tenenbaum; Armando Solar-Lezama (ICLR, 2021)

Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration. - Xavier Puig; Tianmin Shu; Shuang Li 013; Zilin Wang; Yuan-Hong Liao; Joshua B. Tenenbaum; Sanja Fidler; Antonio Torralba 001 (ICLR, 2021)

A large-scale benchmark for few-shot program induction and synthesis. - Ferran Alet; Javier Lopez-Contreras; James Koppel; Maxwell I. Nye; Armando Solar-Lezama; Tomás Lozano-Pérez; Leslie Pack Kaelbling; Joshua B. Tenenbaum (ICML, 2021)

Improved Contrastive Divergence Training of Energy-Based Models. - Yilun Du; Shuang Li 013; Joshua B. Tenenbaum; Igor Mordatch (ICML, 2021)

AGENT: A Benchmark for Core Psychological Reasoning. - Tianmin Shu; Abhishek Bhandwaldar; Chuang Gan 001; Kevin A. Smith 001; Shari Liu; Dan Gutfreund; Elizabeth S. Spelke; Joshua B. Tenenbaum; Tomer D. Ullman (ICML, 2021)

Leveraging Language to Learn Program Abstractions and Search Heuristics. - Catherine Wong; Kevin Ellis; Joshua B. Tenenbaum; Jacob Andreas (ICML, 2021)

Temporal and Object Quantification Networks. - Jiayuan Mao; Zhezheng Luo; Chuang Gan 001; Joshua B. Tenenbaum; Jiajun Wu 001; Leslie Pack Kaelbling; Tomer D. Ullman (IJCAI, 2021)

OPEn: An Open-ended Physics Environment for Learning Without a Task. - Chuang Gan 001; Abhishek Bhandwaldar; Antonio Torralba 001; Joshua B. Tenenbaum; Phillip Isola (IROS, 2021)

Learning Symbolic Operators for Task and Motion Planning. - Tom Silver; Rohan Chitnis; Joshua B. Tenenbaum; Leslie Pack Kaelbling; Tomás Lozano-Pérez (IROS, 2021)

Dynamic Modeling of Hand-Object Interactions via Tactile Sensing. - Qiang Zhang; Yunzhu Li; Yiyue Luo; Wan Shou; Michael Foshey; Junchi Yan; Joshua B. Tenenbaum; Wojciech Matusik; Antonio Torralba 001 (IROS, 2021)

Noether Networks: meta-learning useful conserved quantities. - Ferran Alet; Dylan Doblar; Allan Zhou; Josh Tenenbaum 001; Kenji Kawaguchi; Chelsea Finn (NeurIPS, 2021)

Physion: Evaluating Physical Prediction from Vision in Humans and Machines. - Daniel Bear; Elias Wang; Damian Mrowca; Felix J. Binder; Hsiao-Yu Tung; R. T. Pramod; Cameron Holdaway; Sirui Tao; Kevin A. Smith 001; Fan-Yun Sun; Fei-Fei Li 001; Nancy Kanwisher; et al. (NeurIPS Datasets and Benchmarks, 2021)

Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language. - Mingyu Ding; Zhenfang Chen; Tao Du 001; Ping Luo 002; Josh Tenenbaum 001; Chuang Gan 001 (NeurIPS, 2021)

Learning Signal-Agnostic Manifolds of Neural Fields. - Yilun Du; Katie Collins; Josh Tenenbaum 001; Vincent Sitzmann (NeurIPS, 2021)

Unsupervised Learning of Compositional Energy Concepts. - Yilun Du; Shuang Li 013; Yash Sharma 001; Josh Tenenbaum 001; Igor Mordatch (NeurIPS, 2021)

ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. - Chuang Gan 001; Jeremy Schwartz; Seth Alter; Damian Mrowca; Martin Schrimpf; James Traer; Julian De Freitas; Jonas Kubilius; Abhishek Bhandwaldar; Nick Haber; Megumi Sano; Kuno Kim; et al. (NeurIPS Datasets and Benchmarks, 2021)

3DP3: 3D Scene Perception via Probabilistic Programming. - Nishad Gothoskar; Marco F. Cusumano-Towner; Ben Zinberg; Matin Ghavamizadeh; Falk Pollok; Austin Garrett; Josh Tenenbaum 001; Dan Gutfreund; Vikash K. Mansinghka (NeurIPS, 2021)

PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning. - Yining Hong; Li Yi 001; Josh Tenenbaum 001; Antonio Torralba 001; Chuang Gan 001 (NeurIPS, 2021)

Learning to Compose Visual Relations. - Nan Liu 010; Shuang Li 013; Yilun Du; Josh Tenenbaum 001; Antonio Torralba 001 (NeurIPS, 2021)

Grammar-Based Grounded Lexicon Learning. - Jiayuan Mao; Freda Shi; Jiajun Wu 001; Roger Levy; Josh Tenenbaum 001 (NeurIPS, 2021)

Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning. - Maxwell I. Nye; Michael Henry Tessler; Joshua B. Tenenbaum; Brenden M. Lake (NeurIPS, 2021)

Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering. - Vincent Sitzmann; Semon Rezchikov; Bill Freeman; Josh Tenenbaum 001; Frédo Durand (NeurIPS, 2021)

STAR: A Benchmark for Situated Reasoning in Real-World Videos. - Bo Wu 018; Shoubin Yu; Zhenfang Chen; Josh Tenenbaum 001; Chuang Gan 001 (NeurIPS Datasets and Benchmarks, 2021)

A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics. - Kai Xu 016; Akash Srivastava; Dan Gutfreund; Felix Sosa; Tomer D. Ullman; Josh Tenenbaum 001; Charles Sutton (NeurIPS, 2021)

DreamCoder: bootstrapping inductive program synthesis with wake-sleep library learning. - Kevin Ellis; Catherine Wong; Maxwell I. Nye; Mathias Sablé-Meyer; Lucas Morales; Luke B. Hewitt; Luc Cary; Armando Solar-Lezama; Joshua B. Tenenbaum (PLDI, 2021)

Plans or Outcomes: How Do We Attribute Intelligence to Others? - Marta Kryven; Tomer D. Ullman; William Cowan; Joshua B. Tenenbaum (Cogn. Sci., 2021)

Modular Object-Oriented Games: A Task Framework for Reinforcement Learning, Psychology, and Neuroscience. - Nicholas Watters; Joshua B. Tenenbaum; Mehrdad Jazayeri (CoRR, 2021)

The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark for Physically Realistic Embodied AI. - Chuang Gan 001; Siyuan Zhou; Jeremy Schwartz; Seth Alter; Abhishek Bhandwaldar; Dan Gutfreund; Daniel L. K. Yamins; James J. DiCarlo; Josh H. McDermott; Antonio Torralba 001; Joshua B. Tenenbaum (CoRR, 2021)

Probabilistic Programming Bots in Intuitive Physics Game Play. - Fahad Alhasoun; Sarah Alnegheimish; Joshua B. Tenenbaum (CoRR, 2021)

Physion: Evaluating Physical Prediction from Vision in Humans and Machines. - Daniel M. Bear; Elias Wang; Damian Mrowca; Felix J. Binder; Hsiau-Yu Fish Tung; R. T. Pramod; Cameron Holdaway; Sirui Tao; Kevin A. Smith 001; Fan-Yun Sun; Li Fei-Fei 001; Nancy Kanwisher; et al. (CoRR, 2021)

Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning. - Pedro A. Tsividis; João Loula; Jake Burga; Nathan Foss; Andres Campero; Thomas Pouncy; Samuel J. Gershman; Joshua B. Tenenbaum (CoRR, 2021)

Growing knowledge culturally across generations to solve novel, complex tasks. - Michael Henry Tessler; Pedro A. Tsividis; Jason Madeano; Brin Harper; Joshua B. Tenenbaum (CoRR, 2021)

Modeling human intention inference in continuous 3D domains by inverse planning and body kinematics. - Yingdong Qian; Marta Kryven; Tao Gao 004; Hanbyul Joo; Josh Tenenbaum 001 (CoRR, 2021)

Vector-based pedestrian navigation in cities. - Christian Bongiorno; Yulun Zhou; Marta Kryven; David Theurel; Alessandro Rizzo; Paolo Santi; Josh Tenenbaum 001; Carlo Ratti (Nat. Comput. Sci., 2021)

World model learning and inference. - Karl J. Friston; Rosalyn J. Moran; Yukie Nagai; Tadahiro Taniguchi; Hiroaki Gomi; Josh Tenenbaum 001 (Neural Networks, 2021)

Data-Efficient Learning for Complex and Real-Time Physical Problem Solving Using Augmented Simulation. - Kei Ota; Devesh K. Jha; Diego Romeres; Jeroen van Baar; Kevin A. Smith 001; Takayuki Semitsu; Tomoaki Oiki; Alan Sullivan; Daniel Nikovski; Joshua B. Tenenbaum (IEEE Robotics Autom. Lett., 2021)

Too Many Cooks: Bayesian Inference for Coordinating Multi-Agent Collaboration. - Sarah A. Wu; Rose E. Wang; James A. Evans; Joshua B. Tenenbaum; David C. Parkes; Max Kleiman-Weiner (Top. Cogn. Sci., 2021)

Too Many cooks: Bayesian inference for coordinating Multi-agent Collaboration. - Rose E. Wang; Sarah A. Wu; James A. Evans; David C. Parkes; Joshua B. Tenenbaum; Max Kleiman-Weiner (Human-Like Machine Intelligence, 2022)

Discovering State and Action Abstractions for Generalized Task and Motion Planning. - Aidan Curtis; Tom Silver; Joshua B. Tenenbaum; Tomás Lozano-Pérez; Leslie Pack Kaelbling (AAAI, 2022)

MOST-GAN: 3D Morphable StyleGAN for Disentangled Face Image Manipulation. - Safa C. Medin; Bernhard Egger 001; Anoop Cherian; Ye Wang 001; Joshua B. Tenenbaum; Xiaoming Liu 002; Tim K. Marks (AAAI, 2022)

Preschoolers' sensitivity to abstract correlations in the properties of sets and functions. - Nicole Coates; Max H. Siegel; Junyi Chu; Melissa Kline; Josh Tenenbaum 001; Laura Schulz (CogSci, 2022)

Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning tasks. - Katherine M. Collins; Catherine Wong; Jiahai Feng; Megan Wei; Josh Tenenbaum 001 (CogSci, 2022)

Benchmarking mid-level vision with texture-defined 3D objects. - Yoni Friedman; Thomas P. O'Connell; Max H. Siegel; Daniel M. Bear; Tuan Anh Le 001; Bernhard Egger 001; Josh Tenenbaum 001 (CogSci, 2022)

Modeling risky food sharing as rational communication about relationships. - Michelle Simona Hung; Ashley J. Thomas; Setayesh Radkani; Josh Tenenbaum 001; Rebecca Saxe (CogSci, 2022)

Flexibility in Moral Cognition: When is it okay to break the rules? - Joseph Kwon; Josh Tenenbaum 001; Sydney Levine (CogSci, 2022)

Joint online inference of material properties and object shape. - Vivian C. Paulun; Max H. Siegel; Josh Tenenbaum 001 (CogSci, 2022)

Modeling punishment as a rational communicative social action. - Setayesh Radkani; Josh Tenenbaum 001; Rebecca Saxe (CogSci, 2022)

Learning as Programming: Modeling Efficient Search in Human Concept Learning. - Joshua S. Rule; Steven T. Piantadosi; Josh Tenenbaum 001 (CogSci, 2022)

Efficient exploration of spatial environments through Map Induction using adaptable compositional map representations. - Sugandha Sharma; Aidan Curtis; Marta Kryven; Josh Tenenbaum 001; Ila Fiete (CogSci, 2022)

Identifying concept libraries from language about object structure. - Catherine Wong; William P. McCarthy; Gabriel Grand; Yoni Friedman; Josh Tenenbaum 001; Jacob Andreas; Robert D. Hawkins; Judith E. Fan (CogSci, 2022)

Embodied Concept Learner: Self-supervised Learning of Concepts and Mapping through Instruction Following. - Mingyu Ding; Yan Xu; Zhenfang Chen; David Daniel Cox; Ping Luo 002; Joshua B. Tenenbaum; Chuang Gan 001 (CoRL, 2022)

Learning Neuro-Symbolic Skills for Bilevel Planning. - Tom Silver; Ashay Athalye; Joshua B. Tenenbaum; Tomás Lozano-Pérez; Leslie Pack Kaelbling (CoRL, 2022)

Finding Fallen Objects Via Asynchronous Audio-Visual Integration. - Chuang Gan 001; Yi Gu 002; Siyuan Zhou; Jeremy Schwartz; Seth Alter; James Traer; Dan Gutfreund; Joshua B. Tenenbaum; Josh H. McDermott; Antonio Torralba 001 (CVPR, 2022)

Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction. - Yining Hong; Kaichun Mo; Li Yi 001; Leonidas J. Guibas; Antonio Torralba 001; Joshua B. Tenenbaum; Chuang Gan 001 (CVPR, 2022)

Unsupervised Segmentation in Real-World Images via Spelke Object Inference. - Honglin Chen; Rahul Mysore Venkatesh; Yoni Friedman; Jiajun Wu 001; Joshua B. Tenenbaum; Daniel L. K. Yamins; Daniel M. Bear (ECCV (29), 2022)

Compositional Visual Generation with Composable Diffusion Models. - Nan Liu 010; Shuang Li 013; Yilun Du; Antonio Torralba 001; Joshua B. Tenenbaum (ECCV (17), 2022)

Revisiting the Roles of "Text" in Text Games. - Yi Gu 002; Shunyu Yao; Chuang Gan 001; Josh Tenenbaum 001; Mo Yu (EMNLP (Findings), 2022)

Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface. - Tuan Anh Le 001; Katherine M. Collins; Luke Hewitt; Kevin Ellis; Siddharth Narayanaswamy; Samuel Gershman; Joshua B. Tenenbaum (ICLR, 2022)

RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation. - Pingchuan Ma 002; Tao Du 001; Joshua B. Tenenbaum; Wojciech Matusik; Chuang Gan 001 (ICLR, 2022)

ComPhy: Compositional Physical Reasoning of Objects and Events from Videos. - Zhenfang Chen; Kexin Yi; Yunzhu Li; Mingyu Ding; Antonio Torralba 001; Joshua B. Tenenbaum; Chuang Gan 001 (ICLR, 2022)

Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics. - Sizhe Li; Zhiao Huang; Tao Du 001; Hao Su 001; Joshua B. Tenenbaum; Chuang Gan 001 (ICLR, 2022)

DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools. - Xingyu Lin; Zhiao Huang; Yunzhu Li; Joshua B. Tenenbaum; David Held; Chuang Gan 001 (ICLR, 2022)

FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations. - Lingjie Mei; Jiayuan Mao; Ziqi Wang; Chuang Gan 001; Joshua B. Tenenbaum (ICLR, 2022)

Map Induction: Compositional spatial submap learning for efficient exploration in novel environments. - Sugandha Sharma; Aidan Curtis; Marta Kryven; Joshua B. Tenenbaum; Ila R. Fiete (ICLR, 2022)

Linking Emergent and Natural Languages via Corpus Transfer. - Shunyu Yao; Mo Yu; Yang Zhang 001; Karthik R. Narasimhan; Joshua B. Tenenbaum; Chuang Gan 001 (ICLR, 2022)

Learning Iterative Reasoning through Energy Minimization. - Yilun Du; Shuang Li 013; Joshua B. Tenenbaum; Igor Mordatch (ICML, 2022)

Planning with Diffusion for Flexible Behavior Synthesis. - Michael Janner; Yilun Du; Joshua B. Tenenbaum; Sergey Levine (ICML, 2022)

Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning. - Aviv Netanyahu; Tianmin Shu; Joshua B. Tenenbaum; Pulkit Agrawal 001 (ICML, 2022)

Prompting Decision Transformer for Few-Shot Policy Generalization. - Mengdi Xu; Yikang Shen; Shun Zhang; Yuchen Lu; Ding Zhao; Joshua B. Tenenbaum; Chuang Gan 001 (ICML, 2022)

The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark Towards Physically Realistic Embodied AI. - Chuang Gan 001; Siyuan Zhou; Jeremy Schwartz; Seth Alter; Abhishek Bhandwaldar; Dan Gutfreund; Daniel L. K. Yamins; James J. DiCarlo; Josh H. McDermott; Antonio Torralba 001; Joshua B. Tenenbaum (ICRA, 2022)

DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model. - Nishad Gothoskar; Miguel Lázaro-Gredilla; Yasemin Bekiroglu; Abhishek Agarwal; Joshua B. Tenenbaum; Vikash K. Mansinghka; Dileep George (ICRA, 2022)

Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation. - Anthony Simeonov; Yilun Du; Andrea Tagliasacchi; Joshua B. Tenenbaum; Alberto Rodriguez 003; Pulkit Agrawal 001; Vincent Sitzmann (ICRA, 2022)

Incorporating Rich Social Interactions Into MDPs. - Ravi Tejwani; Yen-Ling Kuo; Tianmin Shu; Bennett Stankovits; Dan Gutfreund; Joshua B. Tenenbaum; Boris Katz; Andrei Barbu (ICRA, 2022)

Learning Neuro-Symbolic Relational Transition Models for Bilevel Planning. - Rohan Chitnis; Tom Silver; Joshua B. Tenenbaum; Tomás Lozano-Pérez; Leslie Pack Kaelbling (IROS, 2022)

Robust Change Detection Based on Neural Descriptor Fields. - Jiahui Fu 002; Yilun Du; Kurran Singh; Joshua B. Tenenbaum; John J. Leonard (IROS, 2022)

Noisy Agents: Self-supervised Exploration by Predicting Auditory Events. - Chuang Gan 001; Xiaoyu Chen; Phillip Isola; Antonio Torralba 001; Joshua B. Tenenbaum (IROS, 2022)

Communicating Natural Programs to Humans and Machines. - Samuel Acquaviva; Yewen Pu; Marta Kryven; Theodoros Sechopoulos; Catherine Wong; Gabrielle E. Ecanow; Maxwell I. Nye; Michael Henry Tessler; Josh Tenenbaum 001 (NeurIPS, 2022)

Learning Physical Dynamics with Subequivariant Graph Neural Networks. - Jiaqi Han; Wenbing Huang 001; Hengbo Ma; Jiachen Li 001; Josh Tenenbaum 001; Chuang Gan 001 (NeurIPS, 2022)

3D Concept Grounding on Neural Fields. - Yining Hong; Yilun Du; Chunru Lin; Josh Tenenbaum 001; Chuang Gan 001 (NeurIPS, 2022)

When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment. - Zhijing Jin 001; Sydney Levine; Fernando Gonzalez Adauto; Ojasv Kamal; Maarten Sap; Mrinmaya Sachan; Rada Mihalcea; Josh Tenenbaum 001; Bernhard Schölkopf (NeurIPS, 2022)

Drawing out of Distribution with Neuro-Symbolic Generative Models. - Yichao Liang; Josh Tenenbaum 001; Tuan Anh Le 001; N. Siddharth 001 (NeurIPS, 2022)

Learning Neural Acoustic Fields. - Andrew F. Luo; Yilun Du; Michael J. Tarr; Josh Tenenbaum 001; Antonio Torralba 001; Chuang Gan 001 (NeurIPS, 2022)

PDSketch: Integrated Domain Programming, Learning, and Planning. - Jiayuan Mao; Tomás Lozano-Pérez; Josh Tenenbaum 001; Leslie Pack Kaelbling (NeurIPS, 2022)

HandMeThat: Human-Robot Communication in Physical and Social Environments. - Yanming Wan; Jiayuan Mao; Josh Tenenbaum 001 (NeurIPS, 2022)

Designing Perceptual Puzzles by Differentiating Probabilistic Programs. - Kartik Chandra; Tzu-Mao Li; Joshua B. Tenenbaum; Jonathan Ragan-Kelley (SIGGRAPH (Conference Paper Track), 2022)

When Is It Acceptable to Break the Rules? Knowledge Representation of Moral Judgement Based on Empirical Data. - Edmond Awad; Sydney Levine; Andrea Loreggia; Nicholas Mattei; Iyad Rahwan; Francesca Rossi 001; Kartik Talamadupula; Joshua B. Tenenbaum; Max Kleiman-Weiner (CoRR, 2022)

Inventing Relational State and Action Abstractions for Effective and Efficient Bilevel Planning. - Tom Silver; Rohan Chitnis; Nishanth Kumar; Willie McClinton; Tomás Lozano-Pérez; Leslie Pack Kaelbling; Joshua B. Tenenbaum (CoRR, 2022)

Seeing 3D Objects in a Single Image via Self-Supervised Static-Dynamic Disentanglement. - Prafull Sharma; Ayush Tewari; Yilun Du; Sergey Zakharov; Rares Ambrus; Adrien Gaidon; William T. Freeman; Frédo Durand; Joshua B. Tenenbaum; Vincent Sitzmann (CoRR, 2022)

Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of Mind. - Tan Zhi-Xuan; Nishad Gothoskar; Falk Pollok; Dan Gutfreund; Joshua B. Tenenbaum; Vikash K. Mansinghka (CoRR, 2022)

Abstract Interpretation for Generalized Heuristic Search in Model-Based Planning. - Tan Zhi-Xuan; Joshua B. Tenenbaum; Vikash K. Mansinghka (CoRR, 2022)

When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment. - Zhijing Jin 001; Sydney Levine; Fernando Gonzalez; Ojasv Kamal; Maarten Sap; Mrinmaya Sachan; Rada Mihalcea; Josh Tenenbaum 001; Bernhard Schölkopf (CoRR, 2022)

Retrospectives on the Embodied AI Workshop. - Matt Deitke; Dhruv Batra; Yonatan Bisk; Tommaso Campari; Angel X. Chang; Devendra Singh Chaplot; Changan Chen; Claudia Pérez-D'Arpino; Kiana Ehsani; Ali Farhadi; Li Fei-Fei 001; Anthony G. Francis; et al. (CoRR, 2022)

Top-Down Synthesis for Library Learning. - Matthew Bowers; Theo X. Olausson; Catherine Wong; Gabriel Grand; Joshua B. Tenenbaum; Kevin Ellis; Armando Solar-Lezama (CoRR, 2022)

Flexible social inference facilitates targeted social learning when rewards are not observable. - Robert D. Hawkins; Andrew M. Berdahl; Alex 'Sandy' Pentland; Joshua B. Tenenbaum; Noah D. Goodman; P. M. Krafft (CoRR, 2022)

Neural Networks special issue on Artificial Intelligence and Brain Science. - Kenji Doya; Karl J. Friston; Masashi Sugiyama; Joshua B. Tenenbaum (Neural Networks, 2022)

Logic, Probability, and Pragmatics in Syllogistic Reasoning. - Michael Henry Tessler; Joshua B. Tenenbaum; Noah D. Goodman (Top. Cogn. Sci., 2022)

Learning Rational Subgoals from Demonstrations and Instructions. - Zhezheng Luo; Jiayuan Mao; Jiajun Wu 001; Tomás Lozano-Pérez; Joshua B. Tenenbaum; Leslie Pack Kaelbling (AAAI, 2023)

Predicate Invention for Bilevel Planning. - Tom Silver; Rohan Chitnis; Nishanth Kumar; Willie McClinton; Tomás Lozano-Pérez; Leslie Pack Kaelbling; Joshua B. Tenenbaum (AAAI, 2023)

Zero-Shot Linear Combinations of Grounded Social Interactions with Linear Social MDPs. - Ravi Tejwani; Yen-Ling Kuo; Tianmin Shu; Bennett Stankovits; Dan Gutfreund; Joshua B. Tenenbaum; Boris Katz; Andrei Barbu (AAAI, 2023)

Storytelling as Inverse Inverse Planning. - Kartik Chandra; Tzu-Mao Li; Josh Tenenbaum 001; Jonathan Ragan-Kelley (CogSci, 2023)

"Just In Time" Representations for Mental Simulation in Intuitive Physics. - Tony Chen 003; Kelsey R. Allen; Samuel J. Cheyette; Josh Tenenbaum 001; Kevin A. Smith 001 (CogSci, 2023)

People seek easily interpretable information. - Samuel J. Cheyette; Frederick Callaway; Neil R. Bramley; Jonathan D. Nelson; Josh Tenenbaum 001 (CogSci, 2023)

Representations of Abstract Relations in Early Childhood. - Nicole Coates; Max H. Siegel; Josh Tenenbaum 001; Laura Schulz (CogSci, 2023)

Perception of Mooney Faces: Extreme Generalization through Inverse Rendering? - Shreya Kapoor; Maximilian Weiherer; Max H. Siegel; Amir Arsalan Soltani; Ilker Yildirim; Josh Tenenbaum 001; Bernhard Egger 001 (CogSci, 2023)

When it's not out of line to get out of line: Principles of universalizability, welfare, and harm. - Joseph Kwon; Tan Zhi-Xuan; Josh Tenenbaum 001; Sydney Levine (CogSci, 2023)

Evaluating statistical language models as pragmatic reasoners. - Benjamin Lipkin; Lionel Wong; Gabriel Grand; Josh Tenenbaum 001 (CogSci, 2023)

Towards a model of confidence judgements in concept learning. - Tracey Mills; Cedegao Zhang; Tony Chen 003; Josh Tenenbaum 001 (CogSci, 2023)

Strategy choice for physical reasoning is (partially) sensitive to cognitive costs. - Thomas Ngo; Samuel J. Cheyette; Josh Tenenbaum 001; Kevin A. Smith 001 (CogSci, 2023)

Grounded physical language understanding with probabilistic programs and simulated worlds. - Cedegao Zhang; Lionel Wong; Gabriel Grand; Josh Tenenbaum 001 (CogSci, 2023)

Language Models as Informative Goal Priors in a Bayesian Theory of Mind. - Tan Zhi-Xuan; Paul Stefan Lunis; Nathalie Fernandez Echeverri; Vikash Mansinghka 001; Josh Tenenbaum 001 (CogSci, 2023)

Learning Reusable Manipulation Strategies. - Jiayuan Mao; Tomás Lozano-Pérez; Joshua B. Tenenbaum; Leslie Pack Kaelbling (CoRL, 2023)

Compositional Diffusion-Based Continuous Constraint Solvers. - Zhutian Yang; Jiayuan Mao; Yilun Du; Jiajun Wu 001; Joshua B. Tenenbaum; Tomás Lozano-Pérez; Leslie Pack Kaelbling (CoRL, 2023)

Are Deep Neural Networks SMARTer Than Second Graders? - Anoop Cherian; Kuan-Chuan Peng; Suhas Lohit; Kevin A. Smith 001; Joshua B. Tenenbaum (CVPR, 2023)

Visual Dependency Transformers: Dependency Tree Emerges from Reversed Attention. - Mingyu Ding; Yikang Shen; Lijie Fan; Zhenfang Chen; Zitian Chen; Ping Luo 002; Joshua B. Tenenbaum; Chuang Gan 001 (CVPR, 2023)

3D Concept Learning and Reasoning from Multi-View Images. - Yining Hong; Chunru Lin; Yilun Du; Zhenfang Chen; Joshua B. Tenenbaum; Chuang Gan 001 (CVPR, 2023)

LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers. - Theo Olausson; Alex Gu; Benjamin Lipkin; Cedegao E. Zhang; Armando Solar-Lezama; Joshua B. Tenenbaum; Roger Levy (EMNLP, 2023)

Unsupervised Compositional Concepts Discovery with Text-to-Image Generative Models. - Nan Liu 010; Yilun Du; Shuang Li 013; Joshua B. Tenenbaum; Antonio Torralba 001 (ICCV, 2023)

3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation. - Guangyao Zhou; Nishad Gothoskar; Lirui Wang; Joshua B. Tenenbaum; Dan Gutfreund; Miguel Lázaro-Gredilla; Dileep George; Vikash K. Mansinghka (ICCV, 2023)

Is Conditional Generative Modeling all you need for Decision Making? - Anurag Ajay; Yilun Du; Abhi Gupta; Joshua B. Tenenbaum; Tommi S. Jaakkola; Pulkit Agrawal 001 (ICLR, 2023)

Planning with Sequence Models through Iterative Energy Minimization. - Hongyi Chen; Yilun Du; Yiye Chen; Joshua B. Tenenbaum; Patricio A. Vela (ICLR, 2023)

Composing Ensembles of Pre-trained Models via Iterative Consensus. - Shuang Li 013; Yilun Du; Joshua B. Tenenbaum; Antonio Torralba 001; Igor Mordatch (ICLR, 2023)

DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics. - Sizhe Li; Zhiao Huang; Tao Chen 046; Tao Du 001; Hao Su 001; Joshua B. Tenenbaum; Chuang Gan 001 (ICLR, 2023)

Neural Groundplans: Persistent Neural Scene Representations from a Single Image. - Prafull Sharma; Ayush Tewari; Yilun Du; Sergey Zakharov; Rares Andrei Ambrus; Adrien Gaidon; William T. Freeman; Frédo Durand; Joshua B. Tenenbaum; Vincent Sitzmann (ICLR, 2023)

SoftZoo: A Soft Robot Co-design Benchmark For Locomotion In Diverse Environments. - Tsun-Hsuan Wang; Pingchuan Ma 002; Andrew Everett Spielberg; Zhou Xian; Hao Zhang; Joshua B. Tenenbaum; Daniela Rus; Chuang Gan 001 (ICLR, 2023)

Planning with Large Language Models for Code Generation. - Shun Zhang; Zhenfang Chen; Yikang Shen; Mingyu Ding; Joshua B. Tenenbaum; Chuang Gan 001 (ICLR, 2023)

Learning Neural Constitutive Laws from Motion Observations for Generalizable PDE Dynamics. - Pingchuan Ma 002; Peter Yichen Chen; Bolei Deng; Joshua B. Tenenbaum; Tao Du 001; Chuang Gan 001; Wojciech Matusik (ICML, 2023)

Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC. - Yilun Du; Conor Durkan; Robin Strudel; Joshua B. Tenenbaum; Sander Dieleman; Rob Fergus; Jascha Sohl-Dickstein; Arnaud Doucet; Will Sussman Grathwohl (ICML, 2023)

Inferring Relational Potentials in Interacting Systems. - Armand Comas Massague; Yilun Du; Christian Fernandez Lopez; Sandesh Ghimire; Mario Sznaier; Joshua B. Tenenbaum; Octavia I. Camps (ICML, 2023)

On the Complexity of Bayesian Generalization. - Yu-Zhe Shi; Manjie Xu; John E. Hopcroft; Kun He 001; Joshua B. Tenenbaum; Song-Chun Zhu; Ying Nian Wu; Wenjuan Han; Yixin Zhu 001 (ICML, 2023)

H-SAUR: Hypothesize, Simulate, Act, Update, and Repeat for Understanding Object Articulations from Interactions. - Kei Ota; Hsiao-Yu Tung; Kevin A. Smith 001; Anoop Cherian; Tim K. Marks; Alan Sullivan; Asako Kanezaki; Joshua B. Tenenbaum (ICRA, 2023)

NOPA: Neurally-guided Online Probabilistic Assistance for Building Socially Intelligent Home Assistants. - Xavier Puig; Tianmin Shu; Joshua B. Tenenbaum; Antonio Torralba 001 (ICRA, 2023)

Compositional Foundation Models for Hierarchical Planning. - Anurag Ajay; Seungwook Han; Yilun Du; Shuang Li 013; Abhi Gupta; Tommi S. Jaakkola; Joshua B. Tenenbaum; Leslie Pack Kaelbling; Akash Srivastava; Pulkit Agrawal 001 (NeurIPS, 2023)

Inferring the Future by Imagining the Past. - Kartik Chandra; Tony Chen 003; Tzu-Mao Li; Jonathan Ragan-Kelley; Josh Tenenbaum 001 (NeurIPS, 2023)

Learning Universal Policies via Text-Guided Video Generation. - Yilun Du; Sherry Yang 001; Bo Dai 001; Hanjun Dai; Ofir Nachum; Josh Tenenbaum 001; Dale Schuurmans; Pieter Abbeel (NeurIPS, 2023)

What's Left? Concept Grounding with Logic-Enhanced Foundation Models. - Joy Hsu; Jiayuan Mao; Joshua B. Tenenbaum; Jiajun Wu 001 (NeurIPS, 2023)

What Planning Problems Can A Relational Neural Network Solve? - Jiayuan Mao; Tomás Lozano-Pérez; Joshua B. Tenenbaum; Leslie Pack Kaelbling (NeurIPS, 2023)

Human spatiotemporal pattern learning as probabilistic program synthesis. - Tracey Mills; Josh Tenenbaum 001; Samuel J. Cheyette (NeurIPS, 2023)

Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision. - Ayush Tewari; Tianwei Yin; George Cazenavette; Semon Rezchikov; Josh Tenenbaum 001; Frédo Durand; Bill Freeman; Vincent Sitzmann (NeurIPS, 2023)

Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties. - Hsiao-Yu Tung; Mingyu Ding; Zhenfang Chen; Daniel Bear; Chuang Gan 001; Josh Tenenbaum 001; Dan Yamins; Judith E. Fan; Kevin A. Smith 001 (NeurIPS, 2023)

DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models. - Tsun-Hsuan Johnson Wang; Juntian Zheng; Pingchuan Ma 002; Yilun Du; Byungchul Kim; Andrew Spielberg; Joshua B. Tenenbaum; Chuang Gan 001; Daniela Rus (NeurIPS, 2023)

3D-IntPhys: Towards More Generalized 3D-grounded Visual Intuitive Physics under Challenging Scenes. - Haotian Xue; Antonio Torralba 001; Josh Tenenbaum 001; Dan Yamins; Yunzhu Li; Hsiao-Yu Tung (NeurIPS, 2023)

NeuSE: Neural SE(3)-Equivariant Embedding for Consistent Spatial Understanding with Objects. - Jiahui Fu 002; Yilun Du; Kurran Singh; Joshua B. Tenenbaum; John J. Leonard (Robotics: Science and Systems, 2023)

ConceptFusion: Open-set multimodal 3D mapping. - Krishna Murthy Jatavallabhula; Alihusein Kuwajerwala; Qiao Gu; Mohd. Omama; Ganesh Iyer; Soroush Saryazdi; Tao Chen 046; Alaa Maalouf; Shuang Li 013; Nikhil Varma Keetha; Ayush Tewari; Joshua B. Tenenbaum; et al. (Robotics: Science and Systems, 2023)

Tactile-Filter: Interactive Tactile Perception for Part Mating. - Kei Ota; Devesh K. Jha; Hsiao-Yu Tung; Joshua B. Tenenbaum (Robotics: Science and Systems, 2023)

Acting as Inverse Inverse Planning. - Kartik Chandra; Tzu-Mao Li; Joshua B. Tenenbaum; Jonathan Ragan-Kelley (SIGGRAPH (Conference Paper Track), 2023)

3D Shape Perception Integrates Intuitive Physics and Analysis-by-Synthesis. - Ilker Yildirim; Max H. Siegel; Amir Arsalan Soltani; Shraman Ray Chaudhari; Joshua B. Tenenbaum (CoRR, 2023)

Dissociating language and thought in large language models: a cognitive perspective. - Kyle Mahowald; Anna A. Ivanova; Idan Asher Blank; Nancy Kanwisher; Joshua B. Tenenbaum; Evelina Fedorenko (CoRR, 2023)

3D Neural Embedding Likelihood for Robust Sim-to-Real Transfer in Inverse Graphics. - Guangyao Zhou; Nishad Gothoskar; Lirui Wang; Joshua B. Tenenbaum; Dan Gutfreund; Miguel Lázaro-Gredilla; Dileep George; Vikash K. Mansinghka (CoRR, 2023)

ConceptFusion: Open-set Multimodal 3D Mapping. - Krishna Murthy Jatavallabhula; Alihusein Kuwajerwala; Qiao Gu; Mohd. Omama; Tao Chen 046; Shuang Li 013; Ganesh Iyer; Soroush Saryazdi; Nikhil Varma Keetha; Ayush Tewari; Joshua B. Tenenbaum; Celso Miguel de Melo; et al. (CoRR, 2023)

On the Expressiveness and Generalization of Hypergraph Neural Networks. - Zhezheng Luo; Jiayuan Mao; Joshua B. Tenenbaum; Leslie Pack Kaelbling (CoRR, 2023)

PDSketch: Integrated Planning Domain Programming and Learning. - Jiayuan Mao; Tomás Lozano-Pérez; Joshua B. Tenenbaum; Leslie Pack Kaelbling (CoRR, 2023)

Evaluating Language Models for Mathematics through Interactions. - Katherine M. Collins; Albert Q. Jiang; Simon Frieder; Lionel Wong; Miri Zilka; Umang Bhatt; Thomas Lukasiewicz; Yuhuai Wu; Joshua B. Tenenbaum; William Hart; Timothy Gowers; Wenda Li; et al. (CoRR, 2023)

Probabilistic Adaptation of Text-to-Video Models. - Mengjiao Yang 001; Yilun Du; Bo Dai 001; Dale Schuurmans; Joshua B. Tenenbaum; Pieter Abbeel (CoRR, 2023)

From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought. - Lionel Wong; Gabriel Grand; Alexander K. Lew; Noah D. Goodman; Vikash K. Mansinghka; Jacob Andreas; Joshua B. Tenenbaum (CoRR, 2023)

The Neuro-Symbolic Inverse Planning Engine (NIPE): Modeling Probabilistic Social Inferences from Linguistic Inputs. - Lance Ying; Katherine M. Collins; Megan Wei; Cedegao E. Zhang; Tan Zhi-Xuan; Adrian Weller; Joshua B. Tenenbaum; Lionel Wong (CoRR, 2023)

Inferring the Goals of Communicating Agents from Actions and Instructions. - Lance Ying; Tan Zhi-Xuan; Vikash Mansinghka 001; Joshua B. Tenenbaum (CoRR, 2023)

The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence. - Hector Zenil; Jesper Tegnér; Felipe S. Abrahão; Alexander Lavin; Vipin Kumar 001; Jeremy G. Frey; Adrian Weller; Larisa N. Soldatova; Alan R. Bundy; Nicholas R. Jennings; Koichi Takahashi; Lawrence Hunter; et al. (CoRR, 2023)

Approaching human 3D shape perception with neurally mappable models. - Thomas P. O'Connell; Tyler Bonnen; Yoni Friedman; Ayush Tewari; Joshua B. Tenenbaum; Vincent Sitzmann; Nancy Kanwisher (CoRR, 2023)

Learning a Hierarchical Planner from Humans in Multiple Generations. - Leonardo Hernandez Cano; Yewen Pu; Robert D. Hawkins; Joshua B. Tenenbaum; Armando Solar-Lezama (CoRR, 2023)

Getting aligned on representational alignment. - Ilia Sucholutsky; Lukas Muttenthaler; Adrian Weller; Andi Peng; Andreea Bobu; Been Kim; Bradley C. Love; Erin Grant; Jascha Achterberg; Joshua B. Tenenbaum; Katherine M. Collins; Katherine L. Hermann; et al. (CoRR, 2023)

AI for Mathematics: A Cognitive Science Perspective. - Cedegao E. Zhang; Katherine M. Collins; Adrian Weller; Joshua B. Tenenbaum (CoRR, 2023)

How to guess a gradient. - Utkarsh Singhal; Brian Cheung; Kartik Chandra; Jonathan Ragan-Kelley; Joshua B. Tenenbaum; Tomaso A. Poggio; Stella X. Yu (CoRR, 2023)

Learning adaptive planning representations with natural language guidance. - Lionel Wong; Jiayuan Mao; Pratyusha Sharma; Zachary S. Siegel; Jiahai Feng; Noa Korneev; Joshua B. Tenenbaum; Jacob Andreas (CoRR, 2023)

Bayes3D: fast learning and inference in structured generative models of 3D objects and scenes. - Nishad Gothoskar; Matin Ghavami; Eric Li; Aidan Curtis; Michael Noseworthy; Karen Chung; Brian Patton; William T. Freeman; Joshua B. Tenenbaum; Mirko Klukas; Vikash K. Mansinghka (CoRR, 2023)

Unsupervised Discovery and Composition of Object Light Fields. - Cameron Smith; Hong-Xing Yu; Sergey Zakharov; Frédo Durand; Joshua B. Tenenbaum; Jiajun Wu 001; Vincent Sitzmann (Trans. Mach. Learn. Res., 2023)

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. - Aarohi Srivastava; Abhinav Rastogi; Abhishek Rao; Abu Awal Md Shoeb; Abubakar Abid; Adam Fisch; Adam R. Brown; Adam Santoro; Aditya Gupta; Adrià Garriga-Alonso; Agnieszka Kluska; Aitor Lewkowycz; et al. (Trans. Mach. Learn. Res., 2023)

Storytelling as Inverse Inverse Planning. - Kartik Chandra; Tzu-Mao Li; Joshua B. Tenenbaum; Jonathan Ragan-Kelley (Top. Cogn. Sci., 2023)

Top-Down Synthesis for Library Learning. - Matthew Bowers; Theo X. Olausson; Lionel Wong; Gabriel Grand; Joshua B. Tenenbaum; Kevin Ellis; Armando Solar-Lezama (Proc. ACM Program. Lang., 2023)

Combining Functional and Automata Synthesis to Discover Causal Reactive Programs. - Ria Das; Joshua B. Tenenbaum; Armando Solar-Lezama; Zenna Tavares (Proc. ACM Program. Lang., 2023)

MOST-GAN Pre-trained Model. - Safa C. Medin; Bernhard Egger 001; Anoop Cherian; Ye Wang 001; Joshua B. Tenenbaum; Xiaoming Liu 002; Tim K. Marks (Zenodo, 2023)

Neural Amortized Inference for Nested Multi-Agent Reasoning. - Kunal Jha; Tuan Anh Le 001; Chuanyang Jin; Yen-Ling Kuo; Joshua B. Tenenbaum; Tianmin Shu (AAAI, 2024)

Generalized Planning in PDDL Domains with Pretrained Large Language Models. - Tom Silver; Soham Dan; Kavitha Srinivas; Joshua B. Tenenbaum; Leslie Pack Kaelbling; Michael Katz 001 (AAAI, 2024)

MMToM-QA: Multimodal Theory of Mind Question Answering. - Chuanyang Jin; Yutong Wu; Jing Cao; Jiannan Xiang; Yen-Ling Kuo; Zhiting Hu; Tomer D. Ullman; Antonio Torralba 001; Joshua B. Tenenbaum; Tianmin Shu (ACL (1), 2024)

Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse Planning. - Tan Zhi-Xuan; Lance Ying; Vikash Mansinghka 001; Joshua B. Tenenbaum (AAMAS, 2024)

Video Language Planning. - Yilun Du; Sherry Yang 001; Pete Florence; Fei Xia 002; Ayzaan Wahid; Brian Ichter; Pierre Sermanet; Tianhe Yu; Pieter Abbeel; Joshua B. Tenenbaum; Leslie Pack Kaelbling; Andy Zeng 001; et al. (ICLR, 2024)

LILO: Learning Interpretable Libraries by Compressing and Documenting Code. - Gabriel Grand; Lionel Wong; Matthew Bowers; Theo X. Olausson; Muxin Liu; Joshua B. Tenenbaum; Jacob Andreas (ICLR, 2024)

Learning to Act from Actionless Videos through Dense Correspondences. - Po-Chen Ko; Jiayuan Mao; Yilun Du; Shao-Hua Sun; Joshua B. Tenenbaum (ICLR, 2024)

Learning to Jointly Understand Visual and Tactile Signals. - Yichen Li 004; Yilun Du; Chao Liu 021; Chao Liu 064; Francis Williams; Michael Foshey; Benjamin Eckart; Jan Kautz; Joshua B. Tenenbaum; Antonio Torralba 001; Wojciech Matusik (ICLR, 2024)

Learning Grounded Action Abstractions from Language. - Lionel Wong; Jiayuan Mao; Pratyusha Sharma; Zachary S. Siegel; Jiahai Feng; Noa Korneev; Joshua B. Tenenbaum; Jacob Andreas (ICLR, 2024)

Probabilistic Adaptation of Black-Box Text-to-Video Models. - Sherry Yang 001; Yilun Du; Bo Dai 001; Dale Schuurmans; Joshua B. Tenenbaum; Pieter Abbeel (ICLR, 2024)

Building Cooperative Embodied Agents Modularly with Large Language Models. - Hongxin Zhang; Weihua Du; Jiaming Shan; Qinhong Zhou; Yilun Du; Joshua B. Tenenbaum; Tianmin Shu; Chuang Gan 001 (ICLR, 2024)

HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environments. - Qinhong Zhou; Sunli Chen; Yisong Wang; Haozhe Xu; Weihua Du; Hongxin Zhang; Yilun Du; Joshua B. Tenenbaum; Chuang Gan 001 (ICLR, 2024)

Improving Factuality and Reasoning in Language Models through Multiagent Debate. - Yilun Du; Shuang Li 013; Antonio Torralba 001; Joshua B. Tenenbaum; Igor Mordatch (ICML, 2024)

Learning Iterative Reasoning through Energy Diffusion. - Yilun Du; Jiayuan Mao; Joshua B. Tenenbaum (ICML, 2024)

Potential Based Diffusion Motion Planning. - Yunhao Luo 001; Chen Sun 002; Joshua B. Tenenbaum; Yilun Du (ICML, 2024)

LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery. - Pingchuan Ma 002; Tsun-Hsuan Wang; Minghao Guo; Zhiqing Sun; Joshua B. Tenenbaum; Daniela Rus; Chuang Gan 001; Wojciech Matusik (ICML, 2024)

Compositional Image Decomposition with Diffusion Models. - Jocelin Su; Nan Liu 010; Yanbo Wang; Joshua B. Tenenbaum; Yilun Du (ICML, 2024)

ContPhy: Continuum Physical Concept Learning and Reasoning from Videos. - Zhicheng Zheng; Xin Yan 008; Zhenfang Chen; Jingzhou Wang; Qin Zhi Eddie Lim; Joshua B. Tenenbaum; Chuang Gan 001 (ICML, 2024)

ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning. - Qiao Gu; Ali Kuwajerwala; Sacha Morin; Krishna Murthy Jatavallabhula; Bipasha Sen; Aditya Agarwal; Corban Rivera; William Paul; Kirsty Ellis; Rama Chellappa; Chuang Gan 001; Celso Miguel de Melo; et al. (ICRA, 2024)

Tactile Estimation of Extrinsic Contact Patch for Stable Placement. - Kei Ota; Devesh K. Jha; Krishna Murthy Jatavallabhula; Asako Kanezaki; Joshua B. Tenenbaum (ICRA, 2024)

PickScan: Object discovery and reconstruction from handheld interactions. - Vincent van der Brugge; Marc Pollefeys; Joshua B. Tenenbaum; Krishna Murthy Jatavallabhula; Ayush Tewari (IROS, 2024)

GOMA: Proactive Embodied Cooperative Communication via Goal-Oriented Mental Alignment. - Lance Ying; Kunal Jha; Shivam Aarya; Joshua B. Tenenbaum; Antonio Torralba 001; Tianmin Shu (IROS, 2024)

Evaluating Multiview Object Consistency in Humans and Image Models. - Tyler Bonnen; Stephanie Fu; Yutong Bai; Thomas P. O'Connell; Yoni Friedman; Nancy Kanwisher; Josh Tenenbaum 001; Alexei A. Efros (NeurIPS, 2024)

Evaluating Large Vision-and-Language Models on Children's Mathematical Olympiads. - Anoop Cherian; Kuan-Chuan Peng; Suhas Lohit; Joanna Matthiesen; Kevin A. Smith 001; Josh Tenenbaum 001 (NeurIPS, 2024)

Physically Compatible 3D Object Modeling from a Single Image. - Minghao Guo; Bohan Wang; Pingchuan Ma 002; Tianyuan Zhang; Crystal Elaine Owens; Chuang Gan 001; Josh Tenenbaum 001; Kaiming He; Wojciech Matusik (NeurIPS, 2024)

Few-Shot Task Learning through Inverse Generative Modeling. - Aviv Netanyahu; Yilun Du; Antonia Bronars; Jyothish Pari; Josh Tenenbaum 001; Tianmin Shu; Pulkit Agrawal 001 (NeurIPS, 2024)

Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness. - Aidan Curtis; George Matheos; Nishad Gothoskar; Vikash Mansinghka 001; Joshua B. Tenenbaum; Tomás Lozano-Pérez; Leslie Pack Kaelbling (Robotics: Science and Systems, 2024)

Sketching With Your Voice: "Non-Phonorealistic" Rendering of Sounds via Vocal Imitation. - Matthew Caren; Kartik Chandra; Joshua B. Tenenbaum; Jonathan Ragan-Kelley; Karima Ma (SIGGRAPH Asia, 2024)

How does the primate brain combine generative and discriminative computations in vision? - Benjamin Peters; James J. DiCarlo; Todd M. Gureckis; Ralf Haefner; Leyla Isik; Joshua B. Tenenbaum; Talia Konkle; Thomas Naselaris; Kimberly L. Stachenfeld; Zenna Tavares; Doris Tsao; Ilker Yildirim; et al. (CoRR, 2024)

Grounding Language about Belief in a Bayesian Theory-of-Mind. - Lance Ying; Tan Zhi-Xuan; Lionel Wong; Vikash Mansinghka 001; Joshua B. Tenenbaum (CoRR, 2024)

Loose LIPS Sink Ships: Asking Questions in Battleship with Language-Informed Program Sampling. - Gabriel Grand; Valerio Pepe; Jacob Andreas; Joshua B. Tenenbaum (CoRR, 2024)

WatChat: Explaining perplexing programs by debugging mental models. - Kartik Chandra; Tzu-Mao Li; Rachit Nigam; Joshua B. Tenenbaum; Jonathan Ragan-Kelley (CoRR, 2024)

Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems. - David Dalrymple; Joar Skalse; Yoshua Bengio; Stuart Russell 001; Max Tegmark; Sanjit Seshia; Steve Omohundro; Christian Szegedy; Ben Goldhaber; Nora Ammann; Alessandro Abate; Joe Halpern; et al. (CoRR, 2024)

Finding structure in logographic writing with library learning. - Guangyuan Jiang; Matthias Hofer 002; Jiayuan Mao; Lionel Wong; Joshua B. Tenenbaum; Roger P. Levy (CoRR, 2024)

Elements of World Knowledge (EWOK): A cognition-inspired framework for evaluating basic world knowledge in language models. - Anna A. Ivanova; Aalok Sathe; Benjamin Lipkin; Unnathi Kumar; Setayesh Radkani; Thomas Hikaru Clark; Carina Kauf; Jennifer Hu 001; R. T. Pramod; Gabriel Grand; Vivian C. Paulun; Maria Ryskina; et al. (CoRR, 2024)

Representational Alignment Supports Effective Machine Teaching. - Ilia Sucholutsky; Katherine M. Collins; Maya Malaviya; Nori Jacoby; Weiyang Liu; Theodore R. Sumers; Michalis Korakakis; Umang Bhatt; Mark K. Ho; Joshua B. Tenenbaum; Bradley C. Love; Zachary A. Pardos; et al. (CoRR, 2024)

People use fast, goal-directed simulation to reason about novel games. - Cedegao E. Zhang; Katherine M. Collins; Lionel Wong; Adrian Weller; Joshua B. Tenenbaum (CoRR, 2024)

Infinite Ends from Finite Samples: Open-Ended Goal Inference as Top-Down Bayesian Filtering of Bottom-Up Proposals. - Tan Zhi-Xuan; Gloria Kang; Vikash Mansinghka 001; Joshua B. Tenenbaum (CoRR, 2024)

Building Machines that Learn and Think with People. - Katherine M. Collins; Ilia Sucholutsky; Umang Bhatt; Kartik Chandra; Lionel Wong; Mina Lee 002; Cedegao E. Zhang; Tan Zhi-Xuan; Mark K. Ho; Vikash Mansinghka 001; Adrian Weller; Joshua B. Tenenbaum; et al. (CoRR, 2024)

Understanding Epistemic Language with a Bayesian Theory of Mind. - Lance Ying; Tan Zhi-Xuan; Lionel Wong; Vikash Mansinghka 001; Joshua B. Tenenbaum (CoRR, 2024)

SIFToM: Robust Spoken Instruction Following through Theory of Mind. - Lance Ying; Jason Xinyu Liu; Shivam Aarya; Yizirui Fang; Stefanie Tellex; Joshua B. Tenenbaum; Tianmin Shu (CoRR, 2024)

Learning Linear Attention in Polynomial Time. - Morris Yau; Ekin Akyürek; Jiayuan Mao; Joshua B. Tenenbaum; Stefanie Jegelka; Jacob Andreas (CoRR, 2024)

Keypoint Abstraction using Large Models for Object-Relative Imitation Learning. - Xiaolin Fang 002; Bo-Ruei Huang; Jiayuan Mao; Jasmine Shone; Joshua B. Tenenbaum; Tomás Lozano-Pérez; Leslie Pack Kaelbling (CoRR, 2024)

One-Shot Manipulation Strategy Learning by Making Contact Analogies. - Yuyao Liu; Jiayuan Mao; Joshua B. Tenenbaum; Tomás Lozano-Pérez; Leslie Pack Kaelbling (CoRR, 2024)

Functional Risk Minimization. - Ferran Alet; Clement Gehring; Tomás Lozano-Pérez; Kenji Kawaguchi; Joshua B. Tenenbaum; Leslie Pack Kaelbling (CoRR, 2024)

Approximate planning in spatial search. - Marta Kryven; Suhyoun Yu; Max Kleiman-Weiner; Tomer D. Ullman; Joshua B. Tenenbaum (PLoS Comput. Biol., 2024)

Unsupervised 3D Scene Representation Learning via Movable Object Inference. - Honglin Chen; Wanhee Lee; Hong-Xing Yu; Rahul Mysore Venkatesh; Joshua B. Tenenbaum; Daniel Bear; Jiajun Wu 001; Daniel L. K. Yamins (Trans. Mach. Learn. Res., 2024)

Building 3D Generative Models from Minimal Data. - Skylar Sutherland; Bernhard Egger 001; Joshua B. Tenenbaum (Int. J. Comput. Vis., 2024)

memo. - Kartik Chandra; Tony Chen 003; Josh Tenenbaum 001; Jonathan Ragan-Kelley (Zenodo, 2024)

When is it acceptable to break the rules? Knowledge representation of moral judgements based on empirical data. - Edmond Awad; Sydney Levine; Andrea Loreggia; Nicholas Mattei; Iyad Rahwan; Francesca Rossi 001; Kartik Talamadupula; Joshua B. Tenenbaum; Max Kleiman-Weiner (Auton. Agents Multi Agent Syst., 2024)

What Makes a Maze Look Like a Maze? - Joy Hsu; Jiayuan Mao; Joshua B. Tenenbaum; Noah D. Goodman; Jiajun Wu 001 (ICLR, 2025)

VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning. - Yichao Liang; Nishanth Kumar; Hao Tang 008; Adrian Weller; Joshua B. Tenenbaum; Tom Silver; João F. Henriques; Kevin Ellis (ICLR, 2025)

Can Large Language Models Understand Symbolic Graphics Programs? - Zeju Qiu; Weiyang Liu; Haiwen Feng; Zhen Liu 019; Tim Z. Xiao; Katherine M. Collins; Joshua B. Tenenbaum; Adrian Weller; Michael J. Black; Bernhard Schölkopf (ICLR, 2025)

Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains. - Vighnesh Subramaniam; Yilun Du; Joshua B. Tenenbaum; Antonio Torralba 001; Shuang Li 013; Igor Mordatch (ICLR, 2025)

Vision CNNs trained to estimate spatial latents learned similar ventral-stream-aligned representations. - Yudi Xie; Weichen Huang; Esther Alter; Jeremy Schwartz; Joshua B. Tenenbaum; James J. DiCarlo (ICLR, 2025)

When Is It Acceptable to Break the Rules? Knowledge Representation of Moral Judgements Based on Empirical Data (Extended Abstract). - Edmond Awad; Sydney Levine; Andrea Loreggia; Nicholas Mattei; Iyad Rahwan; Francesca Rossi 001; Kartik Talamadupula; Joshua B. Tenenbaum; Max Kleiman-Weiner (AAMAS, 2025)

Stochastic Lazy Knowledge Compilation for Inference in Discrete Probabilistic Programs. - Maddy Bowers; Alexander K. Lew; Joshua B. Tenenbaum; Armando Solar-Lezama; Vikash K. Mansinghka (Proc. ACM Program. Lang., 2025)

Understanding Epistemic Language with a Language-augmented Bayesian Theory of Mind. - Lance Ying; Tan Zhi-Xuan; Lionel Wong; Vikash Mansinghka 001; Joshua B. Tenenbaum (Trans. Assoc. Comput. Linguistics, 2025)

Synthesizing world models for bilevel planning. - Zergham Ahmed; Joshua B. Tenenbaum; Christopher Bates; Samuel J. Gershman (Trans. Mach. Learn. Res., 2025)

Resource-Rational Virtual Bargaining for Moral Judgment: Toward a Probabilistic Cognitive Model. - Diego Trujillo; Mindy Zhang; Tan Zhi-Xuan; Joshua B. Tenenbaum; Sydney Levine (Top. Cogn. Sci., 2025)

Hypothesis-Driven Theory-of-Mind Reasoning for Large Language Models. - Hyunwoo Kim 002; Melanie Sclar; Tan Zhi-Xuan; Lance Ying; Sydney Levine; Yang Liu; Joshua B. Tenenbaum; Yejin Choi 001 (CoRR, 2025)

Testing the limits of fine-tuning to improve reasoning in vision language models. - Luca M. Schulze Buschoff; Konstantinos Voudouris; Elif Akata; Matthias Bethge; Joshua B. Tenenbaum; Eric Schulz (CoRR, 2025)

On Benchmarking Human-Like Intelligence in Machines. - Lance Ying; Katherine M. Collins; Lionel Wong; Ilia Sucholutsky; Ryan Liu 001; Adrian Weller; Tianmin Shu; Thomas L. Griffiths 001; Joshua B. Tenenbaum (CoRR, 2025)

Self-Steering Language Models. - Gabriel Grand; Joshua B. Tenenbaum; Vikash K. Mansinghka; Alexander K. Lew; Jacob Andreas (CoRR, 2025)

LLM-Guided Probabilistic Program Induction for POMDP Model Estimation. - Aidan Curtis; Hao Tang 008; Thiago Veloso; Kevin Ellis; Joshua B. Tenenbaum; Tomás Lozano-Pérez; Leslie Pack Kaelbling (CoRR, 2025)

Neuro-Symbolic Concepts. - Jiayuan Mao; Joshua B. Tenenbaum; Jiajun Wu 001 (CoRR, 2025)

Belief Attribution as Mental Explanation: The Role of Accuracy, Informativity, and Causality. - Lance Ying; Almog Hillel; Ryan Truong; Vikash K. Mansinghka; Joshua B. Tenenbaum; Tan Zhi-Xuan (CoRR, 2025)

Scaling up the think-aloud method. - Daniel Wurgaft; Ben Prystawski; Kanishk Gandhi; Cedegao E. Zhang; Joshua B. Tenenbaum; Noah D. Goodman (CoRR, 2025)

What's in the Box? Reasoning about Unseen Objects from Multimodal Cues. - Lance Ying; Daniel Xu; Alicia Zhang; Katherine M. Collins; Max H. Siegel; Joshua B. Tenenbaum (CoRR, 2025)

Minding the Politeness Gap in Cross-cultural Communication. - Yuka Machino; Matthias Hofer 002; Max H. Siegel; Joshua B. Tenenbaum; Robert D. Hawkins (CoRR, 2025)

Language-Informed Synthesis of Rational Agent Models for Grounded Theory-of-Mind Reasoning On-The-Fly. - Lance Ying; Ryan Truong; Katherine M. Collins; Cedegao E. Zhang; Megan Wei; Tyler Brooke-Wilson; Tan Zhi-Xuan; Lionel Wong; Joshua B. Tenenbaum (CoRR, 2025)

Resource Rational Contractualism Should Guide AI Alignment. - Sydney Levine; Matija Franklin; Tan Zhi-Xuan; Secil Yanik Guyot; Lionel Wong; Daniel Kilov; Yejin Choi 001; Joshua B. Tenenbaum; Noah D. Goodman; Seth Lazar; Iason Gabriel (CoRR, 2025)

Adaptive Social Learning using Theory of Mind. - Lance Ying; Ryan Truong; Joshua B. Tenenbaum; Samuel J. Gershman (CoRR, 2025)

Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models. - Lionel Wong; Katherine M. Collins; Lance Ying; Cedegao E. Zhang; Adrian Weller; Tobias Gerstenberg; Timothy O'Donnell; Alexander K. Lew; Jacob Andreas; Joshua B. Tenenbaum; Tyler Brooke-Wilson (CoRR, 2025)

Assessing Adaptive World Models in Machines with Novel Games. - Lance Ying; Katherine M. Collins; Prafull Sharma; Cedric Colas; Kaiya Ivy Zhao; Adrian Weller; Zenna Tavares; Phillip Isola; Samuel J. Gershman; Jacob Andreas; Thomas L. Griffiths 001; François Chollet; et al. (CoRR, 2025)

Empathy in Explanation. - Katherine M. Collins; Kartik Chandra; Adrian Weller; Jonathan Ragan-Kelley; Joshua B. Tenenbaum (CoRR, 2025)

Generation and Evaluation in the Human Invention Process through the Lens of Game Design. - Katherine M. Collins; Graham Todd; Cedegao E. Zhang; Adrian Weller; Julian Togelius; Junyi Chu; Lionel Wong; Thomas L. Griffiths 001; Joshua B. Tenenbaum (CoRR, 2025)

Meschers: Geometry Processing of Impossible Objects. - Ana Dodik; Isabella Yu; Kartik Chandra; Jonathan Ragan-Kelley; Joshua B. Tenenbaum; Vincent Sitzmann; Justin Solomon 001 (ACM Trans. Graph., 2025)

Compositional Physical Reasoning of Objects and Events From Videos. - Zhenfang Chen; Shilong Dong; Kexin Yi; Yunzhu Li; Mingyu Ding; Antonio Torralba 001; Joshua B. Tenenbaum; Chuang Gan 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2025)