About
 

Joshua Tenenbaum

Co-Scientific Director, MIT Quest for Intelligence; Professor, Brain and Cognitive Sciences; MacArthur Fellow

Faculty Website Research Publications

Who they work with

Categories

Causal Inference Human-Centered AI Knowledge Representation Machine Learning

Joshua Tenenbaum is a professor of computational cognitive science in MIT’s Department of Brain and Cognitive Sciences and a co-scientific director with the MIT Quest for Intelligence. He is also an investigator at the Center for Brains, Minds and Machines and the Computer Science and Artificial Intelligence Laboratory. Tenenbaum’s research straddles cognitive science and artificial intelligence, where his goals are to reverse engineer human intelligence and to build machines that behave in human-like ways and have greater use to society. Through a combination of mathematical modeling, computer simulation, and behavioral experiments, Tenenbaum tries to uncover the logic behind our everyday inductive leaps: constructing perceptual representations, separating “style” and “content” in perception, learning concepts and words, judging similarity or representativeness, inferring causal connections, noticing coincidences and predicting the future. Tenenbaum is a MacArthur Fellow and has received the National Academy of Sciences’ Troland Research Award. He earned a BA from Yale University, and a PhD in brain and cognitive sciences from MIT.

Publications

Media

Videos

 

Top Work

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

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

Neuro-Symbolic AI

Publications with the MIT-IBM Watson AI Lab

3D Concept Grounding on Neural Fields
3D Concept Grounding on Neural Fields
NeurIPS
 
Learning Neural Acoustic Fields
Learning Neural Acoustic Fields
NeurIPS
 
Learning Physical Dynamics with Subequivariant Graph Neural Networks
Learning Physical Dynamics with Subequivariant Graph Neural Networks
NeurIPS
 
Music Gesture for Visual Sound Separation
Music Gesture for Visual Sound Separation
 
Prompting Decision Transformer for Few-shot Policy Generalization
Prompting Decision Transformer for Few-shot Policy Generalization
ICML
 
Finding Fallen Objects Via Asynchronous Audio-Visual Integration
Finding Fallen Objects Via Asynchronous Audio-Visual Integration
CVPR
 
Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction
Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction
CVPR
 
RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation
RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation
ICLR
 
Linking Emergent and Natural Languages via Corpus Transfer
Linking Emergent and Natural Languages via Corpus Transfer
ICLR
 
Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics
Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics
ICLR
 
DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools
DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools
ICLR
 
Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks
Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks
AAAI
 
GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling
GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling
AAAI
 
Learning Symbolic Operators for Task and Motion Planning
Learning Symbolic Operators for Task and Motion Planning
Robotics
 
Few-Shot Bayesian Imitation Learning with Logical Program Policies
Few-Shot Bayesian Imitation Learning with Logical Program Policies
AAAI
 
Temporal and Object Quantification Networks
Temporal and Object Quantification Networks
IJCAI
 
Discovering State and Action Abstractions for Generalized Task and Motion Planning
Discovering State and Action Abstractions for Generalized Task and Motion Planning
AAAI
 
A large-scale benchmark for few-shot program induction and synthesis
A large-scale benchmark for few-shot program induction and synthesis
ICML
 
A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics
A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics
NeurIPS
 
Grammar-Based Grounded Lexicon Learning
Grammar-Based Grounded Lexicon Learning
NeurIPS
 
Noether networks: meta-learning useful conserved quantities
Noether networks: meta-learning useful conserved quantities
NeurIPS
 
STAR: A Benchmark for Situated Reasoning in Real-World Videos
STAR: A Benchmark for Situated Reasoning in Real-World Videos
NeurIPS
 
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation
NeurIPS
 
3DP3: 3D Scene Perception via Probabilistic Programming
3DP3: 3D Scene Perception via Probabilistic Programming
NeurIPS
 
Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language
Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language
NeurIPS
 
PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning
PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning
NeurIPS
 
FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations
FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations
ICLR
 
Leveraging Language to Learn Program Abstractions and Search Heuristics
Leveraging Language to Learn Program Abstractions and Search Heuristics
ICML
 
Learning Physical Graph Representations from Visual Scenes
Learning Physical Graph Representations from Visual Scenes
NeurIPS
 
Online Bayesian Goal Inference for Boundedly-Rational Planning Agents
Online Bayesian Goal Inference for Boundedly-Rational Planning Agents
NeurIPS
 
Foley Music: Learning to Generate Music from Videos
Foley Music: Learning to Generate Music from Videos
ECCVComputer Vision
 
CLEVRER: The first video dataset for neuro-symbolic reasoning
CLEVRER: The first video dataset for neuro-symbolic reasoning
Neuro-Symbolic AIComputer Vision
 
Deep Audio Priors Emerge From Harmonic Convolutional Networks
Deep Audio Priors Emerge From Harmonic Convolutional Networks
Audio ProcessingDeep Learning
 
ObjectNet: A bias-controlled dataset object recognition
ObjectNet: A bias-controlled dataset object recognition
Computer VisionNeurIPS
 
ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
NeurIPS
 
Visual Concept-Metaconcept Learning
Visual Concept-Metaconcept Learning
Neuro-Symbolic AI
 
Write, Execute, Assess: Program Synthesis with a REPL
Write, Execute, Assess: Program Synthesis with a REPL
Program Synthesis
 
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
Neuro-Symbolic AIComputer Vision
 
Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction
Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction
NeurIPS
 
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
Generative ModelsExplainability
 
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
Neuro-Symbolic AIComputer Vision