Josh Tenenbaum

Learning Universal Policies via Text-Guided Video Generation NIPS 2023 What Planning Problems Can A Relational Neural Network Solve? NIPS 2023 Human spatiotemporal pattern learning as probabilistic program synthesis NIPS 2023 Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties NIPS 2023 DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models NIPS 2023 3D-IntPhys: Towards More Generalized 3D-grounded Visual Intuitive Physics under Challenging Scenes NIPS 2023 What’s Left? Concept Grounding with Logic-Enhanced Foundation Models NIPS 2023 Compositional Foundation Models for Hierarchical Planning NIPS 2023 Inferring the Future by Imagining the Past NIPS 2023 Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision NIPS 2023 Revisiting the Roles of “Text” in Text Games EMNLP 2022 PDSketch: Integrated Domain Programming, Learning, and Planning NIPS 2022 When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment NIPS 2022 Learning Physical Dynamics with Subequivariant Graph Neural Networks NIPS 2022 Drawing out of Distribution with Neuro-Symbolic Generative Models NIPS 2022 HandMeThat: Human-Robot Communication in Physical and Social Environments NIPS 2022 3D Concept Grounding on Neural Fields NIPS 2022 Communicating Natural Programs to Humans and Machines NIPS 2022 Learning Neural Acoustic Fields NIPS 2022 A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics NIPS 2021 Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning NIPS 2021 Learning to Compose Visual Relations NIPS 2021 Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language NIPS 2021 Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering NIPS 2021 PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning NIPS 2021 Noether Networks: meta-learning useful conserved quantities NIPS 2021 Unsupervised Learning of Compositional Energy Concepts NIPS 2021 3DP3: 3D Scene Perception via Probabilistic Programming NIPS 2021 Learning Signal-Agnostic Manifolds of Neural Fields NIPS 2021 Grammar-Based Grounded Lexicon Learning NIPS 2021 Program Synthesis with Pragmatic Communication NIPS 2020 Learning abstract structure for drawing by efficient motor program induction NIPS 2020 Learning Physical Graph Representations from Visual Scenes NIPS 2020 Multi-Plane Program Induction with 3D Box Priors NIPS 2020 Learning Compositional Rules via Neural Program Synthesis NIPS 2020 Online Bayesian Goal Inference for Boundedly Rational Planning Agents NIPS 2020 Few-Shot Bayesian Imitation Learning with Logical Program Policies AAAI 2020 Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations NIPS 2019 Visual Concept-Metaconcept Learning NIPS 2019 ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models NIPS 2019 Finding Friend and Foe in Multi-Agent Games NIPS 2019 Write, Execute, Assess: Program Synthesis with a REPL NIPS 2019 Flexible neural representation for physics prediction NIPS 2018 Visual Object Networks: Image Generation with Disentangled 3D Representations NIPS 2018 End-to-End Differentiable Physics for Learning and Control NIPS 2018 Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction NIPS 2018 Learning to Infer Graphics Programs from Hand-Drawn Images NIPS 2018 3D-Aware Scene Manipulation via Inverse Graphics NIPS 2018 Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding NIPS 2018 Learning to Exploit Stability for 3D Scene Parsing NIPS 2018 Learning to Reconstruct Shapes from Unseen Classes NIPS 2018 Learning to Share and Hide Intentions using Information Regularization NIPS 2018 Shape and Material from Sound NIPS 2017 Self-Supervised Intrinsic Image Decomposition NIPS 2017 MarrNet: 3D Shape Reconstruction via 2.5D Sketches NIPS 2017 Learning to See Physics via Visual De-animation NIPS 2017 Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation NIPS 2016 Sampling for Bayesian Program Learning NIPS 2016 Probing the Compositionality of Intuitive Functions NIPS 2016 Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling NIPS 2016 Softstar: Heuristic-Guided Probabilistic Inference NIPS 2015 Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning NIPS 2015 Deep Convolutional Inverse Graphics Network NIPS 2015 Unsupervised Learning by Program Synthesis NIPS 2015 Risk and Regret of Hierarchical Bayesian Learners ICML 2015 Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs NIPS 2013 One-shot learning by inverting a compositional causal process NIPS 2013