Joshua B. Tenenbaum
- Jonathan H. Huggins, Joshua B. Tenenbaum:
Risk and Regret of Hierarchical Bayesian Learners (pdf) (bibtex)
Proceedings of the 32nd International Conference on Machine Learning
#learning theory, #hierarchical modeling
@article{JonathanH.Huggins:2015:2be49,
author = {Jonathan H. Huggins and Joshua B. Tenenbaum},
journal = {Proceedings of the 32nd International Conference on Machine Learning},
title = {Risk and Regret of Hierarchical Bayesian Learners},
year = {2015},
keywords = {learning theory, hierarchical modeling},
doi = {},
url = {http://arxiv.org/pdf/1505.04984.pdf}
}
- Ilker Yildirim, Tejas D. Kulkarni, Winrich A. Freiwald, Joshua B. Tenenbaum:
Efficient analysis-by-synthesis in vision: A computational framework, behavioral tests, and modeling neuronal representations (pdf) (bibtex)
Proceedings of the Thirty-Seventh Annual Conference of the Cognitive Science Society
#analysis-by-synthesis, #psychophysics, #neural modeling, #face perception, #macaque face patches
@article{IlkerYildirim:2015:65e3f,
author = {Ilker Yildirim and Tejas D. Kulkarni and Winrich A. Freiwald and Joshua B. Tenenbaum},
journal = {Proceedings of the Thirty-Seventh Annual Conference of the Cognitive Science Society},
title = {Efficient analysis-by-synthesis in vision: A computational framework, behavioral tests, and modeling neuronal representations},
year = {2015},
keywords = {analysis-by-synthesis, psychophysics, neural modeling, face perception, macaque face patches},
doi = {},
url = {http://www.mit.edu/~ilkery/papers/yildirimetal_cogsci15.pdf}
}
- Neil Bramley, Tobias Gerstenberg, Joshua B. Tenenbaum:
Natural science: Active learning in dynamic physical microworlds (pdf) (bibtex)
Proceedings of the 38th Annual Conference of the Cognitive Science Society
#causality, #learning, #intuitive physics
@article{NeilBramley:2016:87d9f,
author = {Neil Bramley and Tobias Gerstenberg and Joshua B. Tenenbaum},
journal = {Proceedings of the 38th Annual Conference of the Cognitive Science Society},
title = {Natural science: Active learning in dynamic physical microworlds},
year = {2016},
keywords = {causality, learning, intuitive physics},
doi = {},
url = {http://web.mit.edu/tger/www/papers/Natural%20science%20Active%20learning%20in%20dynamic%20physical%20microworlds,%20Bramley,%20Gerstenberg,%20Tenenbaum,%202016.pdf}
}
- Tobias Gerstenberg, Joshua B. Tenenbaum:
Understanding “almost”: Empirical and computational studies of near misses (pdf) (bibtex)
Proceedings of the 38th Annual Conference of the Cognitive Science Society
#counterfactuals, #causality, #intuitive physics, #language, #almost
@article{TobiasGerstenberg:2016:5328e,
author = {Tobias Gerstenberg and Joshua B. Tenenbaum},
journal = {Proceedings of the 38th Annual Conference of the Cognitive Science Society},
title = {Understanding “almost”: Empirical and computational studies of near misses},
year = {2016},
keywords = {counterfactuals, causality, intuitive physics, language, almost},
doi = {},
url = {http://web.mit.edu/tger/www/papers/Understanding%20almost%20Empirical%20and%20computational%20studies%20of%20near%20misses,%20Gerstenberg,%20Tenenbaum,%202016.pdf}
}
- Michael Chang, Tomer Ullman, Antonio Torralba, Joshua B. Tenenbaum:
A Compositional Object-Based Approach To Learning Physical Dynamics (web) (bibtex)
Proceedings of the 5th International Conference on Learning Representations
#intuitive physics, #simulation, #deep learning, #unsupervised learning, #scene understanding
@article{MichaelChang:2017:07f51,
author = {Michael Chang and Tomer Ullman and Antonio Torralba and Joshua B. Tenenbaum},
journal = {Proceedings of the 5th International Conference on Learning Representations},
title = {A Compositional Object-Based Approach To Learning Physical Dynamics},
year = {2017},
keywords = {intuitive physics, simulation, deep learning, unsupervised learning, scene understanding},
doi = {},
url = {https://arxiv.org/abs/1612.00341}
}
- Tobias Gerstenberg, Joshua B. Tenenbaum:
Intuitive Theories (pdf) (bibtex)
Oxford Handbook of Causal Reasoning
#intuitive theories, #causality, #counterfactuals, #intuitive physics, #intuitive psychology
@article{TobiasGerstenberg:2017:9a988,
author = {Tobias Gerstenberg and Joshua B. Tenenbaum},
journal = {Oxford Handbook of Causal Reasoning},
title = {Intuitive Theories},
year = {2017},
keywords = {intuitive theories, causality, counterfactuals, intuitive physics, intuitive psychology},
doi = {},
url = {http://web.mit.edu/tger/www/papers/Intuitive%20Theories,%20Gerstenberg,%20Tenenbaum,%202017.pdf}
}
- Tobias Gerstenberg, Liang Zhou, Kevin A. Smith, Joshua B. Tenenbaum:
Faulty towers: A hypothetical simulation model of physical support (pdf) (bibtex)
Proceedings of the 39th Annual Conference of the Cognitive Science Society
#causality, #counterfactual, #hypothetical, #mental simulation, #intuitive physics, #physical support
@article{TobiasGerstenberg:2017:d421a,
author = {Tobias Gerstenberg and Liang Zhou and Kevin A. Smith and Joshua B. Tenenbaum},
journal = {Proceedings of the 39th Annual Conference of the Cognitive Science Society},
title = {Faulty towers: A hypothetical simulation model of physical support},
year = {2017},
keywords = {causality, counterfactual, hypothetical, mental simulation, intuitive physics, physical support},
doi = {},
url = {http://web.mit.edu/tger/www/papers/Intuitive%20Theories,%20Gerstenberg,%20Tenenbaum,%202017.pdf}
}
- Ilker Yildirim, Tobias Gerstenberg, Basil Saeed, Marc Toussant, Joshua B. Tenenbaum:
Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints (pdf) (bibtex)
Proceedings of the 39th Annual Conference of the Cognitive Science Society
#planning, #problem solving, #logic-geometric programming, #intuitive physics, #scene understanding
@article{IlkerYildirim:2017:b8461,
author = {Ilker Yildirim and Tobias Gerstenberg and Basil Saeed and Marc Toussant and Joshua B. Tenenbaum},
journal = {Proceedings of the 39th Annual Conference of the Cognitive Science Society},
title = {Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints},
year = {2017},
keywords = {planning, problem solving, logic-geometric programming, intuitive physics, scene understanding},
doi = {},
url = {http://web.mit.edu/tger/www/papers/Physical%20problem%20solving%20Joint%20planning%20with%20symbolic,%20geometric,%20and%20dynamic%20constraints,%20Yildirim%20et%20al.,%202017.pdf}
}
- Renqiao Zhang, Jiajun Wu, Chengkai Zhang, William T. Freeman, Joshua B. Tenenbaum:
A Comparative Evaluation of Approximate Probabilistic Simulation and Deep Neural Networks as Accounts of Human Physical Scene Understanding (web) (bibtex)
Proceedings of the 38th Annual Conference of the Cognitive Science Society
#intuitive physics, #simulation, #deep learning, #scene understanding
@article{RenqiaoZhang:2016:84ee7,
author = {Renqiao Zhang and Jiajun Wu and Chengkai Zhang and William T. Freeman and Joshua B. Tenenbaum},
journal = {Proceedings of the 38th Annual Conference of the Cognitive Science Society},
title = {A Comparative Evaluation of Approximate Probabilistic Simulation and Deep Neural Networks as Accounts of Human Physical Scene Understanding},
year = {2016},
keywords = {intuitive physics, simulation, deep learning, scene understanding},
doi = {},
url = {http://blocks.csail.mit.edu/}
}
- Jiajun Wu, Joseph J. Lim, Hongyi Zhang, Joshua B. Tenenbaum, William T. Freeman:
Physics 101: Learning Physical Object Properties from Unlabeled Videos (web) (bibtex)
British Machine Vision Conference (BMVC)
#intuitive physics, #unsupervised learning, #deep learning, #scene understanding
@article{JiajunWu:2016:9e6c9,
author = {Jiajun Wu and Joseph J. Lim and Hongyi Zhang and Joshua B. Tenenbaum and William T. Freeman},
journal = {British Machine Vision Conference (BMVC)},
title = {Physics 101: Learning Physical Object Properties from Unlabeled Videos},
year = {2016},
keywords = {intuitive physics, unsupervised learning, deep learning, scene understanding},
doi = {},
url = {http://phys101.csail.mit.edu/}
}
- Jiajun Wu, Ilker Yildirim, Joseph J. Lim, William T. Freeman, Joshua B. Tenenbaum:
Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning (web) (bibtex)
Advances in Neural Information Processing Systems (NIPS)
#intuitive physics, #simulation, #deep learning, #unsupervised learning, #scene understanding
@article{JiajunWu:2015:27dc4,
author = {Jiajun Wu and Ilker Yildirim and Joseph J. Lim and William T. Freeman and Joshua B. Tenenbaum},
journal = {Advances in Neural Information Processing Systems (NIPS)},
title = {Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning},
year = {2015},
keywords = {intuitive physics, simulation, deep learning, unsupervised learning, scene understanding},
doi = {},
url = {http://galileo.csail.mit.edu/}
}
- Jiajun Wu, Tianfan Xue, Joseph J. Lim, Yuandong Tian, Joshua B. Tenenbaum, Antonio Torralba, William T. Freeman:
Single Image 3D Interpreter Network (web) (bibtex)
European Conference in Computer Vision (ECCV)
#deep learning, #self-supervised learning, #3d vision
@article{JiajunWu:2016:770f7,
author = {Jiajun Wu and Tianfan Xue and Joseph J. Lim and Yuandong Tian and Joshua B. Tenenbaum and Antonio Torralba and William T. Freeman},
journal = {European Conference in Computer Vision (ECCV)},
title = {Single Image 3D Interpreter Network},
year = {2016},
keywords = {deep learning, self-supervised learning, 3d vision},
doi = {},
url = {http://3dinterpreter.csail.mit.edu/}
}
- Jiajun Wu, Chengkai Zhang, Tianfan Xue, William T. Freeman, Joshua B. Tenenbaum:
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling (web) (bibtex)
Advances in Neural Information Processing Systems (NIPS)
#simulation, #deep learning, #generative adversarial learning, #3d vision
@article{JiajunWu:2016:3471d,
author = {Jiajun Wu and Chengkai Zhang and Tianfan Xue and William T. Freeman and Joshua B. Tenenbaum},
journal = {Advances in Neural Information Processing Systems (NIPS)},
title = {Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling},
year = {2016},
keywords = {simulation, deep learning, generative adversarial learning, 3d vision},
doi = {},
url = {http://3dgan.csail.mit.edu/}
}
- Zhoutong Zhang, Jiajun Wu, Qiujia Li, Zhengjia Huang, James Traer, Josh H. McDermott, Joshua B. Tenenbaum, William T. Freeman:
Generative Modeling of Audible Shapes for Object Perception (pdf) (bibtex)
IEEE International Conference on Computer Vision (ICCV)
#deep learning, #simulation, #auditory perception, #scene understanding
@article{ZhoutongZhang:2017:4f1fd,
author = {Zhoutong Zhang and Jiajun Wu and Qiujia Li and Zhengjia Huang and James Traer and Josh H. McDermott and Joshua B. Tenenbaum and William T. Freeman},
journal = {IEEE International Conference on Computer Vision (ICCV)},
title = {Generative Modeling of Audible Shapes for Object Perception},
year = {2017},
keywords = {deep learning, simulation, auditory perception, scene understanding},
doi = {},
url = {https://jiajunwu.com/papers/gensound_iccv.pdf}
}
- Jiajun Wu, Joshua B. Tenenbaum, Pushmeet Kohli:
Neural Scene De-rendering (web) (bibtex)
IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
#deep learning, #self-supervised learning, #inverse graphics, #computer vision, #scene understanding
@article{JiajunWu:2017:2afa9,
author = {Jiajun Wu and Joshua B. Tenenbaum and Pushmeet Kohli},
journal = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
title = {Neural Scene De-rendering},
year = {2017},
keywords = {deep learning, self-supervised learning, inverse graphics, computer vision, scene understanding},
doi = {},
url = {http://nsd.csail.mit.edu/}
}
- Jiajun Wu, Erika Lu, Pushmeet Kohli, William T. Freeman, Joshua B. Tenenbaum:
Learning to See Physics via Visual De-animation (web) (bibtex)
Advances in Neural Information Processing Systems (NIPS)
#intuitive physics, #simulation, #deep learning, #scene understanding
@article{JiajunWu:2017:4849f,
author = {Jiajun Wu and Erika Lu and Pushmeet Kohli and William T. Freeman and Joshua B. Tenenbaum},
journal = {Advances in Neural Information Processing Systems (NIPS)},
title = {Learning to See Physics via Visual De-animation},
year = {2017},
keywords = {intuitive physics, simulation, deep learning, scene understanding},
doi = {},
url = {http://vda.csail.mit.edu/}
}
- Jiajun Wu, Yifan Wang, Tianfan Xue, Xingyuan Sun, William T. Freeman, Joshua B. Tenenbaum:
MarrNet: 3D Shape Reconstruction via 2.5D Sketches (web) (bibtex)
Advances in Neural Information Processing Systems (NIPS)
#3d vision, #deep learning
@article{JiajunWu:2017:cfe2b,
author = {Jiajun Wu and Yifan Wang and Tianfan Xue and Xingyuan Sun and William T. Freeman and Joshua B. Tenenbaum},
journal = {Advances in Neural Information Processing Systems (NIPS)},
title = {MarrNet: 3D Shape Reconstruction via 2.5D Sketches},
year = {2017},
keywords = {3d vision, deep learning},
doi = {},
url = {http://marrnet.csail.mit.edu/}
}
- Michael Janner, Jiajun Wu, Tejas D. Kulkarni, Ilker Yildirim, Joshua B. Tenenbaum:
Self-Supervised Intrinsic Image Decomposition (web) (bibtex)
Advances in Neural Information Processing Systems (NIPS)
#computer vision, #deep learning, #self-supervised learning
@article{MichaelJanner:2017:e02af,
author = {Michael Janner and Jiajun Wu and Tejas D. Kulkarni and Ilker Yildirim and Joshua B. Tenenbaum},
journal = {Advances in Neural Information Processing Systems (NIPS)},
title = {Self-Supervised Intrinsic Image Decomposition},
year = {2017},
keywords = {computer vision, deep learning, self-supervised learning},
doi = {},
url = {http://rin.csail.mit.edu/}
}
- Zhoutong Zhang, Qiujia Li, Zhengjia Huang, Jiajun Wu, Joshua B. Tenenbaum, William T. Freeman:
Shape and Material from Sound (web) (bibtex)
Advances in Neural Information Processing Systems (NIPS)
#auditory perception, #deep learning, #simulation
@article{ZhoutongZhang:2017:1d3c7,
author = {Zhoutong Zhang and Qiujia Li and Zhengjia Huang and Jiajun Wu and Joshua B. Tenenbaum and William T. Freeman},
journal = {Advances in Neural Information Processing Systems (NIPS)},
title = {Shape and Material from Sound},
year = {2017},
keywords = {auditory perception, deep learning, simulation},
doi = {},
url = {http://sound.csail.mit.edu/}
}
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