Publications

    Unsupervised Learning

  • C. Kemp, J. B. Tenenbaum:
    The discovery of structural form (pdf) (bibtex)
    Proceedings of the National Academy of Sciences
    #cognitive development, #structure discovery, #unsupervised learning
    @article{C.Kemp:2008:eeacc,
    author = {C. Kemp and J. B. Tenenbaum},
    journal = {Proceedings of the National Academy of Sciences},
    title = {The discovery of structural form},
    year = {2008},
    keywords = {cognitive development, structure discovery, unsupervised learning},
    doi = {},
    url = {http://www.psy.cmu.edu/~ckemp/papers/kempt08.pdf}
    }
  • B. M. Lake, J. B. Tenenbaum:
    Discovering structure by learning sparse graphs (pdf) (bibtex)
    Proceedings of the 32nd Cognitive Science Conference
    #structure discovery, #semantic cognition, #unsupervised learning
    @article{B.M.Lake:2010:9f55c,
    author = {B. M. Lake and J. B. Tenenbaum},
    journal = {Proceedings of the 32nd Cognitive Science Conference},
    title = {Discovering structure by learning sparse graphs},
    year = {2010},
    keywords = {structure discovery, semantic cognition, unsupervised learning},
    doi = {},
    url = {http://web.mit.edu/brenden/www/LakeTenenbaum09CogSci.pdf}
    }
  • R. B. Grosse, R. Salakhutdinov, W. T. Freeman, J. B. Tenenbaum:
    Exploiting compositionality to explore a large space of model structures (pdf) (bibtex)
    Proceedings of the 28th Conference on Uncertainty in AI (UAI)
    #structure discovery, #unsupervised learning, #bayesian model
    @article{R.B.Grosse:2012:f3693,
    author = {R. B. Grosse and R. Salakhutdinov and W. T. Freeman and J. B. Tenenbaum},
    journal = {Proceedings of the 28th Conference on Uncertainty in AI (UAI)},
    title = {Exploiting compositionality to explore a large space of model structures},
    year = {2012},
    keywords = {structure discovery, unsupervised learning, bayesian model},
    doi = {},
    url = {http://people.csail.mit.edu/rgrosse/uai2012-matrix.pdf}
    }
  • D. Duvenaud, J. R. Lloyd, R. B. Grosse, J. B. Tenenbaum, Z. Ghahramani:
    Structure discovery in nonparametric regression through compositional kernel search (pdf) (bibtex)
    International Conference on Machine Learning
    #structure discovery, #unsupervised learning, #bayesian model
    @article{D.Duvenaud:2013:29ae2,
    author = {D. Duvenaud and J. R. Lloyd and R. B. Grosse and J. B. Tenenbaum and Z. Ghahramani},
    journal = {International Conference on Machine Learning},
    title = {Structure discovery in nonparametric regression through compositional kernel search},
    year = {2013},
    keywords = {structure discovery, unsupervised learning, bayesian model},
    doi = {},
    url = {http://people.csail.mit.edu/rgrosse/icml2013-gp.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}
    }
  • 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/}
    }

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