S.J. Gershman
- S.J. Gershman, E. Vul, J.B. Tenenbaum:
Multistability and Perceptual Inference (pdf) (bibtex)
Neural Computation
#vision, #mcmc
@article{S.J.Gershman:2012:21525,
author = {S.J. Gershman and E. Vul and J.B. Tenenbaum},
journal = {Neural Computation},
title = {Multistability and Perceptual Inference},
year = {2012},
keywords = {vision, mcmc},
doi = {},
url = {http://www.princeton.edu/~sjgershm/GershmanVulTenenbaum12.pdf}
}
- S.J. Gershman, F.J. Jaekel, J.B. Tenenbaum:
Bayesian vector analysis and the perception of hierarchical motion (pdf) (bibtex)
Proceedings of the Thirty-Fifth Annual Conference of the Cognitive Science Society
#vision, #motion perception
@article{S.J.Gershman:2013:8a458,
author = {S.J. Gershman and F.J. Jaekel and J.B. Tenenbaum},
journal = {Proceedings of the Thirty-Fifth Annual Conference of the Cognitive Science Society},
title = {Bayesian vector analysis and the perception of hierarchical motion},
year = {2013},
keywords = {vision, motion perception},
doi = {},
url = {http://web.mit.edu/sjgershm/www/GershmanJaekelTenenbaum13.pdf}
}
- D. Wingate, C. Diuk, T. O’Donnell, J.B. Tenenbaum, S.J. Gershman:
Compositional policy priors (pdf) (bibtex)
MIT CSAIL Technical Report 2013-007
#reinforcement learning, #grammar induction
@article{D.Wingate:2013:8a8cd,
author = {D. Wingate and C. Diuk and T. O’Donnell and J.B. Tenenbaum and S.J. Gershman},
journal = {MIT CSAIL Technical Report 2013-007},
title = {Compositional policy priors},
year = {2013},
keywords = {reinforcement learning, grammar induction},
doi = {},
url = {http://web.mit.edu/sjgershm/www/MIT-CSAIL-TR-2013-007.pdf}
}
- S.J. Gershman, J.B. Tenenbaum:
Phrase similarity in humans and machines (pdf) (bibtex)
Proceedings of the 37th Annual Conference of the Cognitive Science Society.
#similarity; semantics; neural networks
@article{S.J.Gershman:2015:37e02,
author = {S.J. Gershman and J.B. Tenenbaum},
journal = {Proceedings of the 37th Annual Conference of the Cognitive Science Society.},
title = {Phrase similarity in humans and machines},
year = {2015},
keywords = {similarity; semantics; neural networks},
doi = {},
url = {http://web.mit.edu/sjgershm/www/GershmanTenenbaum15.pdf}
}
- S.J. Gershman, J.B. Tenenbaum, F. Jaekel:
Discovering hierarchical motion structure (pdf) (bibtex)
Vision Research
#motion perception; structure learning
@article{S.J.Gershman:2015:dcd05,
author = {S.J. Gershman and J.B. Tenenbaum and F. Jaekel},
journal = {Vision Research},
title = {Discovering hierarchical motion structure},
year = {2015},
keywords = {motion perception; structure learning},
doi = {},
url = {http://web.mit.edu/sjgershm/www/GershmanTenenbaumJakel15.pdf}
}
- Austerweil, J.L., S.J. Gershman, J.B. Tenenbaum, T.L. Griffiths:
Structure and flexibility in Bayesian models of cognition (pdf) (bibtex)
Oxford Handbook of Computational and Mathematical Psychology
#bayesian nonparametrics; structure learning
@article{Austerweil:2015:5842a,
author = {Austerweil and J.L. and S.J. Gershman and J.B. Tenenbaum and T.L. Griffiths},
journal = {Oxford Handbook of Computational and Mathematical Psychology},
title = {Structure and flexibility in Bayesian models of cognition},
year = {2015},
keywords = {bayesian nonparametrics; structure learning},
doi = {},
url = {http://web.mit.edu/sjgershm/www/Austerweil15.pdf}
}
- S.J. Gershman, E.J. Horvitz, J.B. Tenenbaum:
Computational rationality: a converging paradigm for intelligence in brains, minds, and machines (pdf) (bibtex)
Science
#decision making, #reinforcement learning, #hypothesis sampling
@article{S.J.Gershman:2015:79191,
author = {S.J. Gershman and E.J. Horvitz and J.B. Tenenbaum},
journal = {Science},
title = {Computational rationality: a converging paradigm for intelligence in brains, minds, and machines},
year = {2015},
keywords = {decision making, reinforcement learning, hypothesis sampling},
doi = {},
url = {http://web.mit.edu/sjgershm/www/GershmanHorvitzTenenbaum15.pdf}
}
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