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Found 906 results
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Ullman, S. et al. Image interpretation by iterative bottom-up top- down processing. (2021).PDF icon CBMM-Memo-120.pdf (2.83 MB)
Ullman, S., Assif, L., Fetaya, E. & Harari, D. Atoms of recognition in human and computer vision. PNAS 113, 2744–2749 (2016).PDF icon mirc_author_manuscript_with_figures_and_SI-2.pdf (1.65 MB)
Ullman, S., Dorfman, N. & Harari, D. A model for discovering ‘containment’ relations. Cognition 183, 67 - 81 (2019).
Ullman, T. D. & Tenenbaum, J. B. Bayesian Models of Conceptual Development: Learning as Building Models of the World. Annual Review of Developmental Psychology 2, 533 - 558 (2020).
Ullman, T. D., Stuhlmüller, A., Goodman, N. D. & Tenenbaum, J. B. Learning physical parameters from dynamic scenes. Cognitive Psychology 104, 57-82 (2018).PDF icon T-Ullman-etal_CogPsych_LearningPhysicalParametersFromDynamicScenes.pdf (3.15 MB)
Udrescu, S. - M. et al. AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity. Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020) (2020).PDF icon 2006.10782.pdf (2.62 MB)
T
Tuckute, G., Feather, J., Boebinger, D. & McDermott, J. H. Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions. PLOS Biology 21, e3002366 (2023).
Tsividis, P., Gershman, S. J., Tenenbaum, J. B. & Schulz, L. Information Selection in Noisy Environments with Large Action Spaces. 9th Biennial Conference of the Cognitive Development Society Columbus, OH, (2015).
Tsividis, P., Tenenbaum, J. B. & Schulz, L. Hypothesis-Space Constraints in Causal Learning. Annual Meeting of the Cognitive Science Society (CogSci) (2015). at <https://mindmodeling.org/cogsci2015/papers/0418/index.html>PDF icon hypothesis_space_constraints (1).pdf (1.54 MB)
Tsividis, P., Pouncy, T., Xu, J. L., Tenenbaum, J. B. & Gershman, S. J. Human Learning in Atari. AAAI Spring Symposium Series (2017).PDF icon Tsividis et al - Human Learning in Atari.pdf (844.47 KB)
Traer, J. & McDermott, J. H. A library of real-world reverberation and a toolbox for its analysis and measurement. Annual Meeting of Acoustical Society of America (2017).
Traer, J. & McDermott, J. H. Human recognition of environmental sounds is not always robust to reverberation. Annual Meeting of the Acoustical Society 143, (2018).
Traer, J. & McDermott, J. H. Auditory Perception of Material and Force from Impact Sounds. Annual Meeting of Association for Research in Otolaryngology (2017).
Traer, J. & McDermott, J. H. Human inference of force from impact sounds: Perceptual evidence for inverse physics. Annual Meeting of the Acoustical Society 143, (2018).
Traer, J. & McDermott, J. H. Environmental statistics enable perceptual separation of sound and space. Speech and Audio in the Northeast (2016).
Traer, J., Norman-Haignere, S. & McDermott, J. H. Causal inference in environmental sound recognition. Cognition (2021). doi:10.1016/j.cognition.2021.104627
Traer, J., Cusimano, M. & McDermott, J. H. A perceptually inspired generative model of rigid-body contact sounds. Proceedings of the 22nd International Conference on Digital Audio Effects (DAFx-19) (2019).
Traer, J. & McDermott, J. H. Investigating audition with a generative model of impact sounds. Annual Meeting of Acoustical Society of America (2017).
Traer, J. & McDermott, J. H. Statistics of natural reverberation enable perceptual separation of sound and space. Proceedings of the National Academy of Sciences 113, E7856 - E7865 (2016).
Toussaint, M., Allen, K., Smith, K. A. & Tenenbaum, J. B. Differentiable physics and stable modes for tool-use and manipulation planning. Robotics: Science and Systems 2018 (2018).PDF icon ToussaintEtAl_DiffPhysStable.pdf (1.97 MB)
Tomova, L. et al. Acute social isolation evokes midbrain craving responses similar to hunger. Nature Neuroscience 23, 1597 - 1605 (2020).PDF icon s41593-020-00742-z.pdf (5.47 MB)
Tomov, M. S., Schulz, E. & Gershman, S. J. Multi-task reinforcement learning in humans. Nature Human Behaviour (2021). doi:10.1038/s41562-020-01035-y
Tomov, M. S., Tsividis, P. A., Pouncy, T., Tenenbaum, J. B. & Gershman, S. J. The neural architecture of theory-based reinforcement learning. Neuron 111, 1331 - 1344.e8 (2023).
Tiwary, K. et al. What if Eye..? Computationally Recreating Vision Evolution. arXiv (2025). at <https://arxiv.org/abs/2501.15001>PDF icon 2501.15001v1.pdf (5.2 MB)
Tian, L., Ellis, K., Kryven, M. & Tenenbaum, J. B. Learning abstract structure for drawing by efficient motor program induction. Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020) (2020). at <https://papers.nips.cc/paper/2020/hash/1c104b9c0accfca52ef21728eaf01453-Abstract.html>

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