Publication

Found 247 results
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2017
Telenczuk, B. et al. Local field potentials primarily reflect inhibitory neuron activity in human and monkey cortex. Nature Scientific Reports (2017). doi:10.1038/srep40211PDF icon srep40211.pdf (2.53 MB)
Wu, J. et al. MarrNet: 3D Shape Reconstruction via 2.5D Sketches. Advances in Neural Information Processing Systems 30 540–550 (2017). at <http://papers.nips.cc/paper/6657-marrnet-3d-shape-reconstruction-via-25d-sketches.pdf>PDF icon MarrNet: 3D Shape Reconstruction via 2.5D Sketches (6.25 MB)
Ullman, T. D., Spelke, E. S., Battaglia, P. & Tenenbaum, J. B. Mind Games: Game Engines as an Architecture for Intuitive Physics. Trends in Cognitive Science 21, 649 - 665 (2017).PDF icon Preprint submitted to Trends in Cognitive Science (17.64 MB)
Theurel, D. Modeling brain dynamics using mathematics from quantum mechanics. Peter Chin's Lab, Boston University Boston University, (2017).
Grossman, N. et al. Noninvasive Deep Brain Stimulation via Temporally Interfering Electric Fields. Cell 169, 1029 - 1041.e16 (2017).
Deen, B. et al. Organization of high-level visual cortex in human infants. Nature Communications (2017). doi:10.1038/ncomms13995
Yildirim, I., Gerstenberg, T., Saeed, B., Toussant, M. & Tenenbaum, J. B. Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints. Proceedings of the 39th Annual Conference of the Cognitive Science Society (2017).PDF icon Physical problem solving Joint planning with symbolic, geometric, and dynamic constraints, Yildirim et al., 2017.pdf (2.46 MB)
Yildirim, I., Gerstenberg, T., Saeed, B., Toussant, M. & Tenenbaum, J. B. Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints. Proceedings of the 39th Annual Conference of the Cognitive Science Society (2017).PDF icon Physical problem solving Joint planning with symbolic, geometric, and dynamic constraints, Yildirim et al., 2017.pdf (2.46 MB)
Baker, C., Jara-Ettinger, J., Saxe, R. & Tenenbaum, J. B. Rational quantitative attribution of beliefs, desires, and percepts in human mentalizing. Nature Human Behavior 1, (2017).PDF icon article.pdf (2.17 MB)
Tang, H., Kreiman, G. & Zhao, Q. Computational and Cognitive Neuroscience of Vision (Springer Singapore, 2017). at <http://www.springer.com/us/book/9789811002113>
Tacchetti, A., Voinea, S., Evangelopoulos, G. & Poggio, T. Representation Learning from Orbit Sets for One-shot Classification. AAAI Spring Symposium Series, Science of Intelligence (2017). at <https://www.aaai.org/ocs/index.php/SSS/SSS17/paper/view/15357>
Janner, M., Wu, J., Kulkarni, T., Yildirim, I. & Tenenbaum, J. B. Self-supervised intrinsic image decomposition. Annual Conference on Neural Information Processing Systems (NIPS) (2017). at <https://papers.nips.cc/paper/7175-self-supervised-intrinsic-image-decomposition>PDF icon intrinsicImg_nips_2017.pdf (5.87 MB)
zhang, zhoutong et al. Shape and Material from Sound. Advances in Neural Information Processing Systems 30 1278–1288 (2017). at <http://papers.nips.cc/paper/6727-shape-and-material-from-sound.pdf>
Soltani, A. Arsalan, Huang, H., Wu, J., Kulkarni, T. & Tenenbaum, J. B. Synthesizing 3D Shapes via Modeling Multi-view Depth Maps and Silhouettes with Deep Generative Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017). doi:10.1109/CVPR.2017.269PDF icon Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes with Deep Generative Networks.pdf (2.86 MB)
Liu, S., Ullman, T. D., Tenenbaum, J. B. & Spelke, E. S. Ten-month-old infants infer the value of goals from the costs of actions. Science 358, 1038-1041 (2017).PDF icon ivc_full_preprint_withsm.pdf (1.6 MB)
Liu, S., Ullman, T., Tenenbaum, J. B. & Spelke, E. S. Ten-month-old infants infer value from effort. Society for Research in Child Development (2017).
Liu, S., Ullman, T., Tenenbaum, J. B. & Spelke, E. S. Ten-month-old infants infer value from effort. SRCD (2017).
Jing, L. et al. Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNN. 34th International Conference on Machine Learning 70, 1733-1741 (2017).PDF icon 1612.05231.pdf (2.3 MB)
Lin, H. & Tegmark, M. Why does deep and cheap learning work so well?. Journal of Statistical Physics 168, 1223–1247 (2017).PDF icon 1608.08225.pdf (2.14 MB)
2016
Lake, B. M., Ullman, T., Tenenbaum, J. B. & Gershman, S. J. Building machines that learn and think like people. (2016).PDF icon machines_that_think.pdf (3.45 MB)
Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex [code]. (2016). at <http://klab.tch.harvard.edu/resources/tangetal_stroop_2016.html>
Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex [dataset]. (2016). at <http://klab.tch.harvard.edu/resources/tangetal_stroop_2016.html>
Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex. eLIFE (2016). doi:10.7554/eLife.12352PDF icon Manuscript  (1.83 MB)
Kleiman-Weiner, M., Ho, M. K., Austerweil, J. L., L, L. Michael & Tenenbaum, J. B. Coordinate to cooperate or compete: abstract goals and joint intentions in social interaction. Proceedings of the 38th Annual Conference of the Cognitive Science Society (2016).PDF icon kleiman2016coordinate.pdf (266.87 KB)
Lin, H. & Tegmark, M. Critical Behavior from Deep Dynamics: A Hidden Dimension in Natural Language. arXiv.org (2016).PDF icon Critical Behavior from Deep Dynamics: A Hidden Dimension in Natural Language (1.64 MB)

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