Publication

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2017
Kanwisher, N. The Quest for the FFA and Where It Led. The Journal of Neuroscience 37, 1056 - 1061 (2017).
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>
Gershman, S. J. & Daw, N. D. Reinforcement learning and episodic memory in humans and animals: an integrative framework. Annual Review of Psychology 68, (2017).PDF icon GershmanDaw17.pdf (422.11 KB)
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>
Cheney, N., Schrimpf, M. & Kreiman, G. On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations. (2017).PDF icon CBMM-Memo-065.pdf (687.76 KB)
N. Murty, A. Ratan & Arun, S. P. Seeing a straight line on a curved surface: decoupling of patterns from surfaces by single IT neurons. Journal of Neurophysiology 11773, 104 - 116 (2017).
Arcaro, M. J., Schade, P. F., Vincent, J. L., Ponce, C. R. & Livingstone, M. S. Seeing faces is necessary for face-domain formation. Nature Neuroscience 5631628, (2017).
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>
Liu, S. & Spelke, E. S. Six-month-old infants expect agents to minimize the cost of their actions. Cognition 160, 35-42 (2017).
Golowich, N., Rakhlin, A. & Shamir, O. Size-Independent Sample Complexity of Neural Networks. (2017).PDF icon 1712.06541.pdf (278.77 KB)
Dillon, M. R. & Spelke, E. S. Spatial cognition across development. SRCD (2017).
Leavitt, M. L., Mendoza-Halliday, D. & J.C., M. - T. Sustained Activity Encoding Working Memories: Not Fully Distributed. Trends in Neurosciences 40 , 328-346 (2017).
Anselmi, F., Evangelopoulos, G., Rosasco, L. & Poggio, T. Symmetry Regularization. (2017).PDF icon CBMM-Memo-063.pdf (6.1 MB)
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)
Paul, R., Barbu, A., Felshin, S., Katz, B. & Roy, N. Temporal Grounding Graphs for Language Understanding with Accrued Visual-Linguistic Context. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI 2017) (2017). at <c>
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. SRCD (2017).
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).
Varela, C. & Wilson, M. A. Thalamic contribution to CA1-mPFC interactions during sleep. Society for Neuroscience's Annual Meeting - SfN 2017 (2017).File AbstractSFNfinal.docx (13.14 KB)
Dehghani, N. Theoretical principles of multiscale spatiotemporal control of neuronal networks: a complex systems perspective. (2017). doi:10.1101/097618PDF icon StimComplexity.pdf (218.1 KB)
Poggio, T. & Liao, Q. Theory II: Landscape of the Empirical Risk in Deep Learning. (2017).PDF icon CBMM Memo 066_1703.09833v2.pdf (5.56 MB)
Zhang, C. et al. Theory of Deep Learning IIb: Optimization Properties of SGD. (2017).PDF icon CBMM-Memo-072.pdf (3.66 MB)
Poggio, T. et al. Theory of Deep Learning III: explaining the non-overfitting puzzle. (2017).PDF icon CBMM-Memo-073.pdf (2.65 MB)PDF icon CBMM Memo 073 v2 (revised 1/15/2018) (2.81 MB)PDF icon CBMM Memo 073 v3 (revised 1/30/2018) (2.72 MB)PDF icon CBMM Memo 073 v4 (revised 12/30/2018) (575.72 KB)

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