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2016
Lafer-Sousa, R., Conway, B. R. & Kanwisher, N. Color-Biased Regions of the Ventral Visual Pathway Lie between Face- and Place-Selective Regions in Humans, as in Macaques. Journal of Neuroscience 36, 1682 - 1697 (2016).
Liu, S. & Spelke, E. S. Continuous representations of action efficiency in infancy. CEU Conference on Cognitive Development (BCCCD16) (2016).
Berzak, Y., Reichart, R. & Katz, B. Contrastive Analysis with Predictive Power: Typology Driven Estimation of Grammatical Error Distributions in ESL. (2016).PDF icon memo-50.pdf (493.74 KB)
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)
Spelke, E. S., Barner, D. & Baron, A. S. Core Knowledge and Conceptual Change (Oxford University Press, 2016).
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)
Kliemann, D., Jacoby, N., Anzellottti, S. & Saxe, R. Decoding task and stimulus representations in face-responsive cortex. Cognitive Neuropsychology (2016).
Poggio, T. Deep Leaning: Mathematics and Neuroscience. A Sponsored Supplement to Science Brain-Inspired intelligent robotics: The intersection of robotics and neuroscience, 9-12 (2016).
Poggio, T. Deep Learning: mathematics and neuroscience. (2016).PDF icon Deep Learning- mathematics and neuroscience.pdf (1.25 MB)
Mhaskar, H. & Poggio, T. Deep vs. shallow networks: An approximation theory perspective. Analysis and Applications 14, 829 - 848 (2016).
Mhaskar, H. & Poggio, T. Deep vs. shallow networks : An approximation theory perspective. (2016).PDF icon Original submission, visit the link above for the updated version (960.27 KB)
Berzak, Y., Barbu, A., Harari, D., Katz, B. & Ullman, S. Do You See What I Mean? Visual Resolution of Linguistic Ambiguities. (2016).PDF icon memo-51.pdf (2.74 MB)
Wang, P. & Yuille, A. DOC: Deep OCclusion Recovering From A Single Image. ECCV (2016).
Dehghani, N. Dynamic balance of excitation and inhibition in human and monkey neocortex. Nature Scientific Reports (2016). doi:10.1038/srep23176PDF icon BalanceExcitationInhibition.pdf (2.1 MB)
Spokes, A. C. & Spelke, E. S. Early Reasoning about Affiliation and Social Networks. International Conference on Infant Studies (ICIS) (2016).
Ullman, T., Tenenbaum, J. B. & Spelke, E. S. Effort as a bridging concept across action and action understanding: Weight and Physical Effort in Predictions of Efficiency in Other Agents. International Conference on Infant Studies (ICIS) (2016).
Traer, J. & McDermott, J. H. Environmental statistics enable perceptual separation of sound and space. Speech and Audio in the Northeast (2016).
Isik, L., Tacchetti, A. & Poggio, T. Fast, invariant representation for human action in the visual system. (2016). at <http://arxiv.org/abs/1601.01358>PDF icon CBMM Memo 042 (3.03 MB)
Luo, Y., Boix, X., Roig, G., Poggio, T. & Zhao, Q. Foveation-based Mechanisms Alleviate Adversarial Examples. (2016).PDF icon cbmm_memo_044.pdf (11.48 MB)
Sliwa, J. & Freiwald, W. A. From agents to actions to interactions: Uncovering multiple social networks in the primate brain. Society for Neuroscience (2016).PDF icon SFN2016.pdf (555.77 KB)
Fischer, J., Mikhael, J. G., Tenenbaum, J. B. & Kanwisher, N. Functional neuroanatomy of intuitive physical inference. Proceedings of the National Academy of Sciences 113, E5072 - E5081 (2016).
Functional neuroanatomy of intuitive physical inference. (2016).
Spokes, A. C. & Spelke, E. S. The Functions of Infants’ Social Categorization: Early Reasoning about Affiliation and Social Networks. International Conference on Infant Studies (ICIS) (2016).
Mao, J. et al. Generation and Comprehension of Unambiguous Object Descriptions. The Conference on Computer Vision and Pattern Recognition (CVPR) (2016). at <https://github.com/ mjhucla/Google_Refexp_toolbox>PDF icon object_description_cbmm.pdf (2.21 MB)
Morère, O. et al. Group Invariant Deep Representations for Image Instance Retrieval. (2016).PDF icon CBMM-Memo-043.pdf (2.66 MB)

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