All Publications

2017

R. Magid, Yan, P., Siegel, M., Tenenbaum, J. B., and Schulz, L., “Changing minds: Children’s inferences about third party belief revision”, Developmental Science, p. e12553, 2017.PDF icon Changing Minds_MagidYanSiegelTenenbaumSchulz_in press.pdf (915.8 KB)
CBMM Funded
J. Sliwa and Freiwald, W. A., “A Dedicated Network for Social Interaction Processing in the Primate Brain”, Science, vol. Vol. 356, no. Issue 6339, pp. pp. 745-749, 2017.
CBMM Funded
P. Henry Winston and Holmes, D., “Character-building stories”, in Advances in Cognitive Systems, Troy, NY, 2017.
CBMM Related
CBMM Memo No.
067
C. Zhang, Liao, Q., Rakhlin, A., Sridharan, K., Miranda, B., Golowich, N., and Poggio, T., “Musings on Deep Learning: Properties of SGD”. 2017.PDF icon CBMM Memo 067 v2 (revised 7/19/2017) (5.88 MB)PDF icon CBMM Memo 067 v3 (revised 9/15/2017) (5.89 MB)PDF icon CBMM Memo 067 v4 (revised 12/26/2017) (5.57 MB)
CBMM Funded
S. Sadagopan, Zarco, W., and Freiwald, W. A., “A Causal Relationship Between Face-Patch Activity and Face-Detection Behavior”, eLife, 2017.PDF icon elife-18558-v1.pdf (813.71 KB)
CBMM Funded
CBMM Memo No.
066
T. Poggio and Liao, Q., “Theory II: Landscape of the Empirical Risk in Deep Learning”. 2017.PDF icon CBMM Memo 066_1703.09833v2.pdf (5.56 MB)
CBMM Funded
CBMM Memo No.
065
N. Cheney, Schrimpf, M., and 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)
CBMM Related
CBMM Memo No.
064
W. Lotter, Kreiman, G., and Cox, D., “Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning”. 2017.PDF icon CBMM-Memo-064.pdf (3 MB)
CBMM Funded
CBMM Memo No.
062
A. Tacchetti, Voinea, S., and Evangelopoulos, G., “Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets”. 2017.PDF icon CBMM-Memo-062.pdf (9.37 MB)
CBMM Funded
C. Baker, Jara-Ettinger, J., Saxe, R., and Tenenbaum, J. B., “Rational quantitative attribution of beliefs, desires, and percepts in human mentalizing”, Nature Human Behavior, vol. 1, no. 0064, 2017.PDF icon article.pdf (2.17 MB)
CBMM Funded
G. Ben-Yosef, Yachin, A., and Ullman, S., “A model for interpreting social interactions in local image regions”, in AAAI Spring Symposium Series, Science of Intelligence, Palo Alto, CA, 2017.PDF icon 2017-Ben-Yosef_Yachin_Ullman-A_model_for_interpreting_social_interactions_in_local_image_regions.pdf (1.53 MB)
CBMM Funded
S. Liu and Spelke, E. S., “Six-month-old infants expect agents to minimize the cost of their actions”, Cognition, vol. 160, pp. 35-42, 2017.
CBMM Funded
T. Poggio, Mhaskar, H., Rosasco, L., Miranda, B., and Liao, Q., “Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review”, International Journal of Automation and Computing, pp. 1-17, 2017.PDF icon art%3A10.1007%2Fs11633-017-1054-2.pdf (1.68 MB)
CBMM Funded
K. M. Scott and Schulz, L., “Lookit (Part 1): a new online platform for developmental research”, Open Mind, vol. 1, no. 1, 2017.PDF icon UNCORRECTED PROOF (561.21 KB)
CBMM Funded
K. M. Scott, Chu, J., and Schulz, L., “Lookit (Part 2): Assessing the viability of online developmental research, Results from three case studies”, Open Mind, vol. 1, no. 1, 2017.PDF icon lookitpart2.pdf (464.02 KB)
CBMM Funded
Y. Wu and Schulz, L., “Inferring Beliefs and Desires From Emotional Reactions to Anticipated and Observed Events”, Child Development, 2017.PDF icon Wu_et_al-2017-Child_Development.pdf (883.1 KB)
CBMM Funded
CBMM Memo No.
061
G. Ben-Yosef, Assif, L., and Ullman, S., “Full interpretation of minimal images”. 2017.PDF icon CBMM Memo 061 v.1 (4.64 MB)PDF icon CBMM Memo 061 v.2 (5.41 MB)
CBMM Funded
N. Kanwisher, “The Quest for the FFA and Where It Led”, The Journal of Neuroscience, vol. 37, no. 5, pp. 1056 - 1061, 2017.
CBMM Funded
A. C. Spokes and Spelke, E. S., “The cradle of social knowledge: Infants' reasoning about caregiving and affiliation”, Cognition, vol. 159, pp. 102-116, 2017.
CBMM Funded
CBMM Memo No.
060
W. Mlynarski and McDermott, J. H., “Learning Mid-Level Auditory Codes from Natural Sound Statistics”. 2017.PDF icon MlynarskiMcDermott_Memo060.pdf (7.11 MB)
CBMM Funded
V. Chandrasekhar, Lin, J., Liao, Q., Morère, O., Veillard, A., Duan, L., and Poggio, T., “Compression of Deep Neural Networks for Image Instance Retrieval”, 2017.PDF icon 1701.04923.pdf (614.33 KB)
CBMM Related
B. Deen, Richardson, H., Dilks, D. D., Takahashi, A., Keil, B., Wald, L., Kanwisher, N., and Saxe, R., “Organization of high-level visual cortex in human infants”, Nature Communications, 2017.
CBMM Funded
J. Z. Leibo, Liao, Q., Anselmi, F., Freiwald, W. A., and Poggio, T., “View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation”, Current Biology, vol. 27, pp. 1-6, 2017.
CBMM Funded
M. Vaziri-Pashkam, Cormiea, S., and Nakayama, K., “Predicting actions from subtle preparatory movements”, Cognition, vol. 168, pp. 65 - 75, 2017.
CBMM Funded
Y. Berzak, Nakamura, C., Flynn, S., and Katz, B., “Predicting Native Language from Gaze”, in Annual Meeting of the Association for Computational Linguistics (ACL 2017), 2017.
CBMM Funded

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