All Publications

2020

T. Poggio, Banburski, A., and Liao, Q., “Theoretical issues in deep networks”, Proceedings of the National Academy of Sciences, p. 201907369, 2020.PDF icon PNASlast.pdf (915.3 KB)
CBMM Funded
L. Isik, Mynick, A., Pantazis, D., and Kanwisher, N., “The speed of human social interaction perception”, NeuroImage, p. 116844, 2020.
CBMM Funded
H. Richardson, Gweon, H., Dodell-Feder, D., Malloy, C., Pelton, H., Keil, B., Kanwisher, N., and Saxe, R., “Response patterns in the developing social brain are organized by social and emotion features and disrupted in children diagnosed with autism spectrum disorder”, Cortex, vol. 125, pp. 12 - 29, 2020.
CBMM Funded
S. Levine, Kleiman-Weiner, M., Schulz, L., Tenenbaum, J., and Cushman, F., “The logic of universalization guides moral judgment”, Proceedings of the National Academy of Sciences (PNAS), p. 202014505, 2020.
CBMM Funded
K. Kar and DiCarlo, J. J., “Fast Recurrent Processing via Ventrolateral Prefrontal Cortex Is Needed by the Primate Ventral Stream for Robust Core Visual Object Recognition”, Neuron, 2020.PDF icon PIIS0896627320307595.pdf (3.92 MB)
CBMM Funded
K. Vinken, Boix, X., and Kreiman, G., “Incorporating intrinsic suppression in deep neural networks captures dynamics of adaptation in neurophysiology and perception”, Science Advances, vol. 6, no. 42, p. eabd4205, 2020.PDF icon gk7967.pdf (3.07 MB)
CBMM Funded
K. Dobs, Kell, A. J. E., Martinez, J., Cohen, M., and Kanwisher, N., “Why Are Face and Object Processing Segregated in the Human Brain? Testing Computational Hypotheses with Deep Convolutional Neural Networks ”, in Conference on Cognitive Computational Neuroscience, Berlin, Germany, 2020.
CBMM Funded
CBMM Memo No.
112
T. A. Poggio and Liao, Q., “Implicit dynamic regularization in deep networks”. 2020.PDF icon TPR_ver2.pdf (2.29 MB)PDF icon Substantial edits (1.52 MB)PDF icon Edits that are extensive but minor in content (1.98 MB)PDF icon Extending theory, setting a post (2 MB)PDF icon A slight update (1.98 MB)
CBMM Funded
G. Ben-Yosef, Kreiman, G., and Ullman, S., “Minimal videos: Trade-off between spatial and temporal information in human and machine vision.”, Cognition, 2020.
CBMM Funded
R. Rajalingham, Kar, K., Sanghavi, S., Dehaene, S., and DiCarlo, J. J., “The inferior temporal cortex is a potential cortical precursor of orthographic processing in untrained monkeys”, Nature Communications, vol. 11, no. 1, 2020.PDF icon s41467-020-17714-3.pdf (25.01 MB)
CBMM Funded
CBMM Memo No.
111
S. Madan, Henry, T., Dozier, J., Ho, H., Bhandari, N., Sasaki, T., Durand, F., Pfister, H., and Boix, X., “On the Capability of Neural Networks to Generalize to Unseen Category-Pose Combinations”. 2020.PDF icon CBMM-Memo-111.pdf (9.76 MB)
CBMM Funded
CBMM Memo No.
107
T. A. Poggio and Cooper, Y., “Loss landscape: SGD has a better view”. 2020.PDF icon CBMM-Memo-107.pdf (1.03 MB)PDF icon Typos and small edits, ver11 (955.08 KB)
CBMM Funded
C. Gen, Schwartz, J., Alter, S., Schrimpf, M., Traer, J., De Freitas, J., Kubilius, J., Bhandwaldar, A., Haber, N., Sano, M., Kim, K., Wang, E., Mrowca, D., Lingelbach, M., Curtis, A., Feigleis, K., Bear, D. M., Gutfreund, D., Cox, D., DiCarlo, J. J., McDermott, J. H., Tenenabum, J. B., and Yamins, D. L. K., “ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation”, arXiv, 2020.PDF icon 2007.04954.pdf (7.06 MB)
CBMM Funded
J. Schwartz, Alter, S., DiCarlo, J. J., McDermott, J. H., Tenenabum, J. B., Yamins, D. L. K., Gutfreund, D., Gan, C., Traer, J., Kubilius, J., Schrimpf, M., Bhandwaldar, A., De Freitas, J., Mrowca, D., Lingelbach, M., Sano, M., Bear, D., Kim, K., Haber, N., and Fan, C., “ThreeDWorld (TDW): A High-Fidelity, Multi-Modal Platform for Interactive Physical Simulation”. 2020.
CBMM Memo No.
110
M. Vuyyuru Reddy, Banburski, A., Pant, N., and Poggio, T., “Biologically Inspired Mechanisms for Adversarial Robustness”. 2020.PDF icon CBMM_Memo_110.pdf (3.14 MB)
CBMM Funded
CBMM Memo No.
109
A. Deza, Liao, Q., Banburski, A., and Poggio, T., “Hierarchically Local Tasks and Deep Convolutional Networks”. 2020.PDF icon CBMM_Memo_109.pdf (2.12 MB)
CBMM Funded
CBMM Memo No.
108
A. Rangamani, Rosasco, L., and Poggio, T., “For interpolating kernel machines, the minimum norm ERM solution is the most stable”. 2020.PDF icon CBMM_Memo_108.pdf (1015.14 KB)PDF icon Better bound (without inequalities!) (1.03 MB)
CBMM Funded
W. Xiao and Kreiman, G., “XDream: Finding preferred stimuli for visual neurons using generative networks and gradient-free optimization”, PLOS Computational Biology, vol. 16, no. 6, p. e1007973, 2020.PDF icon gk7791.pdf (2.39 MB)
CBMM Funded
Y. - L. Kuo, Katz, B., and Barbu, A., “Deep compositional robotic planners that follow natural language commands ”, in International Conference on Robotics and Automation (ICRA), Palais des Congrès de Paris, Paris, France, 2020.
CBMM Funded
CBMM Memo No.
106
S. Shalev-Shwartz and Shashua, A., “An Exit Strategy from the Covid-19 Lockdown based on Risk-sensitive Resource Allocation”. 2020.PDF icon CBMM-Memo-106.pdf (431.13 KB)
CBMM Funded
CBMM Memo No.
105
K. M. Villalobos, Štih, V., Ahmadinejad, A., Sundaram, S., Dozier, J., Francl, A., Azevedo, F., Sasaki, T., and Boix, X., “Do Neural Networks for Segmentation Understand Insideness?”. 2020.PDF icon CBMM-Memo-105.pdf (4.63 MB)PDF icon CBMM Memo 105 v2 (July 2, 2020) (3.2 MB)
CBMM Funded
G. Ben-Yosef, Kreiman, G., and Ullman, S., “What can human minimal videos tell us about dynamic recognition models?”, in International Conference on Learning Representations (ICLR 2020), Virtual Conference, 2020.PDF icon Authors' final version (516.09 KB)
CBMM Funded
I. Dasgupta, Schulz, E., Tenenbaum, J. B., and Gershman, S. J., “A theory of learning to infer.”, Psychological Review, vol. 127, no. 3, pp. 412 - 441, 2020.
CBMM Funded
T. Eisape, Levy, R., Tenenabum, J. B., and Zaslavsky, N., “Toward human-like object naming in artificial neural systems ”, in International Conference on Learning Representations (ICLR 2020), Bridging AI and Cognitive Science workshop, Virtual conference (due to Covid-19), 2020.
CBMM Funded
W. Lotter, Kreiman, G., and Cox, D., “A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception.”, Nature Machine Learning, 2020.PDF icon 1805.10734.pdf (9.59 MB)
CBMM Funded

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