All Publications

2020

M. Sheskin, Scott, K., Mills, C. M., Bergelson, E., Bonawitz, E., Spelke, E. S., Fei-Fei, L., Keil, F. C., Gweon, H., Tenenbaum, J. B., Jara-Ettinger, J., Adolph, K. E., Rhodes, M., Frank, M. C., Mehr, S. A., and Schulz, L., “Online Developmental Science to Foster Innovation, Access, and Impact”, Trends in Cognitive Sciences, vol. 24, no. 9, pp. 675 - 678, 2020.
CBMM Funded
CBMM Memo No.
122
C. Wang, Ross, C., Kuo, Y. - L., Katz, B., and Barbu, A., “Learning a natural-language to LTL executable semantic parser for grounded robotics”. 2020.PDF icon CBMM-Memo-122.pdf (1.03 MB)
CBMM Funded
CBMM Memo No.
112
T. Poggio, Liao, Q., and Xu, M., “Implicit dynamic regularization in deep networks”. 2020.PDF icon TPR_ver2.pdf (2.29 MB)PDF icon Substantial edits (1.52 MB)PDF icon Extending theory, setting a post (2 MB)PDF icon Small edits clarifying role of weight decay (2.39 MB)PDF icon Added: prove NC for multiclass+theorem on connected global minima (2.4 MB)PDF icon Update on rank (2.43 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
H. Mhaskar and Poggio, T., “Function approximation by deep networks”, Communications on Pure & Applied Analysis, vol. 19, no. 8, pp. 4085 - 4095, 2020.PDF icon 1534-0392_2020_8_4085.pdf (514.57 KB)
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
E. Schulz, Quiroga, F., and Gershman, S. J., “Communicating Compositional Patterns”, Open Mind, vol. 4, pp. 25 - 39, 2020.
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. 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)PDF icon Small edits, corrected Hessian for spurious case (337.19 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., Gutfreund, D., Cox, D., DiCarlo, J. J., McDermott, J. H., Tenenbaum, 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., Tenenbaum, 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
H. Sanders, Wilson, M. A., and Gershman, S. J., “Hippocampal remapping as hidden state inference”, eLife, vol. 9, 2020.
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)PDF icon CBMM Memo 105 v3 (January 25, 2022) (8.33 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., Tenenbaum, 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
S. Kim and Spelke, E. S., “Learning from multiple informants: Children’s response to epistemic bases for consensus judgments”, Journal of Experimental Child Psychology, vol. 192, p. 104759, 2020.
CBMM Related
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
W. Lotter, Kreiman, G., and Cox, D., “A neural network trained for prediction mimics diverse features of biological neurons and perception”, Nature Machine Intelligence, vol. 2, no. 4, pp. 210 - 219, 2020.
CBMM Funded

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