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
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
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
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
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)
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
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
N. Zaslavsky, Hu, J., and Levy, R., “Emergence of Pragmatic Reasoning From Least-Effort Optimization ”, in 13th International Conference on the Evolution of Language (EvoLang) , The conference was canceled due to Covid-19, 2020.
CBMM Funded
CBMM Memo No.
104
S. Shalev-Shwartz and Shashua, A., “Can we Contain Covid-19 without Locking-down the Economy?”. 2020.PDF icon CBMM Memo 104 v4 (Apr. 6, 2020) (418.25 KB)PDF icon CBMM Memo 104 v3 (Apr. 1, 2020) (452.94 KB)PDF icon CBMM Memo 104 v2 (Mar. 28, 2020) (427.39 KB)PDF icon CBMM-Memo-104.pdf (425.12 KB)
CBMM Memo No.
103
T. A. Poggio, “Stable Foundations for Learning: a framework for learning theory (in both the classical and modern regime).”. 2020.PDF icon Original file (584.54 KB)PDF icon Corrected typos and details of "equivalence" CV stability and expected error for interpolating machines. Added Appendix on SGD.  (905.29 KB)PDF icon Edited Appendix on SGD. (909.19 KB)PDF icon Deleted Appendix. Corrected typos etc (880.27 KB)PDF icon Added result about square loss and min norm (898.03 KB)
CBMM Funded
I. Yildirim, Belledonne, M., Freiwald, W., and Tenenbaum, J., “Efficient inverse graphics in biological face processing”, Science Advances, vol. 6, no. 10, p. eaax5979, 2020.PDF icon eaax5979.full_.pdf (3.22 MB)
CBMM Funded
K. Dobs, Kell, A. J. E., Martinez, J., Cohen, M., and Kanwisher, N., “Using task-optimized neural networks to understand why brains have specialized processing for faces ”, in Computational and Systems Neurosciences, Denver, CO, USA, 2020.
CBMM Funded
W. A. Freiwald, “The neural mechanisms of face processing: cells, areas, networks, and models”, Current Opinion in Neurobiology, vol. 60, pp. 184 - 191, 2020.
CBMM Funded
K. Kar and DiCarlo, J. J., “Evidence that recurrent pathways between the prefrontal and inferior temporal cortex is critical during core object recognition ”, in COSYNE, Denver, Colorado, USA, 2020.
CBMM Funded

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