All Publications

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
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. 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. A., and Tenenbaum, J. B., “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-Trujillo, 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
G. Kreiman and Serre, T., “Beyond the feedforward sweep: feedback computations in the visual cortex”, Ann. N.Y. Acad. Sci. | Special Issue: The Year in Cognitive Neuroscience, vol. 1464, no. 1, pp. 222-241, 2020.PDF icon gk7812.pdf (1.93 MB)
CBMM Funded
G. Kreiman and Serre, T., “Beyond the feedforward sweep: feedback computations in the visual cortex”, Annals of the New York Academy of Sciences, vol. 1464, no. 1, pp. 222 - 241, 2020.
CBMM Funded
T. Marques, Schrimpf, M., and DiCarlo, J. J., “Hierarchical neural network models that more closely match primary visual cortex tend to better explain higher level visual cortical responses ”, in COSYNE, Denver, Colorado, USA, 2020.
CBMM Funded
T. Poggio, Liao, Q., and Banburski, A., “Complexity Control by Gradient Descent in Deep Networks”, Nature Communications, vol. 11, 2020.PDF icon s41467-020-14663-9.pdf (431.68 KB)
CBMM Funded

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