All Publications

2020

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
M. Schrimpf, Sato, F., Sanghavi, S., and DiCarlo, J. J., “Temporal information for action recognition only needs to be integrated at a choice level in neural networks and primates ”, in COSYNE, Denver, CO, USA, 2020.
CBMM Funded
Y. Han, Roig, G., Geiger, G., and Poggio, T., “Scale and translation-invariance for novel objects in human vision”, Scientific Reports, vol. 10, no. 1411, 2020.PDF icon s41598-019-57261-6.pdf (1.46 MB)
CBMM Funded
J. M. Hicks and McDermott, J. H., “Segregation from Noise as Outlier Detection ”, in Association for Research in Otolaryngology, San Jose, CA, USA, 2020.
CBMM Funded
V. Jacquot, Ying, J., and Kreiman, G., “Can Deep Learning Recognize Subtle Human Activities?”, CVPR 2020, 2020.
CBMM Funded
D. Kim, McMahon, E., Mehr, S., Nakayama, K., Spelke, E. S., and Vaziri-Pashkam, M., “The ability to predict actions of others from distributed cues is still developing in children”, PsyArXiv Preprints, 2020.PDF icon Action_prediction_in_children.pdf (427.84 KB)
CBMM Funded
H. Mhaskar and Poggio, T., “An analysis of training and generalization errors in shallow and deep networks”, Neural Networks, vol. 121, pp. 229 - 241, 2020.
CBMM Funded
A. J. Thomas, Saxe, R., and Spelke, E. S., “Infants represent 'like-kin' affiliation ”, in Budapest Conference on Cognitive Development, Budapest, Hungary, 2020.
CBMM Funded
M. Zhang, Tseng, C., and Kreiman, G., “Putting visual object recognition in context”, CVPR 2020, 2020.PDF icon gk7876.pdf (3.12 MB)
CBMM Funded
CBMM Memo No.
125
Y. - L. Kuo, Katz, B., and Barbu, A., “Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas”. 2020.PDF icon CBMM-Memo-125.pdf (2.12 MB)
CBMM Funded
CBMM Memo No.
124
Y. - L. Kuo, Katz, B., and Barbu, A., “Deep compositional robotic planners that follow natural language commands”. 2020.PDF icon CBMM-Memo-124.pdf (1.03 MB)
CBMM Funded
Y. - L. Kuo, Katz, B., and Barbu, A., “Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas”, in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 2020.
CBMM Funded
S. Liu, “Nature and origins of intuitive psychology in human infants”, 2020.
CBMM Funded
K. A. Smith, Mei, L., Yao, S., Wu, J., Spelke, E. S., Tenenbaum, J. B., and Ullman, T. D., “The fine structure of surprise in intuitive physics: when, why, and how much?”, in Proceedings of the 42th Annual Meeting of the Cognitive Science Society - Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020, virtual, July 29 - August 1, 2020, 2020.
CBMM Funded

2019

J. A. Leonard, Garcia, A., and Schulz, L., “How Adults’ Actions, Outcomes, and Testimony Affect Preschoolers’ Persistence”, Child Development, 2019.
CBMM Funded
S. Schwettmann, Tenenbaum, J. B., and Kanwisher, N., “Invariant representations of mass in the human brain”, eLife, vol. 8, 2019.
CBMM Funded
M. A. Cohen, Dilks, D. D., Koldewyn, K., Weigelt, S., Feather, J., Kell, A. J. E., Keil, B., Fischl, B., Zöllei, L., Wald, L., Saxe, R., and Kanwisher, N., “Representational similarity precedes category selectivity in the developing ventral visual pathway”, NeuroImage, vol. 197, pp. 565 - 574, 2019.
CBMM Funded

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