All Publications

2019

A. Banburski, Liao, Q., Miranda, B., Rosasco, L., Hidary, J., and Poggio, T., Dynamics & Generalization in Deep Networks -Minimizing the Norm, in NAS Sackler Colloquium on Science of Deep Learning, Washington D.C., 2019.
CBMM Funded
CBMM Funded
S. J. Gershman, How to never be wrong, Psychonomic Bulletin & Review, vol. 26, no. 1, pp. 13 - 28, 2019.
CBMM Funded
S. Ullman, Using neuroscience to develop artificial intelligence, Science, vol. 363, no. 6428, pp. 692 - 693, 2019.
CBMM Related
CBMM Related
CBMM Funded
C. I. Calero, Shalom, D. E., Spelke, E. S., and Sigman, M., Language, gesture, and judgment: Children’s paths to abstract geometry, Journal of Experimental Child Psychology, vol. 177, pp. 70 - 85, 2019.
CBMM Funded
K. Dobs, Isik, L., Pantazis, D., and Kanwisher, N., How face perception unfolds over time, Nature Communications, vol. 10, no. 1, 2019.
CBMM Funded
CBMM Related
CBMM Funded
M. Araya-Polo, Adler, A., Farris, S., and Jennings, J., Fast and Accurate Seismic Tomography via Deep Learning, in Deep Learning: Algorithms and Applications, SPRINGER-VERLAG, 2019.
CBMM Related
CBMM Funded
S. Srivastava, Ben-Yosef, G., and Boix, X., Minimal images in deep neural networks: Fragile Object Recognition in Natural Images, in International Conference on Learning Representations (ICLR), New Orleans, La, 2019.
CBMM Funded
W. Xiao, Chen, H., Liao, Q., and Poggio, T., Biologically-plausible learning algorithms can scale to large datasets., in International Conference on Learning Representations, (ICLR 2019), 2019.PDF icon gk7779.pdf (721.53 KB)
CBMM Funded

2018

CBMM Funded

Pages