All Publications

2019

S. J. Gershman, How to never be wrong, Psychonomic Bulletin & Review, vol. 26, no. 1, pp. 13 - 28, 2019.
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
W. Xiao, Chen, H., Liao, Q., and Poggio, T., Biologically-Plausible Learning Algorithms Can Scale to Large Datasets, in International Conference on Learning Representations, 2019.
CBMM Funded

2018

CBMM Funded
A. Tacchetti, Isik, L., and Poggio, T., Invariant Recognition Shapes Neural Representations of Visual Input, Annual Review of Vision Science, vol. 4, no. 1, pp. 403 - 422, 2018.PDF icon annurev-vision-091517-034103.pdf (1.55 MB)
CBMM Funded
J. B. Hamrick, Allen, K., Bapst, V., Zhu, T., McKee, K. R., Tenenbaum, J. B., and Battaglia, P., Relational inductive bias for physical construction in humans and machines, In Proceedings of the Annual Meeting of the Cognitive Science Society (CogSci 2018). 2018.PDF icon 1806.01203.pdf (1022.51 KB)
CBMM Related
M. Araya-Polo, Jennings, J., Adler, A., and Dahlke, T., Deep-learning tomography, The Leading Edge, vol. 37, no. 1, pp. 58 - 66, 2018.PDF icon TLE2018.pdf (1.9 MB)
CBMM Funded
T. Poggio and Liao, Q., Theory I: Deep networks and the curse of dimensionality, Bulletin of the Polish Academy of Sciences: Technical Sciences, vol. 66, no. 6, 2018.PDF icon 02_761-774_00966_Bpast.No_.66-6_28.12.18_K1.pdf (1.18 MB)
CBMM Funded
T. Poggio and Liao, Q., Theory II: Deep learning and optimization, Bulletin of the Polish Academy of Sciences: Technical Sciences, vol. 66, no. 6, 2018.PDF icon 03_775-788_00920_Bpast.No_.66-6_31.12.18_K2.pdf (5.43 MB)
CBMM Funded

Pages