All Publications

2019

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
K. M. Jozwik, Schrimpf, M., Kanwisher, N., and DiCarlo, J. J., “To find better neural network models of human vision, find better neural network models of primate vision”, in BioRxiv, 2019.
CBMM Funded

2018

CBMM Memo No.
092
W. Xiao, Chen, H., Liao, Q., and Poggio, T., “Biologically-plausible learning algorithms can scale to large datasets”. 2018.PDF icon CBMM-Memo-092.pdf (1.31 MB)
CBMM Funded
T. Owaki, Vidal-Naquet, M., Nam, Y., Uchida, G., Sato, T., Câteau, H., Ullman, S., and Tanifuji, M., “Searching for visual features that explain response variance of face neurons in inferior temporal cortex”, PLOS ONE, vol. 13, no. 9, p. e0201192, 2018.
CBMM Related
CBMM Memo No.
090
A. Banburski, Liao, Q., Miranda, B., Poggio, T., Rosasco, L., Hidary, J., and De La Torre, F., “Theory III: Dynamics and Generalization in Deep Networks”. 2018.PDF icon Original, intermediate versions are available under request (2.67 MB)PDF icon CBMM Memo 90 v12.pdf (4.74 MB)PDF icon Theory_III_ver44.pdf Update Hessian (4.12 MB)PDF icon Theory_III_ver48 (Updated discussion of convergence to max margin) (2.56 MB)PDF icon fixing errors and sharpening some proofs (2.45 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
M. Schrimpf and Kubilius, J., “Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?”, bioRxiv preprint, 2018.PDF icon Brain-Score bioRxiv.pdf (789.83 KB)
CBMM Related

2017

A. Arsalan Soltani, Huang, H., Wu, J., Kulkarni, T., and Tenenbaum, J. B., “Synthesizing 3D Shapes via Modeling Multi-view Depth Maps and Silhouettes with Deep Generative Networks”, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017.PDF icon Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes with Deep Generative Networks.pdf (2.86 MB)
CBMM Funded

Pages