All Publications

2018

CBMM Memo No.
093
L. Arend, Han, Y., Schrimpf, M., Bashivan, P., Kar, K., Poggio, T., DiCarlo, J. J., and Boix, X., “Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results”. 2018.PDF icon CBMM-Memo-093.pdf (2.99 MB)
CBMM Funded
N. O'Brien, Latessa, S., Evangelopoulos, G., and Boix, X., “The Language of Fake News: Opening the Black-Box of Deep Learning Based Detectors”, in workshop on "AI for Social Good", NIPS 2018, Montreal, Canada, 2018.PDF icon fake-news-paper-NIPS.pdf (147.36 KB)PDF icon fake-news-paper-NIPS_2018_v2.pdf (147.36 KB)
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
T. Gerstenberg, Ullman, T. D., Nagel, J., Kleiman-Weiner, M., Lagnado, D. A., and Tenenbaum, J. B., “Lucky or clever? From changed expectations to attributions of responsibility”, Cognition, 2018.
CBMM Funded
CBMM Memo No.
091
Q. Liao, Miranda, B., Hidary, J., and Poggio, T., “Classical generalization bounds are surprisingly tight for Deep Networks”. 2018.PDF icon CBMM-Memo-091.pdf (1.43 MB)PDF icon CBMM-Memo-091-v2.pdf (1.88 MB)
CBMM Funded
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
CBMM Memo No.
084
Z. Zhang, Qiao, S., Xie, C., Shen, W., Wang, B., and Yuille, A., “Single-Shot Object Detection with Enriched Semantics”. 2018.PDF icon CBMM-Memo-084.pdf (1.92 MB)
CBMM Funded
CBMM Memo No.
083
Z. Zhang, Xie, C., Wang, J., Xie, L., and Yuille, A., “DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion”. 2018.PDF icon CBMM-Memo-083.pdf (2.32 MB)
CBMM Funded
G. Ben-Yosef and Ullman, S., “Image interpretation above and below the object level”, Proceedings of the Royal Society: Interface Focus, 2018.PDF icon 2018-BenYosef_Ullman-Image_interpretation_above_and_below the object_level.pdf (3.26 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
CBMM Memo No.
077
A. Adler and Wax, M., “Constant Modulus Algorithms via Low-Rank Approximation”. 2018.PDF icon CBMM-Memo-077.pdf (795.61 KB)
CBMM Funded
D. Harari, Tenenbaum, J. B., and Ullman, S., “Discovery and usage of joint attention in images”, arXiv.org, 2018.PDF icon 1804.04604v1.pdf (488.85 KB)
CBMM Funded
CBMM Memo No.
076
H. Mhaskar and Poggio, T., “An analysis of training and generalization errors in shallow and deep networks”. 2018.PDF icon CBMM-Memo-076.pdf (772.61 KB)PDF icon CBMM-Memo-076v2.pdf (2.67 MB)
CBMM Funded
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
D. Muir, Fang, X., and Meyers, E., “Brain-Observatory-Toolbox”. 2018.
CBMM Funded
Fde Avila Belbute-Peres, Smith, K. A., Allen, K., Tenenbaum, J. B., and Kolter, Z., “End-to-end differentiable physics for learning and control”, Advances in Neural Information Processing Systems 31 (NIPS 2018). 2018.PDF icon 7948-end-to-end-differentiable-physics-for-learning-and-control.pdf (794.17 KB)
N. Dehghani and Wimmer, R., “A computational perspective of the role of Thalamus in cognition”, arxiv, 2018.PDF icon ThalamusComputationArxiv.pdf (5.12 MB)
CBMM Related
L. Isik, Tacchetti, A., and Poggio, T., “A fast, invariant representation for human action in the visual system”, Journal of Neurophysiology, 2018.
CBMM Funded
L. Isik and Tacchetti, A., “MEG action recognition data”. 2018.
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

2017

B. M. Lake, Ullman, T. D., Tenenbaum, J. B., and Gershman, S. J., “Building machines that learn and think like people.”, Behavioral and Brain Sciences, vol. 40, p. e253, 2017.
CBMM Funded
E. Schulz, Tenenbaum, J. B., Duvenaud, D., Speekenbrink, M., and Gershman, S. J., “Compositional inductive biases in function learning.”, Cogn Psychol, vol. 99, pp. 44-79, 2017.
CBMM Funded
CBMM Memo No.
073
T. Poggio, Kawaguchi, K., Liao, Q., Miranda, B., Rosasco, L., Boix, X., Hidary, J., and Mhaskar, H., “Theory of Deep Learning III: explaining the non-overfitting puzzle”. 2017.PDF icon CBMM-Memo-073.pdf (2.65 MB)PDF icon CBMM Memo 073 v2 (revised 1/15/2018) (2.81 MB)PDF icon CBMM Memo 073 v3 (revised 1/30/2018) (2.72 MB)PDF icon CBMM Memo 073 v4 (revised 12/30/2018) (575.72 KB)
CBMM Funded
CBMM Memo No.
072
C. Zhang, Liao, Q., Rakhlin, A., Miranda, B., Golowich, N., and Poggio, T., “Theory of Deep Learning IIb: Optimization Properties of SGD”. 2017.PDF icon CBMM-Memo-072.pdf (3.66 MB)
CBMM Funded

Pages