All Publications

2017

CBMM Memo No.
071
F. Cano-Córdoba, Sarma, S., and Subirana, B., “Theory of Intelligence with Forgetting: Mathematical Theorems Explaining Human Universal Forgetting using “Forgetting Neural Networks””. 2017.PDF icon CBMM-Memo-071.pdf (2.54 MB)
CBMM Funded
M. Janner, Wu, J., Kulkarni, T., Yildirim, I., and Tenenbaum, J. B., “Self-supervised intrinsic image decomposition.”, in Annual Conference on Neural Information Processing Systems (NIPS), Long Beach, CA, 2017.PDF icon intrinsicImg_nips_2017.pdf (5.87 MB)
CBMM Funded
L. Isik, Tacchetti, A., and Poggio, T., “A fast, invariant representation for human action in the visual system.”, J Neurophysiol, p. jn.00642.2017, 2017.PDF icon Author's last draft (695.63 KB)
CBMM Funded
T. Liang, Poggio, T., Rakhlin, A., and Stokes, J., “Fisher-Rao Metric, Geometry, and Complexity of Neural Networks”, 2017.PDF icon 1711.01530.pdf (966.99 KB)
CBMM Related
A. Tacchetti, Isik, L., and Poggio, T., “Invariant action recognition dataset”. 2017.
CBMM Funded
CBMM Memo No.
070
Q. Liao and Poggio, T., “Object-Oriented Deep Learning”. 2017.PDF icon CBMM-Memo-070.pdf (963.54 KB)
CBMM Funded
R. Saxe and Houlihan, S. Dae, “Formalizing emotion concepts within a Bayesian model of theory of mind”, Current Option in Psychology, vol. 17, pp. 15-21, 2017.PDF icon 1-s2.0-S2352250X17300283-main.pdf (613.77 KB)
CBMM Funded
CBMM Memo No.
078
J. Wang, Xie, C., Zhang, Z., Zhu, J., Xie, L., and Yuille, A., “Detecting Semantic Parts on Partially Occluded Objects”. 2017.PDF icon CBMM-Memo-078.pdf (1.74 MB)
CBMM Funded
H. Lin and Tegmark, M., “Why does deep and cheap learning work so well?”, Journal of Statistical Physics, vol. 168, no. 6, pp. 1223–1247, 2017.PDF icon 1608.08225.pdf (2.14 MB)
CBMM Related
T. D. Ullman, Spelke, E. S., Battaglia, P., and Tenenbaum, J. B., “Mind Games: Game Engines as an Architecture for Intuitive Physics”, Trends in Cognitive Science, vol. 21, no. 9, pp. 649 - 665, 2017.PDF icon Preprint submitted to Trends in Cognitive Science (17.64 MB)
CBMM Funded
L. Jing, Shen, Y., Dubček, T., Peurifoi, J., Skirlo, S., LeCun, Y., Tegmark, M., and Soljačić, M., “Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNN”, 34th International Conference on Machine Learning, vol. 70. pp. 1733-1741 , 2017.PDF icon 1612.05231.pdf (2.3 MB)
CBMM Related
G. Manek, Lin, J., Chandrasekhar, V., Duan, L., Giduthuri, S., Li, X., and Poggio, T., “Pruning Convolutional Neural Networks for Image Instance Retrieval”, 2017.PDF icon 1707.05455.pdf (143.46 KB)
CBMM Related
CBMM Memo No.
069
A. Volokitin, Roig, G., and Poggio, T., “Do Deep Neural Networks Suffer from Crowding?”. 2017.PDF icon CBMM-Memo-069.pdf (6.47 MB)
CBMM Funded
CBMM Memo No.
068
B. Subirana, Bagiati, A., and Sarma, S., “On the Forgetting of College Academics: at "Ebbinghaus Speed"?”. 2017.PDF icon CBMM Memo 068-On Forgetting - June 18th 2017 v2.pdf (713.7 KB)
CBMM Funded
A. Volokitin and Roig, G., “Do Deep Neural Networks Suffer from Crowding? [code]”. 2017.
CBMM Funded
CBMM Memo No.
063
F. Anselmi, Evangelopoulos, G., Rosasco, L., and Poggio, T., “Symmetry Regularization”. 2017.PDF icon CBMM-Memo-063.pdf (6.1 MB)
CBMM Funded
CBMM Memo No.
067
C. Zhang, Liao, Q., Rakhlin, A., Sridharan, K., Miranda, B., Golowich, N., and Poggio, T., “Musings on Deep Learning: Properties of SGD”. 2017.PDF icon CBMM Memo 067 v2 (revised 7/19/2017) (5.88 MB)PDF icon CBMM Memo 067 v3 (revised 9/15/2017) (5.89 MB)PDF icon CBMM Memo 067 v4 (revised 12/26/2017) (5.57 MB)
CBMM Funded
CBMM Memo No.
066
T. Poggio and Liao, Q., “Theory II: Landscape of the Empirical Risk in Deep Learning”. 2017.PDF icon CBMM Memo 066_1703.09833v2.pdf (5.56 MB)
CBMM Funded
CBMM Memo No.
062
A. Tacchetti, Voinea, S., and Evangelopoulos, G., “Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets”. 2017.PDF icon CBMM-Memo-062.pdf (9.37 MB)
CBMM Funded
T. Poggio, Mhaskar, H., Rosasco, L., Miranda, B., and Liao, Q., “Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review”, International Journal of Automation and Computing, pp. 1-17, 2017.PDF icon art%3A10.1007%2Fs11633-017-1054-2.pdf (1.68 MB)
CBMM Funded
V. Chandrasekhar, Lin, J., Liao, Q., Morère, O., Veillard, A., Duan, L., and Poggio, T., “Compression of Deep Neural Networks for Image Instance Retrieval”, 2017.PDF icon 1701.04923.pdf (614.33 KB)
CBMM Related
J. Z. Leibo, Liao, Q., Anselmi, F., Freiwald, W. A., and Poggio, T., “View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation”, Current Biology, vol. 27, pp. 1-6, 2017.
CBMM Funded
F. Chen, Roig, G., Isik, L., Boix, X., and Poggio, T., “Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision”, in AAAI Spring Symposium Series, Science of Intelligence, 2017.PDF icon paper.pdf (963.87 KB)
CBMM Funded
N. Dehghani, “Design of the Artificial: lessons from the biological roots of general intelligence”, 2017.PDF icon DesignArtificial_Dehghani_arXiv.pdf (222.47 KB)
CBMM Related
G. A del Molino, Boix, X., Lim, J., and Tan, A., “Active Video Summarization: Customized Summaries via On-line Interaction.”, in AAAI Conference on Artificial Intelligence, 2017.PDF icon 21-Garcia-del-Molino-14856.pdf (413.77 KB)
CBMM Related

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