Publication

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2018
Mhaskar, H. & Poggio, T. An analysis of training and generalization errors in shallow and deep networks. (2018).PDF icon CBMM-Memo-076.pdf (772.61 KB)
Xiao, W., Chen, H., Liao, Q. & Poggio, T. Biologically-plausible learning algorithms can scale to large datasets. (2018).PDF icon CBMM-Memo-092.pdf (1.31 MB)
Liao, Q., Miranda, B., Hidary, J. & 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)
Isik, L., Tacchetti, A. & Poggio, T. A fast, invariant representation for human action in the visual system. Journal of Neurophysiology (2018). doi:https://doi.org/10.1152/jn.00642.2017
Tacchetti, A., Isik, L. & Poggio, T. Invariant Recognition Shapes Neural Representations of Visual Input. Annual Review of Vision Science 4, 403 - 422 (2018).PDF icon annurev-vision-091517-034103.pdf (1.55 MB)
Arend, L. et al. 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)
Poggio, T. & Liao, Q. Theory I: Deep networks and the curse of dimensionality. Bulletin of the Polish Academy of Sciences: Technical Sciences 66, (2018).PDF icon 02_761-774_00966_Bpast.No_.66-6_28.12.18_K1.pdf (1.18 MB)
Poggio, T. & Liao, Q. Theory II: Deep learning and optimization. Bulletin of the Polish Academy of Sciences: Technical Sciences 66, (2018).PDF icon 03_775-788_00920_Bpast.No_.66-6_31.12.18_K2.pdf (5.43 MB)
Banburski, A. et al. Theory III: Dynamics and Generalization in Deep Networks. (2018).PDF icon TheoryIII_ver2 (2.67 MB)PDF icon TheoryIII_ver11 (4.17 MB)PDF icon TheoryIII_ver12 (4.74 MB)PDF icon TheoryIII_ver13 (4.75 MB)
2017
Chandrasekhar, V. et al. Compression of Deep Neural Networks for Image Instance Retrieval. (2017). at <https://arxiv.org/abs/1701.04923>PDF icon 1701.04923.pdf (614.33 KB)
Volokitin, A., Roig, G. & Poggio, T. Do Deep Neural Networks Suffer from Crowding?. (2017).PDF icon CBMM-Memo-069.pdf (6.47 MB)
Roig, G., Chen, F., Boix, X. & Poggio, T. Eccentricity Dependent Deep Neural Networks for Modeling Human Vision. Vision Sciences Society (2017).
Chen, F., Roig, G., Isik, L., Boix, X. & Poggio, T. Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision. AAAI Spring Symposium Series, Science of Intelligence (2017). at <https://www.aaai.org/ocs/index.php/SSS/SSS17/paper/view/15360>PDF icon paper.pdf (963.87 KB)
Isik, L., Tacchetti, A. & Poggio, T. A fast, invariant representation for human action in the visual system. J Neurophysiol jn.00642.2017 (2017). doi:10.1152/jn.00642.2017PDF icon Author's last draft (695.63 KB)
Liang, T., Poggio, T., Rakhlin, A. & Stokes, J. Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. arXiv.org (2017). at <https://arxiv.org/abs/1711.01530>PDF icon 1711.01530.pdf (966.99 KB)
Han, Y., Roig, G., Geiger, G. & Poggio, T. On the Human Visual System Invariance to Translation and Scale. Vision Sciences Society (2017).
Han, Y., Roig, G., Geiger, G. & Poggio, T. Is the Human Visual System Invariant to Translation and Scale?. AAAI Spring Symposium Series, Science of Intelligence (2017).
Tacchetti, A., Isik, L. & Poggio, T. Invariant action recognition dataset. (2017). at <http://web.mit.edu/atacchet/www/dataset/>
Tacchetti, A., Isik, L. & Poggio, T. Invariant recognition drives neural representations of action sequences. PLOS Computational Biology 13, e1005859 (2017).PDF icon journal.pcbi_.1005859.pdf (9.24 MB)
Tacchetti, A., Isik, L. & Poggio, T. Invariant recognition drives neural representations of action sequences. PLoS Comp. Bio (2017).
Mutch, J. et al. Computational and Cognitive Neuroscience of Vision 85-104 (Springer, 2017).
Zhang, C. et al. 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)
Liao, Q. & Poggio, T. Object-Oriented Deep Learning. (2017).PDF icon CBMM-Memo-070.pdf (963.54 KB)

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