Publication

Export 143 results:
Filters: Author is T. Poggio  [Clear All Filters]
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 <https://doi.org/10.7910/DVN/DMT0PG>
Tacchetti, A., Isik, L. & Poggio, T. Invariant recognition drives neural representations of action sequences. PLoS Comp. Bio (2017).
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)
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)
Manek, G. et al. Pruning Convolutional Neural Networks for Image Instance Retrieval. (2017). at <https://arxiv.org/abs/1707.05455>PDF icon 1707.05455.pdf (143.46 KB)
Tacchetti, A., Voinea, S., Evangelopoulos, G. & Poggio, T. Representation Learning from Orbit Sets for One-shot Classification. AAAI Spring Symposium Series, Science of Intelligence (2017). at <https://www.aaai.org/ocs/index.php/SSS/SSS17/paper/view/15357>
Anselmi, F., Evangelopoulos, G., Rosasco, L. & Poggio, T. Symmetry Regularization. (2017).PDF icon CBMM-Memo-063.pdf (6.1 MB)
Poggio, T. & Liao, Q. Theory II: Landscape of the Empirical Risk in Deep Learning. (2017).PDF icon CBMM Memo 066_1703.09833v2.pdf (5.56 MB)
Zhang, C. et al. Theory of Deep Learning IIb: Optimization Properties of SGD. (2017).PDF icon CBMM-Memo-072.pdf (3.66 MB)
Poggio, T. et al. 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)
Leibo, J. Z., Liao, Q., Anselmi, F., Freiwald, W. A. & Poggio, T. View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation. Current Biology 27, 1-6 (2017).

Pages