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Filters: Author is Tomaso Poggio [Clear All Filters]
Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results. (2018).
CBMM-Memo-093.pdf (2.99 MB)
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Theory I: Deep networks and the curse of dimensionality. Bulletin of the Polish Academy of Sciences: Technical Sciences 66, (2018).
02_761-774_00966_Bpast.No_.66-6_28.12.18_K1.pdf (1.18 MB)
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Theory II: Deep learning and optimization. Bulletin of the Polish Academy of Sciences: Technical Sciences 66, (2018).
03_775-788_00920_Bpast.No_.66-6_31.12.18_K2.pdf (5.43 MB)
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Theory III: Dynamics and Generalization in Deep Networks. (2018).
Original, intermediate versions are available under request (2.67 MB)
CBMM Memo 90 v12.pdf (4.74 MB)
Theory_III_ver44.pdf Update Hessian (4.12 MB)
Theory_III_ver48 (Updated discussion of convergence to max margin) (2.56 MB)
fixing errors and sharpening some proofs (2.45 MB)
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Compression of Deep Neural Networks for Image Instance Retrieval. (2017). at <https://arxiv.org/abs/1701.04923>
1701.04923.pdf (614.33 KB)
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Do Deep Neural Networks Suffer from Crowding?. (2017).
CBMM-Memo-069.pdf (6.47 MB)
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Eccentricity Dependent Deep Neural Networks for Modeling Human Vision. Vision Sciences Society (2017).
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>
paper.pdf (963.87 KB)
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A fast, invariant representation for human action in the visual system. J Neurophysiol jn.00642.2017 (2017). doi:10.1152/jn.00642.2017
Author's last draft (695.63 KB)
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Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. arXiv.org (2017). at <https://arxiv.org/abs/1711.01530>
1711.01530.pdf (966.99 KB)
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On the Human Visual System Invariance to Translation and Scale. Vision Sciences Society (2017).
Is the Human Visual System Invariant to Translation and Scale?. AAAI Spring Symposium Series, Science of Intelligence (2017).
Invariant recognition drives neural representations of action sequences. PLoS Comp. Bio (2017).
Invariant recognition drives neural representations of action sequences. PLOS Computational Biology 13, e1005859 (2017).
journal.pcbi_.1005859.pdf (9.24 MB)
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Computational and Cognitive Neuroscience of Vision 85-104 (Springer, 2017).
Musings on Deep Learning: Properties of SGD. (2017).
CBMM Memo 067 v2 (revised 7/19/2017) (5.88 MB)
CBMM Memo 067 v3 (revised 9/15/2017) (5.89 MB)
CBMM Memo 067 v4 (revised 12/26/2017) (5.57 MB)
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Object-Oriented Deep Learning. (2017).
CBMM-Memo-070.pdf (963.54 KB)
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Pruning Convolutional Neural Networks for Image Instance Retrieval. (2017). at <https://arxiv.org/abs/1707.05455>
1707.05455.pdf (143.46 KB)
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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>
Symmetry Regularization. (2017).
CBMM-Memo-063.pdf (6.1 MB)
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Theory II: Landscape of the Empirical Risk in Deep Learning. (2017).
CBMM Memo 066_1703.09833v2.pdf (5.56 MB)
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Theory of Deep Learning IIb: Optimization Properties of SGD. (2017).
CBMM-Memo-072.pdf (3.66 MB)
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Theory of Deep Learning III: explaining the non-overfitting puzzle. (2017).
CBMM-Memo-073.pdf (2.65 MB)
CBMM Memo 073 v2 (revised 1/15/2018) (2.81 MB)
CBMM Memo 073 v3 (revised 1/30/2018) (2.72 MB)
CBMM Memo 073 v4 (revised 12/30/2018) (575.72 KB)
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View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation. Current Biology 27, 1-6 (2017).