Export 136 results:
Filters: Author is Tomaso Poggio [Clear All Filters]
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>
A fast, invariant representation for human action in the visual system. J Neurophysiol jn.00642.2017 (2017). doi:10.1152/jn.00642.2017
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. arXiv.org (2017). at <https://arxiv.org/abs/1711.01530>
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).
Computational and Cognitive Neuroscience of Vision 85-104 (Springer, 2017).
Object-Oriented Deep Learning. (2017).
Pruning Convolutional Neural Networks for Image Instance Retrieval. (2017). at <https://arxiv.org/abs/1707.05455>
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).
View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation. Current Biology 27, 1-6 (2017).
When and Why Are Deep Networks Better Than Shallow Ones?. AAAI-17: Thirty-First AAAI Conference on Artificial Intelligence (2017).
Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review. International Journal of Automation and Computing 1-17 (2017). doi:10.1007/s11633-017-1054-2
Deep Leaning: Mathematics and Neuroscience. A Sponsored Supplement to Science Brain-Inspired intelligent robotics: The intersection of robotics and neuroscience, 9-12 (2016).
Deep vs. shallow networks: An approximation theory perspective. Analysis and Applications 14, 829 - 848 (2016).