Biblio

2018
Adler A.  2018.  Eve Zisselman, Amir Adler, Michael Elad, "Compressed Learning for Image Classification: A Deep Neural Network Approach", Handbook of Numerical Analysis, Elsevier, Volume 19, 2018, Pages 3-17..
2017
Chen F., Roig G., Boix X., Poggio T..  2017.  Eccentricity Dependent Deep Neural Networks for Modeling Human Vision. Vision Sciences Society.
Chen F., Roig G., Isik L., Boix X., Poggio T..  2017.  Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision. AAAI Spring Symposium Series, Science of Intelligence.
Han Y., Roig G., Gaiger G., Poggio T..  2017.  On the Human Visual System Invariance to Translation and Scale. Vision Sciences Society.
Han Y., Roig G., Geiger G., Poggio T..  2017.  Is the Human Visual System Invariant to Translation and Scale? AAAI Spring Symposium Series, Science of Intelligence.
Mutch J., Anselmi F., Tacchetti A., Rosasco L., Leibo J., Poggio T..  2017.  Invariant Recognition Predicts Tuning of Neurons in Sensory Cortex. Computational and Cognitive Neuroscience of Vision. :85-104.
Leibo J.Z., Liao Q., Freiwald W., Anselmi F., Poggio T..  2017.  View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation. Current Biology. 27:1-6.
Poggio T., Mhaskar H, Rosasco L., Miranda B., Liao Q..  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.
2016
Liao Q., Poggio T..  2016.  Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex.
Poggio T..  2016.  Deep Learning: Mathematics and Neuroscience. A Sponsored Supplement to Science. Brain-Inspired intelligent robotics: The intersection of robotics and neuroscience:9-12.
Poggio T..  2016.  Deep Learning: mathematics and neuroscience- CBMM Views & Reviews.
Mhaskar H., Poggio T..  2016.  Deep vs. shallow networks : An approximation theory perspective.
Poggio T, Isik L, Tacchetti A..  2016.  Fast, invariant representation for human action in the visual system.
Luo Y., Boix X., Roig G., Zhao Q., Poggio T..  2016.  Foveation-based Mechanisms Alleviate Adversarial Examples.
Poggio T, Chandrasekhar V, Petta J, Lin J, Veillard A, Morere O.  2016.  Group Invariant Deep Representations for Image Instance Retrieval.
Poggio T., Nickel M., Rosasco L..  2016.  Holographic Embeddings of Knowledge Graphs. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16).
Liao Q., Leibo JZ., Poggio T..  2016.  How Important Is Weight Symmetry in Backpropagation? Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16).
Poggio T., Bach F..  2016.  Introduction Special issue: Deep learning. Information and Inference. 5:103-104.
Poggio T., Rosasco L., Anselmi F..  2016.  On invariance and selectivity in representation learning. Information and Inference: A Journal of the IMA. :iaw009.
Mhaskar H., Liao Q., Poggio T..  2016.  Learning Functions: When Is Deep Better Than Shallow”.
Poggio T., Tan C..  2016.  Neural Tuning Size in a Model of Primate Visual Processing Accounts for Three Key Markers of Holistic Face Processing. Public Library of Science | PLoS ONE. 1(3): e0150980
Poggio T., Lewis O..  2016.  Object and Scene Perception. From Neuron to Cognition via Computational Neuroscience.
Poggio T., Liao Q., Kawaguchi K..  2016.  Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning.
Poggio T., Meyers E.M..  2016.  Turing++ Questions: A Test for the Science of (Human) Intelligence.. AI Magazine. 37(1):73-77.
Poggio T., Anselmi F..  2016.  Visual Cortex and Deep Networks: Learning Invariant Representations. :136.

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