Biblio

Filters: Author is Poggio, T.  [Clear All Filters]
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.
Mhaskar H., Poggio T..  2016.  Deep vs. shallow networks : An approximation theory perspective.
Luo Y., Boix X., Roig G., Zhao Q., Poggio T..  2016.  Foveation-based Mechanisms Alleviate Adversarial Examples.
Liao Q., Leibo JZ., Poggio T..  2016.  How Important Is Weight Symmetry in Backpropagation? Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16).
Mhaskar H., Liao Q., Poggio T..  2016.  Learning Functions: When Is Deep Better Than Shallow”.
Poggio T., Liao Q., Kawaguchi K..  2016.  Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning.
Poggio T., Mhaskar H., Rosasco L., Miranda B., Liao Q..  2016.  Why and When Can Deep - but Not Shallow - Networks Avoid the Curse of Dimensionality: a Review.
2015
Anselmi F., Rosasco L., Tan C., Poggio T..  2015.  Deep Convolutional Networks are Hierarchical Kernel Machines.
Nickel M., Rosasco L., Poggio T..  2015.  "Holographic Embeddings of Knowledge Graphs”.
Anselmi F., Rosasco L., Poggio T..  2015.  On Invariance and Selectivity in Representation Learning.
Leibo J.Z, Liao Q., Anselmi F., Poggio T..  2015.  “The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex”. PLOS Computational Biology, . 11
Koch C., Poggio T..  2015.  A Science of Intelligence.
Poggio T..  2015.  What If....
2014
Liao Q., Leibo JZ., Mroueh Y., Poggio T..  2014.  Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines?
Leibo JZ., Liao Q., Anselmi F., Poggio T..  2014.  The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex.
Liao Q., Leibo JZ., Poggio T..  2014.  Learning invariant representations and applications to face verification. NIPS 2013.
Anselmi F., Poggio T..  2014.  Representation Learning in Sensory Cortex: a theory.

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