Biblio

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2016
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.
Poggio T, Rosasco L, Evangelopoulos G, Voinea S, Zhang C.  2015.  Discriminative Template Learning in Group-Convolutional Networks for Invariant Speech Representations. INTERSPEECH 2015.
Nickel M., Rosasco L., Poggio T..  2015.  "Holographic Embeddings of Knowledge Graphs”.
Poggio T, Liao Q, Leibo JZ.  2015.  How Important Is Weight Symmetry in Backpropagation?
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
Poggio T, Anselmi F, Rosasco L.  2015.  I-theory on depth vs width: hierarchical function composition.
Poggio T, Voinea S, Mroueh Y.  2015.  Learning with Group Invariant Features: A Kernel Perspective. NIPS 2015.
Poggio T., L. R, Shashua A., Cohen N., Anselmi F..  2015.  Notes on Hierarchical Splines, DCLNs and i-theory.
Nickel M., K. M., Tresp V., Gabrilovich E..  2015.  “A Review of Relational Machine Learning for Knowledge Graphs: From Multi-Relational Link Prediction to Automated Knowledge Graph Construction”.
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?
Poggio T, Mutch J, Isik L.  2014.  Computational role of eccentricity dependent cortical magnification..
Zhang C, Evangelopoulos G, Voines S, Rosasco L, Poggio T.  2014.  A Deep Representation for Invariance And Music Classification. ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing.
Poggio T, Leibo JZ, Meyers E, Isik L.  2014.  The dynamics of invariant object recognition in the human visual system. Journal of Neurophysiology. 111(1)
Leibo JZ., Liao Q., Anselmi F., Poggio T..  2014.  The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex.
Poggio T, Rosasco L, Zhang C, Voinea S, Evangelopoulos G.  2014.  Learning An Invariant Speech Representation.
Liao Q., Leibo JZ., Poggio T..  2014.  Learning invariant representations and applications to face verification. NIPS 2013.
Zhang C, Frogner C, Araya-Polo M, Hohl D.  2014.  Machine-learning Based Automated Fault Detection in Seismic Traces ∗. EAGE Conference and Exhibition 2014.
Poggio T, Tan C.  2014.  Neural tuning size is a key factor underlying holistic face processing..
Zhang C, Voinea S, Evangelopoulos G, Rosasco L, Poggio T.  2014.  Phone Classification by a Hierarchy of Invariant Representation Layers. INTERSPEECH 2014 - 15th Annual Conf. of the International Speech Communication Association.
Anselmi F., Poggio T..  2014.  Representation Learning in Sensory Cortex: a theory.
Anselmi F, Poggio T.  2014.  Representation Learning in Sensory Cortex: a theory..

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