Publications

2018

Adler, Amir. 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.., 2018.

2017

Poggio, T., H Mhaskar, L. Rosasco, B. Miranda, and Q. Liao. "Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review." International Journal of Automation and Computing (2017): 1-17.
Leibo, J.Z., Q. Liao, W. Freiwald, F. Anselmi, and T. Poggio. "View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation." Current Biology 27 (2017): 1-6.
Chen, F., G. Roig, X. Boix, and T. Poggio. "Eccentricity Dependent Deep Neural Networks for Modeling Human Vision." In Vision Sciences Society., 2017.
Chen, F., G. Roig, L. Isik, X. Boix, and T. Poggio. "Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision." In AAAI Spring Symposium Series, Science of Intelligence., 2017.
Han, Y., G. Roig, G. Geiger, and T. Poggio. "Is the Human Visual System Invariant to Translation and Scale?" In AAAI Spring Symposium Series, Science of Intelligence., 2017.
Han, Y., G. Roig, G. Gaiger, and T. Poggio. "On the Human Visual System Invariance to Translation and Scale." In Vision Sciences Society., 2017.
Mutch, J., F. Anselmi, A. Tacchetti, L. Rosasco, J. Leibo, and T. Poggio. "Invariant Recognition Predicts Tuning of Neurons in Sensory Cortex." In Computational and Cognitive Neuroscience of Vision, 85-104. Springer, 2017.

2016

Poggio, T., M. Nickel, and L. Rosasco. "Holographic Embeddings of Knowledge Graphs." In Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16). Phoenix, Arizona, USA, 2016.
Poggio, T.. "Deep Learning: Mathematics and Neuroscience." A Sponsored Supplement to Science Brain-Inspired intelligent robotics: The intersection of robotics and neuroscience (2016): 9-12.
Poggio, T., H. Mhaskar, L. Rosasco, B. Miranda, and Q. Liao. Why and When Can Deep - but Not Shallow - Networks Avoid the Curse of Dimensionality: a Review., 2016.
CBMM Memo No.
58
Poggio, T., Q. Liao, and K. Kawaguchi. Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning., 2016.
CBMM Memo No.
57
Mhaskar, H., and T. Poggio. Deep vs. shallow networks : An approximation theory perspective., 2016.
CBMM Memo No.
54
Poggio, T., L. Rosasco, and F. Anselmi. "On invariance and selectivity in representation learning." Information and Inference: A Journal of the IMA (2016): iaw009.
Poggio, T., and E.M. Meyers. "Turing++ Questions: A Test for the Science of (Human) Intelligence." AI Magazine 37, no. 1 (2016): 73-77.
Poggio, T., and C. Tan. "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 (2016).
Poggio, T, L Isik, and A. Tacchetti. Fast, invariant representation for human action in the visual system., 2016.
CBMM Memo No.
42
Luo, Y., X. Boix, G. Roig, Q. Zhao, and T. Poggio. Foveation-based Mechanisms Alleviate Adversarial Examples., 2016.
CBMM Memo No.
44
Poggio, T, V Chandrasekhar, J Petta, J Lin, A Veillard, and O Morere. Group Invariant Deep Representations for Image Instance Retrieval., 2016.
CBMM Memo No.
43
Liao, Q., JZ. Leibo, and T. Poggio. "How Important Is Weight Symmetry in Backpropagation?" In Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16). Phoenix, AZ., 2016.
Poggio, T., and F. Bach. "Introduction Special issue: Deep learning." Information and Inference 5 (2016): 103-104.
Mhaskar, H., Q. Liao, and T. Poggio. Learning Functions: When Is Deep Better Than Shallow”., 2016.
CBMM Memo No.
45
Liao, Q., and T. Poggio. Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex., 2016.
CBMM Memo No.
47
Poggio, T.. Deep Learning: mathematics and neuroscience- CBMM Views & Reviews., 2016.
Poggio, T., and O. Lewis. "Object and Scene Perception." In From Neuron to Cognition via Computational Neuroscience. Cambridge, MA, USA: The MIT Press, 2016.
Poggio, T., and F. Anselmi. Visual Cortex and Deep Networks: Learning Invariant Representations. Cambridge, MA, USA: The MIT Press, 2016.

2015

Poggio, T, F Anselmi, and L Rosasco. I-theory on depth vs width: hierarchical function composition., 2015.
CBMM Memo No.
41
Nickel, M., L. Rosasco, and T. Poggio. "Holographic Embeddings of Knowledge Graphs”., 2015.
CBMM Memo No.
39
Poggio, T, Q Liao, and JZ Leibo. How Important Is Weight Symmetry in Backpropagation?., 2015.
CBMM Memo No.
36
Poggio, T, L Rosasco, G Evangelopoulos, S Voinea, and C Zhang. "Discriminative Template Learning in Group-Convolutional Networks for Invariant Speech Representations." In INTERSPEECH 2015. Dresden, Germany, 2015.
Anselmi, F., L. Rosasco, C. Tan, and T. Poggio. Deep Convolutional Networks are Hierarchical Kernel Machines., 2015.
CBMM Memo No.
35
Anselmi, F., L. Rosasco, and T. Poggio. On Invariance and Selectivity in Representation Learning., 2015.
CBMM Memo No.
29
Nickel, M., Murphy. K., V. Tresp, and E. Gabrilovich. “A Review of Relational Machine Learning for Knowledge Graphs: From Multi-Relational Link Prediction to Automated Knowledge Graph Construction”., 2015.
CBMM Memo No.
28
Leibo, J.Z, Q. Liao, F. Anselmi, and T. Poggio. "“The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex”." PLOS Computational Biology, 11 (2015).
Poggio, T, S Voinea, and Y Mroueh. "Learning with Group Invariant Features: A Kernel Perspective." In NIPS 2015., 2015.
Poggio, T., Rosasco L., A. Shashua, N. Cohen, and F. Anselmi. Notes on Hierarchical Splines, DCLNs and i-theory., 2015.
CBMM Memo No.
37
Poggio, T.. What If...., 2015.
Koch, C., and T. Poggio. A Science of Intelligence., 2015.

2014

Anselmi, Fabio, and Tomaso Poggio. Representation Learning in Sensory Cortex: a theory.., 2014.
CBMM Memo No.
26
Poggio, T, L Rosasco, G Evangelopoulos, C Zhang, and S Voinea. "Speech Representations based on a Theory for Learning Invariances." In SANE 2014 - Speech and Audio in the Northeast., 2014.
Poggio, T, JZ Leibo, and Q Liao. Unsupervised learning of clutter-resistant visual representations from natural videos.., 2014.
CBMM Memo No.
23
Poggio, T, L Rosasco, C Zhang, S Voinea, and G Evangelopoulos. Learning An Invariant Speech Representation., 2014.
CBMM Memo No.
22
Poggio, T, and C Tan. Neural tuning size is a key factor underlying holistic face processing.., 2014.
CBMM Memo No.
21
Poggio, T, J Mutch, and L Isik. Computational role of eccentricity dependent cortical magnification.., 2014.
CBMM Memo No.
17
Zhang, C, C Frogner, M Araya-Polo, and D Hohl. "Machine-learning Based Automated Fault Detection in Seismic Traces ∗." In EAGE Conference and Exhibition 2014., 2014.
Zhang, C, G Evangelopoulos, S Voines, L Rosasco, and T Poggio. "A Deep Representation for Invariance And Music Classification." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing. Florence, Italy, 2014.
Leibo, JZ., Q. Liao, F. Anselmi, and T. Poggio. The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex., 2014.
CBMM Memo No.
4
Anselmi, F., J.Z. Leibo, L. Rosasco, J. Mutch, A. Tacchetti, and T. Poggio. "Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning?" Theoretical Computer Science (2014).
Liao, Q., JZ. Leibo, Y. Mroueh, and T. Poggio. Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines?., 2014.
CBMM Memo No.
3
Liao, Q., JZ. Leibo, and T. Poggio. "Learning invariant representations and applications to face verification." In NIPS 2013. Lake Tahoe, Nevada, 2014.
Leibo, JZ, Q Liao, and T Poggio. "Subtasks of Unconstrained Face Recognition." In 9th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. (VISAPP). Lisbon, Portugal, 2014.
Poggio, T, JZ Leibo, E Meyers, and L Isik. "The dynamics of invariant object recognition in the human visual system." Journal of Neurophysiology 111, no. 1 (2014).
Zhang, C, S Voinea, G Evangelopoulos, L Rosasco, and T Poggio. "Phone Classification by a Hierarchy of Invariant Representation Layers." In INTERSPEECH 2014 - 15th Annual Conf. of the International Speech Communication Association. Singapore, 2014.
Voinea, S., C. Zhang, G. Evangelopoulos, L. Rosasco, and T. Poggio. "Word-level Invariant Representations From Acoustic Waveforms." In INTERSPEECH 2014 - 15th Annual Conf. of the International Speech Communication Association., 2014.
Gao, T., D. Harari, J.B. Tenenbaum, and S. Ullman. “When Computer Vision Gazes at Cognition"., 2014.
CBMM Memo No.
25
Anselmi, F., and T. Poggio. Representation Learning in Sensory Cortex: a theory., 2014.
CBMM Memo No.
26
Poggio, T.. Is Research in Intelligence an Existential Risk?., 2014.

2013

Ni, Y, C Frogner, and T Poggio. Computer Science and Artificial Intelligence Laboratory Technical Report., 2013.
Poggio, T, J Mutch, F Anselmi, A tacchetti, L Rosasco, and JZ Leibo. Does invariant recognition predict tuning of neurons in sensory cortex?., 2013.
Kim, H, J Wohlwend, JZ Leibo, and T Poggio. Computer Science and Artificial Intelligence Laboratory Technical Report., 2013.
Meyers, E. "The neural decoding toolbox." (2013).
tacchetti, A, P Mallapragada, M Santoro, and L Rosasco. GURLS: a Least Squares Library for Supervised Learning., 2013.
Villa, S, L Rosasco, and T Poggio. On Learnability, Complexity and Stability., 2013.
Tan, C, and T Poggio. Faces as a "Model Category" for Visual Object Recognition., 2013.
Mroueh, Y, and L Rosasco. q-ary Compressive Sensing., 2013.
Anselmi, F, JZ Leibo, L Rosasco, J Mutch, A tacchetti, and T Poggio. Unsupervised Learning of Invariant Representations in Hierarchical Architectures., 2013.
Liao, Q, JZ Leibo, and T Poggio. Learning invariant representations and applications to face verification., 2013.
Poggio, T, and S Ullman. Vision: are models of object recognition catching up with the brain?., 2013.
Liao, Q, JZ Leibo, Y Mroueh, and T Poggio. Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines?., 2013.
Poggio, T, and T Serre. Models of visual cortex., 2013.
Anselmi, F, JZ Leibo, L Rosasco, J Mutch, A tacchetti, and T Poggio. Unsupervised Learning of Invariant Representations in Hierarchical Architectures., 2013.