All Publications

2017

N. Golowich, Rakhlin, A., and Shamir, O., “Size-Independent Sample Complexity of Neural Networks”, 2017.PDF icon 1712.06541.pdf (278.77 KB)
CBMM Related
Y. Han, Roig, G., Geiger, G., and Poggio, T., “On the Human Visual System Invariance to Translation and Scale”, Vision Sciences Society. 2017.
CBMM Funded
Y. Han, Roig, G., Geiger, G., and Poggio, T., “Is the Human Visual System Invariant to Translation and Scale?”, in AAAI Spring Symposium Series, Science of Intelligence, 2017.
CBMM Funded
H. Mhaskar, Liao, Q., and Poggio, T., “When and Why Are Deep Networks Better Than Shallow Ones?”, AAAI-17: Thirty-First AAAI Conference on Artificial Intelligence. 2017.
CBMM Funded
J. Mutch, Anselmi, F., Tacchetti, A., Rosasco, L., Leibo, J. Z., and Poggio, T., “Invariant Recognition Predicts Tuning of Neurons in Sensory Cortex”, in Computational and Cognitive Neuroscience of Vision, Springer, 2017, pp. 85-104.
CBMM Funded
G. Roig, Chen, F., Boix, X., and Poggio, T., “Eccentricity Dependent Deep Neural Networks for Modeling Human Vision”, Vision Sciences Society. 2017.
CBMM Funded
A. Tacchetti, Voinea, S., Evangelopoulos, G., and Poggio, T., “Representation Learning from Orbit Sets for One-shot Classification”, in AAAI Spring Symposium Series, Science of Intelligence, AAAI, 2017.
CBMM Funded
A. Tacchetti, Isik, L., and Poggio, T., “Invariant recognition drives neural representations of action sequences”, PLoS Comp. Bio, 2017.
CBMM Funded
D. Theurel, “Modeling brain dynamics using mathematics from quantum mechanics”, Peter Chin's Lab, Boston University, vol. Boston University. 2017.
CBMM Related
I. Yildirim and Janner, M., “Causal and compositional generative models in online perception”, in 39th Annual Conference of the Cognitive Science Society, London, UK, 2017.PDF icon yildirim_janner_2_1.pdf (6.88 MB)
CBMM Funded

2016

W. Lotter, Kreiman, G., and Cox, D., “Unsupervised Learning of Visual Structure using Predictive Generative Networks”, in International Conference on Learning Representations (ICLR), San Juan, Puerto Rico, 2016.
CBMM Funded
M. Nickel, Murphy, K., Tresp, V., and Gabrilovich, E., “A Review of Relational Machine Learning for Knowledge Graphs”, Proceedings of the IEEE, vol. 104, no. 1, pp. 11 - 33, 2016.PDF icon 1503.00759v3.pdf (1.53 MB)
CBMM Funded
Q. Liao, Leibo, J. Z., and Poggio, T., “How Important Is Weight Symmetry in Backpropagation?”, in Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), Phoenix, AZ., 2016.PDF icon liao-leibo-poggio.pdf (191.91 KB)
CBMM Funded
T. Poggio, “Deep Leaning: Mathematics and Neuroscience”, A Sponsored Supplement to Science, vol. Brain-Inspired intelligent robotics: The intersection of robotics and neuroscience, pp. 9-12, 2016.
CBMM Funded
CBMM Memo No.
058
T. Poggio, Mhaskar, H., Rosasco, L., Miranda, B., and Liao, Q., “Theory I: Why and When Can Deep Networks Avoid the Curse of Dimensionality?”. 2016.PDF icon CBMM-Memo-058v1.pdf (2.42 MB)PDF icon CBMM-Memo-058v5.pdf (2.45 MB)PDF icon CBMM-Memo-058-v6.pdf (2.74 MB)PDF icon Proposition 4 has been deleted (2.75 MB)
CBMM Funded
B. Katz and Barbu, A., “A look back at the June 2016 BMM Workshop in Sestri Levante, Italy”. 2016.PDF icon Sestri Levante Review (359.33 KB)
CBMM Funded
CBMM Memo No.
057
Q. Liao, Kawaguchi, K., and Poggio, T., “Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning”. 2016.PDF icon CBMM-Memo-057.pdf (1.27 MB)
CBMM Funded
CBMM Memo No.
056
I. Dasgupta, Schulz, E., and Gershman, S. J., “Where do hypotheses come from?”. 2016.PDF icon CBMM-Memo-056-v2.pdf (733.35 KB)
CBMM Funded
CBMM Memo No.
054
H. Mhaskar and Poggio, T., “Deep vs. shallow networks : An approximation theory perspective”. 2016.PDF icon Original submission, visit the link above for the updated version (960.27 KB)
CBMM Funded
CBMM Memo No.
049
J. Z. Leibo, Liao, Q., Freiwald, W. A., Anselmi, F., and Poggio, T., “View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation”. 2016.PDF icon faceMirrorSymmetry_memo_ver01.pdf (3.93 MB)
CBMM Funded
F. Anselmi, Rosasco, L., and Poggio, T., “On invariance and selectivity in representation learning”, Information and Inference: A Journal of the IMA, p. iaw009, 2016.PDF icon imaiai.iaw009.full_.pdf (267.87 KB)
CBMM Funded
CBMM Memo No.
047
Q. Liao and Poggio, T., “Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex”. 2016.PDF icon CBMM Memo No. 047 (1.29 MB)
CBMM Funded
CBMM Memo No.
046
B. M. Lake, Ullman, T., Tenenbaum, J. B., and Gershman, S. J., “Building machines that learn and think like people”. 2016.PDF icon machines_that_think.pdf (3.45 MB)
CBMM Funded
O. Morère, Veillard, A., Chandrasekhar, V., and Poggio, T., “Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval”, arXiv.org, 2016.PDF icon 1603.04595.pdf (2.9 MB)
CBMM Related
T. Poggio and Meyers, E., “Turing++ Questions: A Test for the Science of (Human) Intelligence.”, AI Magazine, vol. 37 , no. 1, pp. 73-77, 2016.PDF icon Turing_Plus_Questions.pdf (424.91 KB)
CBMM Funded

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