All Publications

Submitted

L. Jing, Shen, Y., Dubček, T., Peurifoi, J., Skirlo, S., LeCun, Y., Tegmark, M., and Soljačić, M., “Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNN”. Submitted.PDF icon 1612.05231.pdf (2.3 MB)
CBMM Related
H. W. Lin and Tegmark, M., “Why does deep and cheap learning work so well?”, arXiv preprint arXiv:1608.08225, Submitted.PDF icon 1608.08225.pdf (2.14 MB)
CBMM Related

2017

CBMM Memo No.
069
A. Volokitin, Roig, G., and Poggio, T., “Do Deep Neural Networks Suffer from Crowding?”. 2017.PDF icon CBMM-Memo-069.pdf (6.47 MB)
CBMM Funded
CBMM Memo No.
068
B. Subirana, Bagiati, A., and Sarma, S., “On the Forgetting of College Academics: at "Ebbinghaus Speed"?”. 2017.PDF icon CBMM Memo 068-On Forgetting - June 18th 2017 v2.pdf (713.7 KB)
CBMM Funded
A. Volokitin and Roig, G., “Do Deep Neural Networks Suffer from Crowding? [code]”. 2017.
CBMM Funded
CBMM Memo No.
063
F. Anselmi, Evangelopoulos, G., Rosasco, L., and Poggio, T., “Symmetry Regularization”. 2017.PDF icon CBMM-Memo-063.pdf (6.1 MB)
CBMM Funded
CBMM Memo No.
067
C. Zhang, Liao, Q., Rakhlin, A., Sridharan, K., Miranda, B., Golowich, N., and Poggio, T., “Theory of Deep Learning III: Generalization Properties of SGD”. 2017.PDF icon CBMM Memo 067 v2 (revised 7/19/2017) (5.88 MB)PDF icon CBMM Memo 067 v3 (revised 9/15/2017) (5.89 MB)
CBMM Funded
CBMM Memo No.
066
T. Poggio and Liao, Q., “Theory II: Landscape of the Empirical Risk in Deep Learning”. 2017.PDF icon CBMM Memo 066_1703.09833v2.pdf (5.56 MB)
CBMM Funded
CBMM Memo No.
062
A. Tacchetti, Voinea, S., and Evangelopoulos, G., “Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets”. 2017.PDF icon CBMM-Memo-062.pdf (9.37 MB)
CBMM Funded
T. Poggio, Mhaskar, H., Rosasco, L., Miranda, B., and Liao, Q., “Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review”, International Journal of Automation and Computing, pp. 1-17, 2017.PDF icon art%3A10.1007%2Fs11633-017-1054-2.pdf (1.68 MB)
CBMM Funded
J. Z. Leibo, Liao, Q., Anselmi, F., Freiwald, W. A., and Poggio, T., “View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation”, Current Biology, vol. 27, pp. 1-6, 2017.
CBMM Funded
F. Chen, Roig, G., Isik, L., Boix, X., and Poggio, T., “Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision”, in AAAI Spring Symposium Series, Science of Intelligence, 2017.PDF icon paper.pdf (963.87 KB)
CBMM Funded
N. Dehghani, “Design of the Artificial: lessons from the biological roots of general intelligence”, 2017.PDF icon DesignArtificial_Dehghani_arXiv.pdf (222.47 KB)
CBMM Related
G. A del Molino, Boix, X., Lim, J., and Tan, A., “Active Video Summarization: Customized Summaries via On-line Interaction.”, in AAAI Conference on Artificial Intelligence, 2017.PDF icon 21-Garcia-del-Molino-14856.pdf (413.77 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
D. Theurel, “Modeling brain dynamics using mathematics from quantum mechanics”, Peter Chin's Lab, Boston University, vol. Boston University. 2017.
CBMM Related

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