All Publications

Submitted

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.
070
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
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
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