Publications

CBMM Memos were established in 2014 as a mechanism for our center to share research results with the wider scientific community. Click here to read more about the memos and to see a full list of the memos.

Videos
Support Us

Publication

Search

Show only items where

Author

Type

Term

Year

Keyword

Found 230 results

Author Title Type [ Year

]

Filters: First Letter Of Last Name is L [Clear All Filters]

2017

Telenczuk, B. et al. Local field potentials primarily reflect inhibitory neuron activity in human and monkey cortex. Nature Scientific Reports (2017). doi:10.1038/srep40211

srep40211.pdf (2.53 MB)

Wu, J. et al. MarrNet: 3D Shape Reconstruction via 2.5D Sketches. Advances in Neural Information Processing Systems 30 540–550 (2017). at <http://papers.nips.cc/paper/6657-marrnet-3d-shape-reconstruction-via-25d-sketches.pdf>

MarrNet: 3D Shape Reconstruction via 2.5D Sketches (6.25 MB)

Zhang, C. et al. Musings on Deep Learning: Properties of SGD. (2017).

CBMM Memo 067 v2 (revised 7/19/2017) (5.88 MB)

CBMM Memo 067 v3 (revised 9/15/2017) (5.89 MB)

CBMM Memo 067 v4 (revised 12/26/2017) (5.57 MB)

Liao, Q. & Poggio, T. Object-Oriented Deep Learning. (2017).

CBMM-Memo-070.pdf (963.54 KB)

Manek, G. et al. Pruning Convolutional Neural Networks for Image Instance Retrieval. (2017). at <https://arxiv.org/abs/1707.05455>

1707.05455.pdf (143.46 KB)

Manek, G. et al. Pruning Convolutional Neural Networks for Image Instance Retrieval. (2017). at <https://arxiv.org/abs/1707.05455>

1707.05455.pdf (143.46 KB)

Arcaro, M. J., Schade, P. F., Vincent, J. L., Ponce, C. R. & Livingstone, M. S. Seeing faces is necessary for face-domain formation. Nature Neuroscience 5631628, (2017).

zhang, zhoutong et al. Shape and Material from Sound. Advances in Neural Information Processing Systems 30 1278–1288 (2017). at <http://papers.nips.cc/paper/6727-shape-and-material-from-sound.pdf>

zhang, zhoutong et al. Shape and Material from Sound. Advances in Neural Information Processing Systems 30 1278–1288 (2017). at <http://papers.nips.cc/paper/6727-shape-and-material-from-sound.pdf>

Liu, S. & Spelke, E. S. Six-month-old infants expect agents to minimize the cost of their actions. Cognition 160, 35-42 (2017).

Leavitt, M. L., Mendoza-Halliday, D. & J.C., M. - T. Sustained Activity Encoding Working Memories: Not Fully Distributed. Trends in Neurosciences 40 , 328-346 (2017).

Liu, S., Ullman, T. D., Tenenbaum, J. B. & Spelke, E. S. Ten-month-old infants infer the value of goals from the costs of actions. Science 358, 1038-1041 (2017).

ivc_full_preprint_withsm.pdf (1.6 MB)

Liu, S., Ullman, T., Tenenbaum, J. B. & Spelke, E. S. Ten-month-old infants infer value from effort. SRCD (2017).

Liu, S., Ullman, T., Tenenbaum, J. B. & Spelke, E. S. Ten-month-old infants infer value from effort. Society for Research in Child Development (2017).

Poggio, T. & Liao, Q. Theory II: Landscape of the Empirical Risk in Deep Learning. (2017).

CBMM Memo 066_1703.09833v2.pdf (5.56 MB)

Zhang, C. et al. Theory of Deep Learning IIb: Optimization Properties of SGD. (2017).

CBMM-Memo-072.pdf (3.66 MB)

Poggio, T. et al. Theory of Deep Learning III: explaining the non-overfitting puzzle. (2017).

CBMM-Memo-073.pdf (2.65 MB)

CBMM Memo 073 v2 (revised 1/15/2018) (2.81 MB)

CBMM Memo 073 v3 (revised 1/30/2018) (2.72 MB)

CBMM Memo 073 v4 (revised 12/30/2018) (575.72 KB)

Jing, L. et al. Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNN. 34th International Conference on Machine Learning 70, 1733-1741 (2017).

1612.05231.pdf (2.3 MB)

Landi, S. M. & Freiwald, W. A. Two areas for familiar face recognition in the primate brain. Science 357, 591 - 595 (2017).

591.full_.pdf (928.29 KB)

Leibo, J. Z., Liao, Q., Anselmi, F., Freiwald, W. A. & Poggio, T. View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation. Current Biology 27, 1-6 (2017).

Leibo, J. Z., Liao, Q., Anselmi, F., Freiwald, W. A. & Poggio, T. View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation. Current Biology 27, 1-6 (2017).

Mhaskar, H., Liao, Q. & Poggio, T. When and Why Are Deep Networks Better Than Shallow Ones?. AAAI-17: Thirty-First AAAI Conference on Artificial Intelligence (2017).

Poggio, T., Mhaskar, H., Rosasco, L., Miranda, B. & Liao, Q. Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review. International Journal of Automation and Computing 1-17 (2017). doi:10.1007/s11633-017-1054-2

art%3A10.1007%2Fs11633-017-1054-2.pdf (1.68 MB)

Lin, H. & Tegmark, M. Why does deep and cheap learning work so well?. Journal of Statistical Physics 168, 1223–1247 (2017).

1608.08225.pdf (2.14 MB)

2016

Linderman, S. W., Johnson, M. J., Wilson, M. A. & Chen, Z. A Bayesian nonparametric approach for uncovering rat hippocampal population codes during spatial navigation. Journal of Neuroscience Methods 263, (2016).

Journal of Neuroscience Methods (2.27 MB)

Pages