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.

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Found 142 results

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2018

Banburski, A. et al. Theory III: Dynamics and Generalization in Deep Networks. (2018).

Original, intermediate versions are available under request (2.67 MB)

CBMM Memo 90 v12.pdf (4.74 MB)

Theory_III_ver44.pdf Update Hessian (4.12 MB)

Theory_III_ver48 (Updated discussion of convergence to max margin) (2.56 MB)

fixing errors and sharpening some proofs (2.45 MB)

2017

Meyers, E., Riley, M., Qi, X. - L. & Constantinidis, C. Differences in dynamic and static coding within different subdivision of the prefrontal cortex. Society for Neuroscience's Annual Meeting - SfN 2017 (2017). at <http://www.abstractsonline.com/pp8/#!/4376/presentation/4782>

Volokitin, A., Roig, G. & Poggio, T. Do Deep Neural Networks Suffer from Crowding?. (2017).

CBMM-Memo-069.pdf (6.47 MB)

Volokitin, A. & Roig, G. Do Deep Neural Networks Suffer from Crowding? [code]. (2017).

Roig, G., Chen, F., Boix, X. & Poggio, T. Eccentricity Dependent Deep Neural Networks for Modeling Human Vision. Vision Sciences Society (2017).

Chen, F., Roig, G., Isik, L., Boix, X. & Poggio, T. Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision. AAAI Spring Symposium Series, Science of Intelligence (2017). at <https://www.aaai.org/ocs/index.php/SSS/SSS17/paper/view/15360>

paper.pdf (963.87 KB)

Liang, T., Poggio, T., Rakhlin, A. & Stokes, J. Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. arXiv.org (2017). at <https://arxiv.org/abs/1711.01530>

1711.01530.pdf (966.99 KB)

Han, Y., Roig, G., Geiger, G. & Poggio, T. On the Human Visual System Invariance to Translation and Scale. Vision Sciences Society (2017).

Han, Y., Roig, G., Geiger, G. & Poggio, T. Is the Human Visual System Invariant to Translation and Scale?. AAAI Spring Symposium Series, Science of Intelligence (2017).

Mutch, J. et al. Computational and Cognitive Neuroscience of Vision 85-104 (Springer, 2017).

Dasgupta, I., Bernstein, J., Rolnick, D. & Sompolinsky, H. Markov transitions between attractor states in a recurrent neural network. AAAI (2017).

aaai-abstract (1).pdf (357.72 KB)

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)

Grossman, N. et al. Noninvasive Deep Brain Stimulation via Temporally Interfering Electric Fields. Cell 169, 1029 - 1041.e16 (2017).

Deen, B. et al. Organization of high-level visual cortex in human infants. Nature Communications (2017). doi:10.1038/ncomms13995

Golowich, N., Rakhlin, A. & Shamir, O. Size-Independent Sample Complexity of Neural Networks. (2017).

1712.06541.pdf (278.77 KB)

Anselmi, F., Evangelopoulos, G., Rosasco, L. & Poggio, T. Symmetry Regularization. (2017).

CBMM-Memo-063.pdf (6.1 MB)

Paul, R., Barbu, A., Felshin, S., Katz, B. & Roy, N. Temporal Grounding Graphs for Language Understanding with Accrued Visual-Linguistic Context. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI 2017) (2017). at <c>

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)

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)

2016

Berzak, Y., Reichart, R. & Katz, B. Contrastive Analysis with Predictive Power: Typology Driven Estimation of Grammatical Error Distributions in ESL. (2016).

memo-50.pdf (493.74 KB)

Luo, Y., Boix, X., Roig, G., Poggio, T. & Zhao, Q. Foveation-based Mechanisms Alleviate Adversarial Examples. (2016).

cbmm_memo_044.pdf (11.48 MB)

Nickel, M., Rosasco, L. & Poggio, T. Holographic Embeddings of Knowledge Graphs. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) (2016).

1510.04935v2.pdf (360.65 KB)

Anselmi, F., Rosasco, L. & Poggio, T. On invariance and selectivity in representation learning. Information and Inference: A Journal of the IMA iaw009 (2016). doi:10.1093/imaiai/iaw009

imaiai.iaw009.full_.pdf (267.87 KB)

Rockmore, D. Is it time for a presidential technoethics commission. (2016). at <https://theconversation.com/is-it-time-for-a-presidential-technoethics-commission-58846>

rockmore - Is it time for a presidential technoethics commission.pdf (280.2 KB)

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