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 285 results

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2015

Tacchetti, A., Isik, L. & Poggio, T. Invariant representations for action recognition in the visual system. Vision Sciences Society 15, (2015).

Isik, L., Tacchetti, A. & Poggio, T. Invariant representations for action recognition in the visual system. Computational and Systems Neuroscience (2015).

Poggio, T., Anselmi, F. & Rosasco, L. I-theory on depth vs width: hierarchical function composition. (2015).

cbmm_memo_041.pdf (1.18 MB)

Frogner, C., Zhang, C., Mobahi, H., Araya-Polo, M. & Poggio, T. Learning with a Wasserstein Loss. Advances in Neural Information Processing Systems (NIPS 2015) 28 (2015). at <http://arxiv.org/abs/1506.05439>

Learning with a Wasserstein Loss_1506.05439v2.pdf (2.57 MB)

Mroueh, Y., Voinea, S. & Poggio, T. Learning with Group Invariant Features: A Kernel Perspective. NIPS 2015 (2015). at <https://papers.nips.cc/paper/5798-learning-with-group-invariant-features-a-kernel-perspective>

LearningInvarianceKernel_NIPS2015.pdf (292.18 KB)

Poggio, T., Rosasco, L., Shashua, A., Cohen, N. & Anselmi, F. Notes on Hierarchical Splines, DCLNs and i-theory. (2015).

CBMM Memo 037 (1.83 MB)

Newman, J. P. et al. Optogenetic feedback control of neural activity. Elife 4, e07192 (2015).

elife-07192-v1-download.pdf (5.92 MB)

Buice, M. & de Vries, S. Population Coding, Correlations, and Functional Connectivity in the mouse visual system with the Cortical Activity Map (CAM). Society for Neuroscience 2015 (2015).

2015 SFN Population_Coding.pdf (2.94 MB)

Buice, M. & de Vries, S. Population Coding, Correlations, and Functional Connectivity in the mouse visual system with the Cortical Activity Map (CAM). Society for Neuroscience 2015 (2015).

2015 SFN Population_Coding.pdf (2.94 MB)

Kosakowski, H. L., Powell, L. J. & Spelke, E. S. Preverbal Infants' Third-Party Imitator Preferences: Animated Displays versus Filmed Actors. CBMM Summer Research Program (2015).

Preverbal Infants' Third-Party Imitator Preferences: Animated Displays versus Filmed Actors (46.32 MB)

Koch, C. & Poggio, T. A Science of Intelligence . (2015).

A Science of Intelligence.pdf (659.5 KB)

Anselmi, F. et al. Unsupervised learning of invariant representations. Theoretical Computer Science (2015). doi:10.1016/j.tcs.2015.06.048

Powell, L. J., Deen, B., Guo, L. & Saxe, R. Using fNIRS to Map Functional Specificity in the Infant Brain: An fROI Approach. (2015).

SRCD2015_NIRS_poster.pdf (2.14 MB)

Poggio, T. What if.. (2015).

What if.pdf (2.09 MB)

2016

Liao, Q. & Poggio, T. Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex. (2016).

CBMM Memo No. 047 (1.29 MB)

Poggio, T. Deep Leaning: Mathematics and Neuroscience. A Sponsored Supplement to Science Brain-Inspired intelligent robotics: The intersection of robotics and neuroscience, 9-12 (2016).

Poggio, T. Deep Learning: mathematics and neuroscience. (2016).

Deep Learning- mathematics and neuroscience.pdf (1.25 MB)

Mhaskar, H. & Poggio, T. Deep vs. shallow networks: An approximation theory perspective. Analysis and Applications 14, 829 - 848 (2016).

Mhaskar, H. & Poggio, T. Deep vs. shallow networks : An approximation theory perspective. (2016).

Original submission, visit the link above for the updated version (960.27 KB)

Isik, L., Tacchetti, A. & Poggio, T. Fast, invariant representation for human action in the visual system. (2016). at <http://arxiv.org/abs/1601.01358>

CBMM Memo 042 (3.03 MB)

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

cbmm_memo_044.pdf (11.48 MB)

Morère, O. et al. Group Invariant Deep Representations for Image Instance Retrieval. (2016).

CBMM-Memo-043.pdf (2.66 MB)

Morère, O. et al. Group Invariant Deep Representations for Image Instance Retrieval. (2016).

CBMM-Memo-043.pdf (2.66 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)

Liao, Q., Leibo, J. Z. & Poggio, T. How Important Is Weight Symmetry in Backpropagation?. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) (Association for the Advancement of Artificial Intelligence, 2016).

liao-leibo-poggio.pdf (191.91 KB)

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