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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2016

Krafft, P., Baker, C., Pentland, A. & Tenenbaum, J. B. Modeling Human Ad Hoc Coordination. AAAI (2016).

krafft_aaai2016.pdf (247.58 KB)

Morère, O., Veillard, A., Chandrasekhar, V. & Poggio, T. Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval. arXiv.org (2016). at <https://arxiv.org/abs/1603.04595>

1603.04595.pdf (2.9 MB)

Tan, C. & Poggio, T. Neural Tuning Size in a Model of Primate Visual Processing Accounts for Three Key Markers of Holistic Face Processing. Public Library of Science | PLoS ONE 1(3): e0150980, (2016).

journal.pone_.0150980.PDF (384.15 KB)

Lewis, O. & Poggio, T. From Neuron to Cognition via Computational Neuroscience (The MIT Press, 2016). at <https://mitpress.mit.edu/neuron-cognition>

Tang, H. et al. Predicting episodic memory formation for movie events. Scientific Reports (2016). doi:10.1038/srep30175

Tang, H. et al. Predicting episodic memory formation for movie events. Scientific Reports (2016). doi:10.1038/srep30175

Tang, H. et al. Predicting episodic memory formation for movie events [code]. (2016).

Tang, H. et al. Predicting episodic memory formation for movie events [code]. (2016).

Tang, H. et al. Predicting episodic memory formation for movie events [dataset]. (2016).

Tang, H. et al. Predicting episodic memory formation for movie events [dataset]. (2016).

Kosakowski, H. L., Powell, L. J. & Spelke, E. S. Preverbal Infants' Third-Party Imitator Preferences: Animated Displays versus Filmed Actors. International Conference on Infant Studies (ICIS) (2016).

Preverbal Infants' Third-Party Imitator Preferences: Animated Displays versus Filmed Actors (45.21 MB)

Peres, F., Smith, K. A. & Tenenbaum, J. B. Rapid Physical Predictions from Convolutional Neural Networks. Neural Information Processing Systems, Intuitive Physics Workshop (2016). at <http://phys.csail.mit.edu/papers/9.pdf>

Rapid Physical Predictions - NIPS Physics Workshop Poster (1.47 MB)

Tacchetti, A., Isik, L. & Poggio, T. Spatio-temporal convolutional networks explain neural representations of human actions. (2016).

Liao, Q., Kawaguchi, K. & Poggio, T. Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning. (2016).

CBMM-Memo-057.pdf (1.27 MB)

Poggio, T., Mhaskar, H., Rosasco, L., Miranda, B. & Liao, Q. Theory I: Why and When Can Deep Networks Avoid the Curse of Dimensionality?. (2016).

CBMM-Memo-058v1.pdf (2.42 MB)

CBMM-Memo-058v5.pdf (2.45 MB)

CBMM-Memo-058-v6.pdf (2.74 MB)

Proposition 4 has been deleted (2.75 MB)

N. Murty, A. Ratan & Pramod, R. T. To What Extent Does Global Shape Influence Category Representation in the Brain?. Journal of Neuroscience 36, 4149 - 4151 (2016).

Poggio, T. & Meyers, E. Turing++ Questions: A Test for the Science of (Human) Intelligence. AI Magazine 37 , 73-77 (2016).

Turing_Plus_Questions.pdf (424.91 KB)

Chen, Z., Grosmark, A. D., Penagos, H. & Wilson, M. A. Uncovering representations of sleep-associated hippocampal ensemble spike activity. Scientific Reports 6, (2016).

Hartshorne, J. K. VerbCorner: Testing theories of argument structure through crowdsourcing. Workshop on Events in Language (2016).

VerbCorner_EventsInLanguage.pdf (1.14 MB)

Leibo, J. Z., Liao, Q., Freiwald, W. A., Anselmi, F. & Poggio, T. View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation. (2016).

faceMirrorSymmetry_memo_ver01.pdf (3.93 MB)

Poggio, T. & Anselmi, F. Visual Cortex and Deep Networks: Learning Invariant Representations. 136 (The MIT Press, 2016). at <https://mitpress.mit.edu/books/visual-cortex-and-deep-networks>

2015

Hawrylycz, M. et al. Canonical genetic signatures of the adult human brain. Nature Neuroscience 18, 1844 (2015).

Preprint (40.28 MB)

Dillon, M. R., Pires, A. C., Hyde, D. C. & Spelke, E. S. Children's expectations about training the approximate number system. British Journal of Developmental Psychology 33, (2015).

Anselmi, F., Rosasco, L., Tan, C. & Poggio, T. Deep Convolutional Networks are Hierarchical Kernel Machines. (2015).

CBMM Memo 035_rev5.pdf (975.65 KB)

Zhang, C., Voinea, S., Evangelopoulos, G., Rosasco, L. & Poggio, T. Discriminative Template Learning in Group-Convolutional Networks for Invariant Speech Representations. INTERSPEECH-2015 (International Speech Communication Association (ISCA), 2015). at <http://www.isca-speech.org/archive/interspeech_2015/i15_3229.html>

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