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 76 results

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2017

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).

2016

Kliemann, D., Jacoby, N., Anzellottti, S. & Saxe, R. Decoding task and stimulus representations in face-responsive cortex. Cognitive Neuropsychology (2016).

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)

Berzak, Y., Barbu, A., Harari, D., Katz, B. & Ullman, S. Do You See What I Mean? Visual Resolution of Linguistic Ambiguities. (2016).

memo-51.pdf (2.74 MB)

Wang, P. & Yuille, A. DOC: Deep OCclusion Recovering From A Single Image. ECCV (2016).

Dehghani, N. Dynamic balance of excitation and inhibition in human and monkey neocortex. Nature Scientific Reports (2016). doi:10.1038/srep23176

BalanceExcitationInhibition.pdf (2.1 MB)

2015

Kliemann, D., Jacoby, N., Anzellottti, S. & Saxe, R. Decoding task and stimulus representation in face-responsive cortex. (2015).

Madhavan, R. et al. Decrease in gamma-band activity tracks sequence learning. Frontiers in Systems Neuroscience 8, (2015).

fnsys-08-00222.pdf (5.62 MB)

Mao, J. et al. Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN). (2015).

CBMM Memo 033.pdf (839.42 KB)

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

CBMM Memo 035_rev5.pdf (975.65 KB)

Gershman, S. J., Tenenbaum, J. B. & Jaekel, F. Discovering hierarchical motion structure. Vision Research Available online 26 March 2015, (2015).

hierarchical_motion.pdf (582.01 KB)

Johnson, M. J., Linderman, S. W., Datta, S. R. & Adams, R. Discovering Switching Autoregressive Dynamics in Neural Spike Train Recordings. (2015).

cosyne2015b.pdf (7.27 MB)

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>

Berzak, Y., Barbu, A., Harari, D., Katz, B. & Ullman, S. Do You See What I Mean? Visual Resolution of Linguistic Ambiguities. Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal. (2015).

N. Murty, A. Ratan & Arun, S. P. Dynamics of 3D view invariance in monkey inferotemporal cortex. Journal of Neurophysiology 11319212373232821, 2180 - 2194 (2015).

2014

Rutishauser, U., Cerf, M. & Kreiman, G. Single Neuron Studies of the Brain: Probing Cognition (2014).

Zhang, C., Evangelopoulos, G., Voinea, S., Rosasco, L. & Poggio, T. A Deep Representation for Invariance And Music Classification. (2014).

CBMM-Memo-002.pdf (1.63 MB)

Zhang, C., Evangelopoulos, G., Voinea, S., Rosasco, L. & Poggio, T. A Deep Representation for Invariance and Music Classification. ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE, 2014). doi:10.1109/ICASSP.2014.6854954

Chen, X. et al. Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts. (2014).

CBMM-Memo-015.pdf (974.07 KB)

Isik, L., Meyers, E., Leibo, J. Z. & Poggio, T. The dynamics of invariant object recognition in the human visual system. (2014). doi:http://dx.doi.org/10.7910/DVN/KRUPXZ

Isik, L., Meyers, E., Leibo, J. Z. & Poggio, T. The dynamics of invariant object recognition in the human visual system. J Neurophysiol 111, 91-102 (2014).

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