All Publications

2015

E. Meyers, “How PFC and LIP process single and multiple-object ‘pop-out’ displays”, Society for Neuroscience. 2015.
CBMM Funded
Y. Mroueh, Voinea, S., and Poggio, T., “Learning with Group Invariant Features: A Kernel Perspective”, in NIPS 2015, 2015.PDF icon LearningInvarianceKernel_NIPS2015.pdf (292.18 KB)
CBMM Related
L. J. Powell, Deen, B., Guo, L., and Saxe, R., “Using fNIRS to Map Functional Specificity in the Infant Brain: An fROI Approach”. 2015.PDF icon SRCD2015_NIRS_poster.pdf (2.14 MB)
CBMM Related
L. Rosasco and Villa, S., “Learning with incremental iterative regularization”, in NIPS 2015, 2015.PDF icon Learning with Incremental Iterative Regularization_1405.0042v2.pdf (504.66 KB)
CBMM Related
A. Rudi, Camoriano, R., and Rosasco, L., “Less is More: Nyström Computational Regularization”, in NIPS 2015, 2015.PDF icon Less is More- Nystr ̈om Computational Regularization_1507.04717v4.pdf (287.14 KB)
CBMM Funded
K. Scott, “Moving the lab home: validation of a web-based system for developmental studies”. 2015.
CBMM Related
A. Tacchetti, Isik, L., and Poggio, T., “Invariant representations for action recognition in the visual system.”, Vision Sciences Society, vol. 15, no. 12. Journal of vision, 2015.
CBMM Funded
J. Wang and Yuille, A., “Semantic Part Segmentation using Compositional Model combing Shape and Appearance”, in CVPR, 2015.PDF icon JianyuWangSemanticCVPR2015 (1).pdf (6.15 MB)
CBMM Funded
A. Wong and Yuille, A., “One Shot Learning via Compositions of Meaningful Patches”, in International Conference on Computer Vision (ICCV), 2015.PDF icon AlexWongOneShotCVPR2015.pdf (1.83 MB)
CBMM Funded
J. Wu, Yildirim, I., Lim, J. J., Freeman, W. T., and Tenenbaum, J. B., “Galileo: Perceiving physical object properties by integrating a physics engine with deep learning.”, in NIPS 2015, Montréal, Canada, 2015.
CBMM Funded
I. Yildirim, Kulkarni, T., Freiwald, W. A., and Tenenbaum, J. B., “Efficient and robust analysis-by-synthesis in vision: A computational framework, behavioral tests, and modeling neuronal representations”, in Annual Conference of the Cognitive Science Society, 2015.PDF icon yildirimetal_cogsci15.pdf (3.22 MB)
CBMM Funded
P. Tsividis, Gershman, S. J., Tenenbaum, J. B., and Schulz, L., “Information Selection in Noisy Environments with Large Action Spaces”, 9th Biennial Conference of the Cognitive Development Society, vol. Columbus, OH. 2015.
CBMM Funded
H. Yu, Siddharth, N., Barbu, A., and Siskind, J. Mark, “A Compositional Framework for Grounding Language Inference, Generation, and Acquisition in Video”. 2015.
CBMM Funded

2014

J. Singer and Kreiman, G., “Short temporal asynchrony disrupts visual object recognition.”, J Vis, vol. 14, no. 5, p. 7, 2014.
CBMM Related
CBMM Memo No.
025
T. Gao, Harari, D., Tenenbaum, J. B., and Ullman, S., “When Computer Vision Gazes at Cognition.”. 2014.PDF icon CBMM-Memo-025.pdf (3.78 MB)
CBMM Funded
S. W. Linderman, Stock, C., and Adams, R., “A framework for studying synaptic plasticity with neural spike train data”, in Neural Information Processing Systems, 2014.PDF icon 5274-a-framework-for-studying-synaptic-plasticity-with-neural-spike-train-data.pdf (4.6 MB)
CBMM Related
T. Poggio, “Is Research in Intelligence an Existential Risk?”. 2014.PDF icon Is Research in Intelligence an Existential Risk.pdf (571.42 KB)
CBMM Funded
CBMM Memo No.
027
S. W. Linderman, Johnson, M. J., Wilson, M. A., and Chen, Z., “A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation.”. 2014.PDF icon CBMM-Memo-027.pdf (9.44 MB)
CBMM Funded
CBMM Memo No.
026
F. Anselmi and Poggio, T., “Representation Learning in Sensory Cortex: a theory.”. 2014.PDF icon CBMM-Memo-026_neuron_ver45.pdf (1.35 MB)
CBMM Funded
M. R. Dillon and Spelke, E. S., “Core geometry in perspective”, Developmental Science, 2014.
CBMM Related
S. Voinea, Zhang, C., Evangelopoulos, G., Rosasco, L., and Poggio, T., “Speech Representations based on a Theory for Learning Invariances”. SANE 2014 - Speech and Audio in the Northeast, 2014.
CBMM Related
CBMM Memo No.
024
N. Amir, Besold, T. R., Camoriano, R., Erdogan, G., Flynn, T., Gillary, G., Gomez, J., Herbert-Voss, A., Hotan, G., Kadmon, J., Linderman, S. W., Liu, T. T., Marantan, A., Olson, J., Orchard, G., Pal, D. K., Pasquale, G., Sanders, H., Silberer, C., Smith, K. A., de Briton, C. Stein N., Suchow, J. W., Tessler, M. H., Viejo, G., Walker, D., Wehbe, L., Barbu, A., Isik, L., Mackevicius, E., and Meroz, Y., “Abstracts of the 2014 Brains, Minds, and Machines Summer Course”. 2014.PDF icon CBMM-Memo-024.pdf (2.86 MB)
CBMM Funded
CBMM Memo No.
023
Q. Liao, Leibo, J. Z., and Poggio, T., “Unsupervised learning of clutter-resistant visual representations from natural videos.”. 2014.PDF icon 1409.3879v2.pdf (3.64 MB)
CBMM Funded
CBMM Memo No.
012
A. Barbu, Barrett, D., Chen, W., Siddharth, N., Xiong, C., Corso, J. J., Fellbaum, C. D., Hanson, C., Hanson, S. José, Helie, S., Malaia, E., Pearlmutter, B. A., Siskind, J. Mark, Talavage, T. Michael, and Wilbur, R. B., “Seeing is Worse than Believing: Reading People’s Minds Better than Computer-Vision Methods Recognize Actions”. 2014.PDF icon CBMM Memo 012.pdf (678.95 KB)
CBMM Funded
D. Mendoza-Halliday, Torres, S., and Martinez-Trujillo, J., “Sharp emergence of feature-selective sustained activity along the dorsal visual pathway”, Nature Neuroscience, vol. 7, no. 9, 2014.
CBMM Related

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