All Publications

2016

C. Tan and 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 , vol. 1(3): e0150980, 2016.PDF icon journal.pone_.0150980.PDF (384.15 KB)
CBMM Funded
W. Mlynarski and McDermott, J. H., “Learning mid-level codes for natural sounds”, Computational and Systems Neuroscience (Cosyne) 2016. Salt Lake City, UT, 2016.PDF icon Wiktor_COSYNE_2015_hierarchy_final.pdf (2.52 MB)
CBMM Funded
CBMM Memo No.
044
Y. Luo, Boix, X., Roig, G., Poggio, T., and Zhao, Q., “Foveation-based Mechanisms Alleviate Adversarial Examples”. 2016.PDF icon cbmm_memo_044.pdf (11.48 MB)
CBMM Funded
CBMM Memo No.
043
O. Morère, Veillard, A., Lin, J., Petta, J., Chandrasekhar, V., and Poggio, T., “Group Invariant Deep Representations for Image Instance Retrieval”. 2016.PDF icon CBMM-Memo-043.pdf (2.66 MB)
CBMM Funded
CBMM Memo No.
042
L. Isik, Tacchetti, A., and Poggio, T., “Fast, invariant representation for human action in the visual system”. 2016.PDF icon CBMM Memo 042 (3.03 MB)
CBMM Funded
H. Mhaskar and Poggio, T., “Deep vs. shallow networks: An approximation theory perspective”, Analysis and Applications, vol. 14, no. 06, pp. 829 - 848, 2016.
CBMM Funded
CBMM Memo No.
045
H. Mhaskar, Liao, Q., and Poggio, T., “Learning Functions: When Is Deep Better Than Shallow”. 2016.
CBMM Funded
F. Bach and Poggio, T., “Introduction Special issue: Deep learning”, Information and Inference, vol. 5, pp. 103-104, 2016.
CBMM Funded
Q. Liao, Leibo, J. Z., and Poggio, T., “How Important Is Weight Symmetry in Backpropagation?”, in Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), Phoenix, AZ., 2016.
CBMM Funded
M. Nickel, Rosasco, L., and Poggio, T., “Holographic Embeddings of Knowledge Graphs”, in Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), Phoenix, Arizona, USA, 2016.PDF icon 1510.04935v2.pdf (360.65 KB)
CBMM Funded
M. F. Peterson, Lin, J., Zaun, I., and Kanwisher, N., “Individual differences in face-looking behavior generalize from the lab to the world.”, Journal of Vision, 2016.
CBMM Funded
A. Tacchetti, Isik, L., and Poggio, T., “Spatio-temporal convolutional networks explain neural representations of human actions”, 2016.
CBMM Funded
O. Lewis and Poggio, T., “Object and Scene Perception”, in From Neuron to Cognition via Computational Neuroscience, Cambridge, MA, USA: The MIT Press, 2016.
CBMM Funded

2015

CBMM Memo No.
041
T. Poggio, Anselmi, F., and Rosasco, L., “I-theory on depth vs width: hierarchical function composition”. 2015.PDF icon cbmm_memo_041.pdf (1.18 MB)
CBMM Funded
K. Ellis and Lewis, O., “Metareasoning in Symbolic Domains”, in NIPS Workshop | Bounded Optimality and Rational Metareasoning, 2015.PDF icon metareasoning_submitted.pdf (491.95 KB)
CBMM Funded
Z. Ren, Wang, C., and Yuille, A., “Scene-Domain Active Part Models for Object Representation”, IEEE International Conference on Computer Vision (ICCV) . Santiago, Chile, pp. 2497 - 2505 , 2015.PDF icon Ren_ICCV15.pdf (3.37 MB)
CBMM Funded
CBMM Memo No.
036
Q. Liao, Leibo, J. Z., and Poggio, T., “How Important is Weight Symmetry in Backpropagation?”. 2015.PDF icon 1510.05067v3.pdf (615.32 KB)
CBMM Funded
CBMM Memo No.
039
M. Nickel, Rosasco, L., and Poggio, T., “Holographic Embeddings of Knowledge Graphs”. 2015.PDF icon holographic-embeddings.pdf (677.87 KB)
CBMM Funded
J. Z. Leibo, Liao, Q., Anselmi, F., and Poggio, T., “The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex”, PLOS Computational Biology, vol. 11, no. 10, p. e1004390, 2015.PDF icon journal.pcbi_.1004390.pdf (2.04 MB)
CBMM Funded
C. Zhang, Voinea, S., Evangelopoulos, G., Rosasco, L., and Poggio, T., “Discriminative Template Learning in Group-Convolutional Networks for Invariant Speech Representations”, in INTERSPEECH-2015, Dresden, Germany, 2015.
CBMM Funded
T. Gerstenberg, Goodman, N. D., Lagnado, D. A., and Tenenbaum, J. B., “How, whether, why: Causal judgments as counterfactual contrasts”, Annual Meeting of the Cognitive Science Society (CogSci). Pasadena, CA, pp. 782-787, 2015.PDF icon GerstenbergEtAl2015-Cogsci.pdf (2.16 MB)
CBMM Funded
J. Z. Leibo, Liao, Q., Anselmi, F., and Poggio, T., “The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex”. 2015.Binary Data modularity_dataset_ver1.tar.gz (36.14 MB)
CBMM Funded
F. Anselmi, Leibo, J. Z., Rosasco, L., Mutch, J., Tacchetti, A., and Poggio, T., “Unsupervised learning of invariant representations”, Theoretical Computer Science, 2015.
CBMM Related
CBMM Memo No.
035
F. Anselmi, Rosasco, L., Tan, C., and Poggio, T., “Deep Convolutional Networks are Hierarchical Kernel Machines”. 2015.PDF icon CBMM Memo 035_rev5.pdf (975.65 KB)
CBMM Funded
CBMM Memo No.
031
A. Yuille and Mottaghi, R., “Complexity of Representation and Inference in Compositional Models with Part Sharing”. 2015.PDF icon CBMM Memo 031.pdf (1.14 MB)
CBMM Funded

Pages