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L. Bergen, Levy, R., and Goodman, N., “Pragmatic Reasoning through Semantic Inference”, Semantics and Pragmatics, vol. Vol 9 (2016) , In Press.PDF icon BergenLevyGoodman2015.pdf (1.12 MB)
CBMM Related
S. Gershman, Tenenbaum, J. B., and Jaekel, F., “Discovering hierarchical motion structure”, Vision Research, vol. Available online 26 March 2015, In Press.PDF icon hierarchical_motion.pdf (582.01 KB)
CBMM Funded
T. Gerstenberg and Tenenbaum, J., “Intuitive theories”, in Oxford Handbook of Causal Reasoning, Oxford University Press, In Press.PDF icon Intuitive Theories (Gerstenberg, Tenenbaum, 2016.pdf (6.06 MB)
CBMM Funded
H. Tang, Kreiman, G., and Zhao, Q., “Recognition of occluded objects”, in Computational and Cognitive Neuroscience of Vision, Springer Singapore, In Press.
CBMM Funded
CBMM Memo No.
057
Q. Liao, Kawaguchi, K., and Poggio, T., “Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning”. 2016.PDF icon CBMM-Memo-057.pdf (1.27 MB)
CBMM Funded
CBMM Memo No.
055
Y. Berzak, Huang, Y., Barbu, A., Korhonen, A., and Katz, B., “Anchoring and Agreement in Syntactic Annotations”. 2016.PDF icon CBMM-Memo-055.pdf (768.54 KB)
CBMM Related
CBMM Memo No.
054
H. Mhaskar and Poggio, T., “Deep vs. shallow networks : An approximation theory perspective”. 2016.PDF icon CBMM-Memo-054.pdf (960.27 KB)
CBMM Funded
CBMM Memo No.
053
G. Dehaene-Lambertz and Spelke, E. S., “The infancy of the human brain”. 2016.PDF icon CBMM-Memo-053.pdf (1.51 MB)
CBMM Funded
CBMM Memo No.
052
Y. Berzak, Kenney, J., Spadine, C., Wang, J. Xian, Lam, L., Mori, K. Sophie, Garza, S., and Katz, B., “Universal Dependencies for Learner English”. 2016.PDF icon memo-52_rev1.pdf (472.67 KB)
CBMM Funded
CBMM Memo No.
051
Y. Berzak, Barbu, A., Harari, D., Katz, B., and Ullman, S., “Do You See What I Mean? Visual Resolution of Linguistic Ambiguities”. 2016.PDF icon memo-51.pdf (2.74 MB)
CBMM Funded
CBMM Memo No.
050
Y. Berzak, Reichart, R., and Katz, B., “Contrastive Analysis with Predictive Power: Typology Driven Estimation of Grammatical Error Distributions in ESL”. 2016.PDF icon memo-50.pdf (493.74 KB)
CBMM Funded
CBMM Memo No.
049
J. Z. Leibo, Liao, Q., Freiwald, W., Anselmi, F., and Poggio, T., “View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation”. 2016.PDF icon faceMirrorSymmetry_memo_ver01.pdf (3.93 MB)
CBMM Funded
CBMM Memo No.
048
E. Schulz, Tenenbaum, J. B., Duvenaud, D., Speekenbrink, M., and Gershman, S. J., “Probing the compositionality of intuitive functions”. 2016.PDF icon CBMM-Memo-048.pdf (815.72 KB)
CBMM Related
CBMM Memo No.
047
Q. Liao and Poggio, T., “Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex”. 2016.PDF icon CBMM Memo No. 047 (1.29 MB)
CBMM Funded
CBMM Memo No.
046
B. M. Lake, Ullman, T. D., Tenenbaum, J. B., and Gershman, S. J., “Building machines that learn and think like people”. 2016.PDF icon machines_that_think.pdf (3.45 MB)
CBMM Funded
CBMM Memo No.
045
H. Mhaskar, Liao, Q., and Poggio, T., “ Learning Functions: When Is Deep Better Than Shallow”. 2016.
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
W. Lotter, Kreiman, G., and Cox, D., “Unsupervised Learning of Visual Structure using Predictive Generative Networks”, in International Conference on Learning Representations (ICLR), San Juan, Puerto Rico, 2016.
CBMM Funded
M. Nickel, Murphy, K., Tresp, V., and Gabrilovich, E., “A Review of Relational Machine Learning for Knowledge Graphs”, Proceedings of the IEEE, vol. 104, no. 1, pp. 11 - 33, 2016.PDF icon 1503.00759v3.pdf (1.53 MB)
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.PDF icon liao-leibo-poggio.pdf (191.91 KB)
CBMM Funded
T. Poggio and Anselmi, F., Visual Cortex and Deep Networks: Learning Invariant Representations. Cambridge, MA, USA: The MIT Press, 2016, p. 136.
CBMM Funded
C. E. Robertson, Hermann, K. L., Mynick, A., Kravitz, D. J., and Kanwisher, N., “Neural Representations Integrate the Current Field of View with the Remembered 360° Panorama”, Current Biology, 2016.
CBMM Funded
Z. Chen, Grosmark, A. D., Penagos, H., and Wilson, M. A., “Uncovering representations of sleep-associated hippocampal ensemble spike activity”, Scientific Reports, vol. 6, 2016.
CBMM Funded