All Publications

2017

D. Mendoza-Halliday and Martinez-Trujillo, J., “Neuronal population coding of perceived and memorized visual features in the lateral prefrontal cortex”, Nature Communications, vol. 8, 2017.
CBMM Related
J. Jara-Ettinger, Floyd, S., Tenenbaum, J. B., and Schulz, L., “Children understand that agents maximize expected utilities.”, Journal of Experimental Psychology: General, vol. 146, no. 11, pp. 1574 - 1585, 2017.PDF icon ExpectedUtilities_Final.pdf (950.09 KB)
CBMM Related
A. Ratan N. Murty and Arun, S. P., “Effect of silhouetting and inversion on view invariance in the monkey inferotemporal cortex”, Journal of Neurophysiology, vol. 11823, no. 1, pp. 353 - 362, 2017.
CBMM Related
N. Grossman, Bono, D., Dedic, N., Kodandaramaiah, S. B., Rudenko, ii, A., Suk, H. - J., Cassara, A., Neufeld, E., Kuster, N., Tsai, L. - H., Pascual-Leone, A., and Boyden, E. S., “Noninvasive Deep Brain Stimulation via Temporally Interfering Electric Fields”, Cell, vol. 169, no. 6, pp. 1029 - 1041.e16, 2017.
CBMM Funded
A. Ratan N. Murty and Arun, S. P., “A Balanced Comparison of Object Invariances in Monkey IT Neurons”, eneuro, vol. 4, no. 2, pp. ENEURO.0333-16.2017, 2017.
CBMM Related
A. Ratan N. Murty and Arun, S. P., “Seeing a straight line on a curved surface: decoupling of patterns from surfaces by single IT neurons”, Journal of Neurophysiology, vol. 11773, no. 1, pp. 104 - 116, 2017.
CBMM Related
W. Kool, Gershman, S. J., and Cushman, F. A., “Cost-Benefit Arbitration Between Multiple Reinforcement-Learning Systems.”, Psychol Sci, vol. 28, no. 9, pp. 1321-1333, 2017.
CBMM Funded
B. M. Lake, Ullman, T. D., Tenenbaum, J. B., and Gershman, S. J., “Building machines that learn and think like people.”, Behavioral and Brain Sciences, vol. 40, p. e253, 2017.
CBMM Funded
E. Schulz, Tenenbaum, J. B., Duvenaud, D., Speekenbrink, M., and Gershman, S. J., “Compositional inductive biases in function learning.”, Cogn Psychol, vol. 99, pp. 44-79, 2017.
CBMM Funded
CBMM Memo No.
073
T. Poggio, Kawaguchi, K., Liao, Q., Miranda, B., Rosasco, L., Boix, X., Hidary, J., and Mhaskar, H., “Theory of Deep Learning III: explaining the non-overfitting puzzle”. 2017.PDF icon CBMM-Memo-073.pdf (2.65 MB)PDF icon CBMM Memo 073 v2 (revised 1/15/2018) (2.81 MB)PDF icon CBMM Memo 073 v3 (revised 1/30/2018) (2.72 MB)PDF icon CBMM Memo 073 v4 (revised 12/30/2018) (575.72 KB)
CBMM Funded
CBMM Memo No.
072
C. Zhang, Liao, Q., Rakhlin, A., Miranda, B., Golowich, N., and Poggio, T., “Theory of Deep Learning IIb: Optimization Properties of SGD”. 2017.PDF icon CBMM-Memo-072.pdf (3.66 MB)
CBMM Funded
CBMM Memo No.
071
F. Cano-Córdoba, Sarma, S., and Subirana, B., “Theory of Intelligence with Forgetting: Mathematical Theorems Explaining Human Universal Forgetting using “Forgetting Neural Networks””. 2017.PDF icon CBMM-Memo-071.pdf (2.54 MB)
CBMM Funded
M. Janner, Wu, J., Kulkarni, T., Yildirim, I., and Tenenbaum, J. B., “Self-supervised intrinsic image decomposition.”, in Annual Conference on Neural Information Processing Systems (NIPS), Long Beach, CA, 2017.PDF icon intrinsicImg_nips_2017.pdf (5.87 MB)
CBMM Funded
A. Tacchetti, Isik, L., and Poggio, T., “Invariant recognition drives neural representations of action sequences”, PLOS Computational Biology, vol. 13, no. 12, p. e1005859, 2017.PDF icon journal.pcbi_.1005859.pdf (9.24 MB)
CBMM Funded
J. Wu, Lu, E., Kohli, P., Freeman, W. T., and Tenenbaum, J. B., “Learning to See Physics via Visual De-animation”, Advances in Neural Information Processing Systems 30. pp. 152–163, 2017.PDF icon Learning to See Physics via Visual De-animation (1.11 MB)
CBMM Funded
J. Wu, Wang, Y., Xue, T., Sun, X., Freeman, W. T., and Tenenbaum, J. B., “MarrNet: 3D Shape Reconstruction via 2.5D Sketches”, Advances in Neural Information Processing Systems 30. Curran Associates, Inc., Long Beach, CA, pp. 540–550, 2017.PDF icon MarrNet: 3D Shape Reconstruction via 2.5D Sketches (6.25 MB)
CBMM Funded
zhoutong zhang, Li, Q., Huang, Z., Wu, J., Tenenbaum, J. B., and Freeman, W. T., “Shape and Material from Sound”, Advances in Neural Information Processing Systems 30. Long Beach, CA, pp. 1278–1288, 2017.
CBMM Funded
L. Isik, Tacchetti, A., and Poggio, T., “A fast, invariant representation for human action in the visual system.”, J Neurophysiol, p. jn.00642.2017, 2017.PDF icon Author's last draft (695.63 KB)
CBMM Funded
T. Liang, Poggio, T., Rakhlin, A., and Stokes, J., “Fisher-Rao Metric, Geometry, and Complexity of Neural Networks”, 2017.PDF icon 1711.01530.pdf (966.99 KB)
CBMM Related
S. Liu, Ullman, T. D., Tenenbaum, J. B., and Spelke, E. S., “Ten-month-old infants infer the value of goals from the costs of actions”, Science, vol. 358, pp. 1038-1041, 2017.PDF icon ivc_full_preprint_withsm.pdf (1.6 MB)
CBMM Funded
E. Meyers, Riley, M., Qi, X. - L., and Constantinidis, C., “Differences in dynamic and static coding within different subdivision of the prefrontal cortex”, Society for Neuroscience's Annual Meeting - SfN 2017. Washington, DC, 2017.
CBMM Funded
A. Tacchetti, Isik, L., and Poggio, T., “Invariant action recognition dataset”. 2017.
CBMM Funded
CBMM Memo No.
080
W. Shen, Wang, B., Jiang, Y., Wang, Y., and Yuille, A., “Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection”. 2017.PDF icon CBMM-Memo-080.pdf (2.51 MB)
CBMM Funded
CBMM Memo No.
070
Q. Liao and Poggio, T., “Object-Oriented Deep Learning”. 2017.PDF icon CBMM-Memo-070.pdf (963.54 KB)
CBMM Funded
T. Gerstenberg, Peterson, M. F., Goodman, N. D., Lagnado, D. A., and Tenenbaum, J. B., “Eye-Tracking Causality”, Psychological Science, vol. 73, 2017.
CBMM Funded

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