All Publications

2018

Fde Avila Belbute-Peres, Smith, K. A., Allen, K., Tenenbaum, J. B., and Kolter, Z., “End-to-end differentiable physics for learning and control”, Advances in Neural Information Processing Systems 31 (NIPS 2018). 2018.PDF icon 7948-end-to-end-differentiable-physics-for-learning-and-control.pdf (794.17 KB)
N. Dehghani and Wimmer, R., “A computational perspective of the role of Thalamus in cognition”, arxiv, 2018.PDF icon ThalamusComputationArxiv.pdf (5.12 MB)
CBMM Related
L. Isik, Tacchetti, A., and Poggio, T., “A fast, invariant representation for human action in the visual system”, Journal of Neurophysiology, 2018.
CBMM Funded
L. Isik and Tacchetti, A., “MEG action recognition data”. 2018.
CBMM Funded
O. Lewis, “Structured learning and inference with neural networks and generative models”, 2018.
CBMM Funded
W. Mlynarski and McDermott, J. H., “Co-occurrence statistics of natural sound features predict perceptual grouping”, Computational and Systems Neuroscience (Cosyne) 2018. 2018.
CBMM Funded
T. Poggio and Liao, Q., “Theory I: Deep networks and the curse of dimensionality”, Bulletin of the Polish Academy of Sciences: Technical Sciences, vol. 66, no. 6, 2018.PDF icon 02_761-774_00966_Bpast.No_.66-6_28.12.18_K1.pdf (1.18 MB)
CBMM Funded
T. Poggio and Liao, Q., “Theory II: Deep learning and optimization”, Bulletin of the Polish Academy of Sciences: Technical Sciences, vol. 66, no. 6, 2018.PDF icon 03_775-788_00920_Bpast.No_.66-6_31.12.18_K2.pdf (5.43 MB)
CBMM Funded
M. Schrimpf and Kubilius, J., “Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?”, bioRxiv preprint, 2018.PDF icon Brain-Score bioRxiv.pdf (789.83 KB)
CBMM Related
J. Sliwa, Marvel, S. R., Ianni, G. A., and Freiwald, W. A., “Comparing human and monkey neural circuits for processing social scenes”, Cognitive Neuroscience Society Annual Meeting (CNS), Boston, MA. 2018.
CBMM Funded
J. Traer and McDermott, J. H., “Human recognition of environmental sounds is not always robust to reverberation”, Annual Meeting of the Acoustical Society, vol. 143, no. 3. 2018.
CBMM Related
J. Wang, Zhang, Z., Xie, C., Zhou, Y., Premachandran, V., Zhu, J., Xie, L., and Yuille, A., “Visual Concepts and Compositional Voting”, Annals of Mathematical Sciences and Applications (AMSA), vol. 3, no. 1, pp. 151–188, 2018.
CBMM Funded

2017

J. A. Leonard, Lee, Y., and Schulz, L., “Infants make more attempts to achieve a goal when they see adults persist”, Science, vol. 357, no. 6357, pp. 1290 - 1294, 2017.
CBMM Funded
M. L. Leavitt, Mendoza-Halliday, D., and J.C., M. - T., “Sustained Activity Encoding Working Memories: Not Fully Distributed”, Trends in Neurosciences , vol. 40 , no. 6, pp. 328-346, 2017.
CBMM Related
D. Mendoza-Halliday and Martinez-Trujillo, J. C., “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. M., 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

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