All Publications

2020

M. J. McPherson and McDermott, J. H., “Time-dependent discrimination advantages for harmonic sounds suggest efficient coding for memory”, Proceedings of the National Academy of Sciences, vol. 117, no. 50, pp. 32169 - 32180, 2020.
CBMM Related
A. Netanyahu, Shu, T., Katz, B., Barbu, A., and Tenenbaum, J. B., “PHASE: PHysically-grounded Abstract Social Eventsfor Machine Social Perception”, in Shared Visual Representations in Human and Machine Intelligence (SVRHM) workshop at NeurIPS 2020, 2020.PDF icon phase_physically_grounded_abstract_social_events_for_machine_social_perception.pdf (2.49 MB)
CBMM Funded
M. Nye, Solar-Lezama, A., Tenenbaum, J. B., and Lake, B. M., “Learning Compositional Rules via Neural Program Synthesis”, in Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020), 2020.PDF icon 2003.05562.pdf (2.51 MB)
CBMM Funded
T. Poggio and Liao, Q., “Explicit regularization and implicit bias in deep network classifiers trained with the square loss”, arXiv, 2020.
CBMM Funded
D. J. Schaeffer, Selvanayagam, J., Johnston, K. D., Menon, R. S., Freiwald, W. A., and Everling, S., “Face selective patches in marmoset frontal cortexAbstract”, Nature Communications, vol. 11, no. 1, 2020.
CBMM Related
L. Tian, Ellis, K., Kryven, M., and Tenenbaum, J. B., “Learning abstract structure for drawing by efficient motor program induction”, in Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020), 2020.
CBMM Funded
S. - M. Udrescu, Tan, A., Feng, J., Neto, O., Wu, T., and Tegmark, M., “AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity”, in Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020), 2020.PDF icon 2006.10782.pdf (2.62 MB)
CBMM Funded
C. Wang, Ross, C., Kuo, Y. - L., Katz, B., and Barbu, A., “Learning a Natural-language to LTL Executable Semantic Parser for Grounded Robotics”, Proceedings of Conference on Robot Learning (CoRL-2020), 2020.
CBMM Funded
K. Allen, Smith, K. A., and Tenenbaum, J. B., “Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning”, Proceedings of the National Academy of Sciences, p. 201912341, 2020.PDF icon 1912341117.full_.pdf (2.15 MB)
CBMM Funded
CBMM Memo No.
113
A. Banburski, Gandhi, A., Alford, S., Dandekar, S., Chin, P., and Poggio, T., “Dreaming with ARC”, Learning Meets Combinatorial Algorithms workshop at NeurIPS 2020. 2020.PDF icon CBMM Memo 113.pdf (1019.64 KB)
CBMM Funded
S. J. Gershman, “Origin of perseveration in the trade-off between reward and complexity”, Cognition, vol. 204, p. 104394, 2020.
CBMM Related
M. Schrimpf, Kubilius, J., Lee, M. J., N. Murty, A. Ratan, Ajemian, R., and DiCarlo, J. J., “Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence”, Neuron, vol. 108, no. 3, pp. 413 - 423, 2020.
CBMM Related
L. Tomova, Wang, K. L., Thompson, T., Matthews, G. A., Takahashi, A., Tye, K. M., and Saxe, R., “Acute social isolation evokes midbrain craving responses similar to hunger”, Nature Neuroscience, vol. 23, no. 12, pp. 1597 - 1605, 2020.PDF icon s41593-020-00742-z.pdf (5.47 MB)
CBMM Funded
K. Kar and DiCarlo, J. J., “Fast Recurrent Processing via Ventrolateral Prefrontal Cortex Is Needed by the Primate Ventral Stream for Robust Core Visual Object Recognition”, Neuron, 2020.PDF icon PIIS0896627320307595.pdf (3.92 MB)
CBMM Funded
T. Poggio and Banburski, A., “An Overview of Some Issues in the Theory of Deep Networks”, IEEJ Transactions on Electrical and Electronic Engineering, vol. 15, no. 11, pp. 1560 - 1571, 2020.
CBMM Funded
K. Vinken, Boix, X., and Kreiman, G., “Incorporating intrinsic suppression in deep neural networks captures dynamics of adaptation in neurophysiology and perception”, Science Advances, vol. 6, no. 42, p. eabd4205, 2020.PDF icon gk7967.pdf (3.07 MB)
CBMM Funded
J. Bill, Pailian, H., Gershman, S. J., and Drugowitsch, J., “Hierarchical structure is employed by humans during visual motion perception”, Proceedings of the National Academy of Sciences, vol. 117, no. 39, pp. 24581 - 24589, 2020.
CBMM Related
M. R. Dillon, Izard, V., and Spelke, E. S., “Infants’ sensitivity to shape changes in 2D visual forms”, Infancy, vol. 25, no. 5, pp. 618 - 639, 2020.
CBMM Funded
K. Dobs, Kell, A. J. E., Martinez-Trujillo, J., Cohen, M., and Kanwisher, N., “Why Are Face and Object Processing Segregated in the Human Brain? Testing Computational Hypotheses with Deep Convolutional Neural Networks ”, in Conference on Cognitive Computational Neuroscience, Berlin, Germany, 2020.
CBMM Funded
M. Sheskin, Scott, K., Mills, C. M., Bergelson, E., Bonawitz, E., Spelke, E. S., Fei-Fei, L., Keil, F. C., Gweon, H., Tenenbaum, J. B., Jara-Ettinger, J., Adolph, K. E., Rhodes, M., Frank, M. C., Mehr, S. A., and Schulz, L., “Online Developmental Science to Foster Innovation, Access, and Impact”, Trends in Cognitive Sciences, vol. 24, no. 9, pp. 675 - 678, 2020.
CBMM Funded
CBMM Memo No.
122
C. Wang, Ross, C., Kuo, Y. - L., Katz, B., and Barbu, A., “Learning a natural-language to LTL executable semantic parser for grounded robotics”. 2020.PDF icon CBMM-Memo-122.pdf (1.03 MB)
CBMM Funded
CBMM Memo No.
112
T. Poggio, Liao, Q., and Xu, M., “Implicit dynamic regularization in deep networks”. 2020.PDF icon v1.2 (2.29 MB)PDF icon v.59 Update on rank (2.43 MB)
CBMM Funded
G. Ben-Yosef, Kreiman, G., and Ullman, S., “Minimal videos: Trade-off between spatial and temporal information in human and machine vision.”, Cognition, 2020.
CBMM Funded
H. Mhaskar and Poggio, T., “Function approximation by deep networks”, Communications on Pure & Applied Analysis, vol. 19, no. 8, pp. 4085 - 4095, 2020.PDF icon 1534-0392_2020_8_4085.pdf (514.57 KB)
CBMM Funded
R. Rajalingham, Kar, K., Sanghavi, S., Dehaene, S., and DiCarlo, J. J., “The inferior temporal cortex is a potential cortical precursor of orthographic processing in untrained monkeys”, Nature Communications, vol. 11, no. 1, 2020.PDF icon s41467-020-17714-3.pdf (25.01 MB)
CBMM Funded

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