All Publications

2021

A. Adler, Araya-Polo, M., and Poggio, T., “Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows”, IEEE Signal Processing Magazine, vol. 38, no. 2, pp. 89 - 119, 2021.
CBMM Funded
CBMM Memo No.
116
O. Kunhardt, Deza, A., and Poggio, T. A., “The Effects of Image Distribution and Task on Adversarial Robustness”. 2021.PDF icon CBMM_Memo_116.pdf (5.44 MB)
CBMM Funded
CBMM Memo No.
114
T. Poggio, “From Associative Memories to Deep Networks”. 2021.PDF icon CBMM-Memo-114.pdf (1.01 MB)PDF icon A few edits (1.03 MB)
CBMM Funded
CBMM Memo No.
115
A. Banburski, De La Torre, F., Pant, N., Shastri, I., and Poggio, T., “Distribution of Classification Margins: Are All Data Equal?”. 2021.PDF icon CBMM Memo 115.pdf (9.56 MB)
CBMM Funded

2020

T. Poggio, Banburski, A., and Liao, Q., “Theoretical issues in deep networks”, Proceedings of the National Academy of Sciences, p. 201907369, 2020.PDF icon PNASlast.pdf (915.3 KB)
CBMM Funded
L. Isik, Mynick, A., Pantazis, D., and Kanwisher, N., “The speed of human social interaction perception”, NeuroImage, p. 116844, 2020.
CBMM Funded
H. Richardson, Gweon, H., Dodell-Feder, D., Malloy, C., Pelton, H., Keil, B., Kanwisher, N., and Saxe, R., “Response patterns in the developing social brain are organized by social and emotion features and disrupted in children diagnosed with autism spectrum disorder”, Cortex, vol. 125, pp. 12 - 29, 2020.
CBMM Funded
S. Levine, Kleiman-Weiner, M., Schulz, L., Tenenbaum, J., and Cushman, F., “The logic of universalization guides moral judgment”, Proceedings of the National Academy of Sciences (PNAS), p. 202014505, 2020.
CBMM Funded
J. Dapello, Marques, T., Schrimpf, M., Geiger, F., Cox, D., and DiCarlo, J. J., “Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations”, Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020). 2020.
CBMM Funded
E. Malkin, Deza, A., and Poggio, T. A., “CUDA-Optimized real-time rendering of a Foveated Visual System”, in Shared Visual Representations in Human and Machine Intelligence (SVRHM) workshop at NeurIPS 2020, 2020.PDF icon Foveated_Drone_SVRHM_2020.pdf (13.44 MB)PDF icon v1 (12/15/2020) (14.7 MB)
CBMM Funded
A. Netanyahu, Shu, T., Katz, B., Barbu, A., and Tenenabum, 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
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
K. R. 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
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
K. Dobs, Kell, A. J. E., Martinez, 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
CBMM Memo No.
112
T. A. Poggio and Liao, Q., “Implicit dynamic regularization in deep networks”. 2020.PDF icon TPR_ver2.pdf (2.29 MB)PDF icon Substantial edits (1.52 MB)PDF icon Edits that are extensive but minor in content (1.98 MB)PDF icon Extending theory, setting a post (2 MB)PDF icon Fine tuning (2.01 MB)PDF icon Corrections in Appendix about Neural Collapse (2.01 MB)PDF icon Small edits clarifying role of weight decay (2.39 MB)PDF icon Added: prove NC for multiclass+theorem on connected global minima (2.4 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. N. 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

Pages