All Publications

2021

CBMM Memo No.
114
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
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
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
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
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
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

Pages