All Publications

Submitted

L. Jing, Shen, Y., Dubček, T., Peurifoi, J., Skirlo, S., LeCun, Y., Tegmark, M., and Soljačić, M., “Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNN”. Submitted.PDF icon 1612.05231.pdf (2.3 MB)
CBMM Related

In Press

G. Ben-Yosef and Ullman, S., “Image interpretation above and below the object level”, Proceedings of the Royal Society: Interface Focus, In Press.PDF icon 2018-BenYosef_Ullman-Image_interpretation_above_and_below the object_level.pdf (3.26 MB)
CBMM Funded
T. Gerstenberg, Ullman, T. D., Nagel, J., Kleiman-Weiner, M., Lagnado, D. A., and Tenenbaum, J. B., “Lucky or clever? From changed expectations to attributions of responsibility”, Cognition, In Press.
CBMM Funded

2018

T. D. Ullman, Stuhlmüller, A., Goodman, N. D., and Tenenebaum, J. B., “Learning physical parameters from dynamic scenes.”, Cognitive Psychology, vol. 104, pp. 57-82, 2018.PDF icon T-Ullman-etal_CogPsych_LearningPhysicalParametersFromDynamicScenes.pdf (3.15 MB)
CBMM Funded
CBMM Memo No.
084
Z. Zhang, Qiao, S., Xie, C., Shen, W., Wang, B., and Yuille, A. L., “Single-Shot Object Detection with Enriched Semantics”. 2018.PDF icon CBMM-Memo-084.pdf (1.92 MB)
CBMM Funded
CBMM Memo No.
083
Z. Zhang, Xie, C., Wang, J., Xie, L., and Yuill, A. L., “DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion”. 2018.PDF icon CBMM-Memo-083.pdf (2.32 MB)
CBMM Funded
CBMM Memo No.
077
A. Adler and Wax, M., “Constant Modulus Algorithms via Low-Rank Approximation”. 2018.PDF icon CBMM-Memo-077.pdf (795.61 KB)
CBMM Funded
D. Harari, Tenenabum, J. B., and Ullman, S., “Discovery and usage of joint attention in images”, arXiv.org, 2018.PDF icon 1804.04604v1.pdf (488.85 KB)
CBMM Funded
CBMM Memo No.
076
H. Mhaskar and Poggio, T., “An analysis of training and generalization errors in shallow and deep networks”. 2018.PDF icon CBMM-Memo-076.pdf (772.61 KB)
CBMM Funded
M. Araya-Polo, Jennings, J., Adler, A., and Dahlke, T., “Deep-learning tomography”, The Leading Edge, vol. 37, no. 1, pp. 58 - 66, 2018.PDF icon TLE2018.pdf (1.9 MB)
CBMM Funded
D. Muir, Fang, X., and Meyers, E., “Brain-Observatory-Toolbox”. 2018.
CBMM Funded
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

2017

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)
CBMM Funded
CBMM Memo No.
072
C. Zhang, Liao, Q., Rakhlin, A., Miranda, B., Golowich, N., and Poggio, T. A., “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., “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
L. Isik, Tacchetti, A., and Poggio, T. A., “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. A., 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
A. Tacchetti, Isik, L., and Poggio, T., “Invariant action recognition dataset”. 2017.
CBMM Funded
CBMM Memo No.
070
Q. Liao and Poggio, T. A., “Object-Oriented Deep Learning”. 2017.PDF icon CBMM-Memo-070.pdf (963.54 KB)
CBMM Funded
CBMM Memo No.
078
J. Wang, Xie, C., Zhang, Z., Zhu, J., Xie, L., and Yuille, A., “Detecting Semantic Parts on Partially Occluded Objects”. 2017.PDF icon CBMM-Memo-078.pdf (1.74 MB)
CBMM Funded