All Publications

Submitted

G. Ben-Yosef, Assif, L., and Ullman, S., “Full interpretation of minimal images.”, Submitted.PDF icon Full interpretation of minimal images.pdf (4.55 MB)
CBMM Funded
T. Gerstenberg, Zhou, L., Smith, K. A., and Tenenbaum, J. B., “Faulty Towers: A counterfactual simulation model of physical support”, Proceedings of the 39th Annual Conference of the Cognitive Science Society. Submitted.PDF icon Faulty Towers A counterfactual simulation model of physical support, Gerstenberg et al., 2017.pdf (8.75 MB)
CBMM Funded
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
H. Lin and Tegmark, M., “Critical Behavior from Deep Dynamics: A Hidden Dimension in Natural Language”, Submitted.PDF icon Critical Behavior from Deep Dynamics: A Hidden Dimension in Natural Language (1.64 MB)
CBMM Related
H. W. Lin and Tegmark, M., “Why does deep and cheap learning work so well?”, arXiv preprint arXiv:1608.08225, Submitted.PDF icon 1608.08225.pdf (2.14 MB)
CBMM Related
S. Stephan, Willemsen, P., and Gerstenberg, T., “Marbles in inaction: Counterfactual simulation and causation by omission”, Proceedings of the 39th Annual Conference of the Cognitive Science Society. Submitted.PDF icon Marbles in Inaction Counterfactual Simulation and Causation by Omission, Stephan, Willemsen, Gerstenberg, 2017.pdf (1.46 MB)
CBMM Funded
I. Yildirim, Gerstenberg, T., Saeed, B., Toussant, M., and Tenenbaum, J. B., “Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints”, Proceedings of the 39th Annual Conference of the Cognitive Science Society. Submitted.PDF icon Physical problem solving Joint planning with symbolic, geometric, and dynamic constraints, Yildirim et al., 2017.pdf (2.46 MB)
CBMM Funded

In Press

R. Magid, Yan, P., Siegel, M., Tenenbaum, J. B., and Schulz, L. E., “Changing minds: Children’s inferences about third party belief revision”. In Press.PDF icon Changing Minds_MagidYanSiegelTenenbaumSchulz_in press.pdf (915.8 KB)
CBMM Funded
R. Magid and Schulz, L. E., “Moral alchemy: How love changes norms”. In Press.PDF icon Moral Alchemy_Magid&Schulz.pdf (627.46 KB)
CBMM Related
S. Sadagopan, Zarco, W., and Freiwald, W. A., “A Causal Relationship Between Face-Patch Activity and Face-Detection Behavior”, eLife, In Press.
CBMM Funded
R. Saxe and Houlihan, S. D., “Formalizing emotion concepts within a Bayesian model of theory of mind”, Current Option in Psychology, In Press.
CBMM Funded
Y. Wu and Schulz, L., “Inferring Beliefs and Desires From Emotional Reactions to Anticipated and Observed Events”, In Press.
CBMM Funded

2017

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
M. J. Arcaro, Schade, P. F., Vincent, J. L., Ponce, C. R., and Livingstone, M. S., “Seeing faces is necessary for face-domain formation”, Nature Neuroscience, vol. 5631628, 2017.
CBMM Related
G. Kreiman, “A null model for cortical representations with grandmothers galore”, Language, Cognition and Neuroscience, pp. 274 - 285, 2017.
CBMM Funded
H. Penagos, Varela, C., and Wilson, M. A., “Oscillations, neural computations and learning during wake and sleep”, Current Opinion in Neurobiology, vol. 44C, no. June 2017, 2017.
CBMM Funded
CBMM Memo No.
069
A. Volokitin, Roig, G., and Poggio, T., “Do Deep Neural Networks Suffer from Crowding?”. 2017.PDF icon CBMM-Memo-069.pdf (6.47 MB)
CBMM Funded
CBMM Memo No.
068
B. Subirana, Bagiati, A., and Sarma, S., “On the Forgetting of College Academics: at "Ebbinghaus Speed"?”. 2017.PDF icon CBMM Memo 068-On Forgetting - June 18th 2017 v2.pdf (713.7 KB)
CBMM Funded
A. Volokitin and Roig, G., “Do Deep Neural Networks Suffer from Crowding? [code]”. 2017.
CBMM Funded
CBMM Memo No.
063
F. Anselmi, Evangelopoulos, G., Rosasco, L., and Poggio, T., “Symmetry Regularization”. 2017.PDF icon CBMM-Memo-063.pdf (6.1 MB)
CBMM Funded
J. Sliwa and Freiwald, W. A., “A Dedicated Network for Social Interaction Processing in the Primate Brain”, Science, vol. Vol. 356, no. Issue 6339, pp. pp. 745-749, 2017.
CBMM Funded
CBMM Memo No.
067
C. Zhang, Liao, Q., Rakhlin, A., Sridharan, K., Miranda, B., Golowich, N., and Poggio, T., “Theory of Deep Learning III: Generalization Properties of SGD”. 2017.PDF icon CBMM Memo 067 v2 (revised 7/19/2017) (5.88 MB)PDF icon CBMM Memo 067 v3 (revised 9/15/2017) (5.89 MB)
CBMM Funded
CBMM Memo No.
066
T. Poggio and Liao, Q., “Theory II: Landscape of the Empirical Risk in Deep Learning”. 2017.PDF icon CBMM Memo 066_1703.09833v2.pdf (5.56 MB)
CBMM Funded
CBMM Memo No.
065
N. Cheney, Schrimpf, M., and Kreiman, G., “On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations”. 2017.PDF icon CBMM-Memo-065.pdf (687.76 KB)
CBMM Related
CBMM Memo No.
064
W. Lotter, Kreiman, G., and Cox, D., “Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning”. 2017.PDF icon CBMM-Memo-064.pdf (3 MB)
CBMM Funded