All Publications

2019

M. Araya-Polo, Adler, A., Farris, S., and Jennings, J., “Fast and Accurate Seismic Tomography via Deep Learning”, in Deep Learning: Algorithms and Applications, SPRINGER-VERLAG, 2019.
CBMM Related
K. M. Jozwik, Schrimpf, M., Kanwisher, N., and DiCarlo, J. J., “To find better neural network models of human vision, find better neural network models of primate vision”, in BioRxiv, 2019.
CBMM Funded
K. M. Jozwik, Lee, H., Kanwisher, N., and DiCarlo, J. J., “Are topographic deep convolutional neural networks better models of the ventral visual stream?”, in Conference on Cognitive Computational Neuroscience, 2019.
CBMM Related
G. Kreiman, “What do neurons really want? The role of semantics in cortical representations.”, in Psychology of Learning and Motivation, vol. 70, 2019.
CBMM Funded
S. Liu, Cushman, F. A., Gershman, S. J., Kool, W., and Spelke, E. S., “Hard choices: Children’s understanding of the cost of action selection. ”, in Cognitive Science Society, 2019.PDF icon phk_cogsci_2019_final.pdf (276.14 KB)
CBMM Funded
S. Liu, McCoy, J. P., and Ullman, T. D., “People's perceptions of others’ risk preferences.”, in Cognitive Science Society, 2019.PDF icon risk_cogsci_2019_final.pdf (899.8 KB)
CBMM Funded
S. Liu, Brooks, N. B., and Spelke, E. S., “Origins of the concepts cause, cost, and goal in prereaching infants.”, Cognitive Development Society. 2019.PDF icon liu_etal_lumi_cds2019_final.pdf (22.95 MB)
CBMM Funded
S. Srivastava, Ben-Yosef, G., and Boix, X., “Minimal images in deep neural networks: Fragile Object Recognition in Natural Images”, in International Conference on Learning Representations (ICLR), New Orleans, La, 2019.
CBMM Funded
W. Xiao, Chen, H., Liao, Q., and Poggio, T., “Biologically-plausible learning algorithms can scale to large datasets.”, in International Conference on Learning Representations, (ICLR 2019), 2019.PDF icon gk7779.pdf (721.53 KB)
CBMM Funded

2018

S. Hu, Ciliberti, D., Grosmark, A. D., Michon, édéric, Ji, D., Penagos, H., áki, örgy, Wilson, M. A., Kloosterman, F., and Chen, Z., “Real-Time Readout of Large-Scale Unsorted Neural Ensemble Place Codes”, Cell Reports, vol. 25, no. 10, pp. 2635 - 2642.e5, 2018.
CBMM Funded
P. Misra, Marconi, A., Peterson, M. F., and Kreiman, G., “Minimal memory for details in real life events”, Scientific Reports, vol. 8, no. 1, 2018.
CBMM Funded
T. D. Ullman, Stuhlmüller, A., Goodman, N. D., and Tenenbaum, 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
D. Adhya, Swarup, V., Nowosaid, P., Shum, C., Jozwik, K. M., McAlonan, G., Mendez, M. A., Horder, J., Murphy, D., Geschwind, D. H., Price, J., Carroll, J., Srivastava, D. P., and Baron-Cohen, S., “Shared gene co-expression networks in autism from induced pluripotent stem cell (iPSC) neurons”, in BioRxiv, 2018.
CBMM Related
CBMM Memo No.
097
B. Myanganbayar, Mata, C., Dekel, G., Katz, B., Ben-Yosef, G., and Barbu, A., “Partially Occluded Hands: A challenging new dataset for single-image hand pose estimation”. 2018.PDF icon CBMM-Memo-097.pdf (8.53 MB)
CBMM Funded
CBMM Memo No.
095
K. M. Villalobos, Dozier, J., Stih, V., Francl, A., Azevedo, F., Poggio, T., Sasaki, T., and Boix, X., “Can Deep Neural Networks Do Image Segmentation by Understanding Insideness?”. 2018.PDF icon CBMM-Memo-095.pdf (1.96 MB)
CBMM Funded
W. Kool and Botvinick, M., “Mental labour”, Nature Human Behaviour, vol. 2, no. 12, pp. 899 - 908, 2018.
CBMM Funded
R. Madhavan, Bansal, A. K., Madsen, J., Golby, A. J., Tierney, T. S., Eskandar, E., Anderson, W. S., and Kreiman, G., “Neural Interactions Underlying Visuomotor Associations in the Human Brain”, Cerebral Cortex, vol. 1–17, 2018.
CBMM Funded
B. Myanganbayar, Mata, C., Dekel, G., Katz, B., Ben-Yosef, G., and Barbu, A., “Partially Occluded Hands: A challenging new dataset for single-image hand pose estimation”, in The 14th Asian Conference on Computer Vision (ACCV 2018), 2018.PDF icon partially-occluded-hands-6.pdf (8.29 MB)
CBMM Funded
L. J. Powell and Spelke, E. S., “Third-Party Preferences for Imitators in Preverbal Infants”, Open Mind, vol. 2, no. 2, pp. 61 - 71, 2018.
CBMM Funded
A. Tacchetti, Voinea, S., and Evangelopoulos, G., “Trading robust representations for sample complexity through self-supervised visual experience”, in Advances in Neural Information Processing Systems 31, Montreal, Canada, 2018, pp. 9640–9650.PDF icon trading-robust-representations-for-sample-complexity-through-self-supervised-visual-experience.pdf (3.32 MB)PDF icon NeurIPS2018_Poster.pdf (6.12 MB)
CBMM Memo No.
094
G. Ben-Yosef, Kreiman, G., and Ullman, S., “Spatiotemporal interpretation features in the recognition of dynamic images”. 2018.PDF icon CBMM-Memo-094.pdf (1.21 MB)Package icon CBMM-Memo-094-dynamic-figures.zip (1.8 MB)File fig1.ppsx (147.67 KB)File fig2.ppsx (419.72 KB)File fig4.ppsx (673.41 KB)File figS1.ppsx (587.88 KB)File figS2.ppsx (281.56 KB)
CBMM Funded
CBMM Memo No.
093
L. Arend, Han, Y., Schrimpf, M., Bashivan, P., Kar, K., Poggio, T., DiCarlo, J. J., and Boix, X., “Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results”. 2018.PDF icon CBMM-Memo-093.pdf (2.99 MB)
CBMM Funded
CBMM Memo No.
092
W. Xiao, Chen, H., Liao, Q., and Poggio, T., “Biologically-plausible learning algorithms can scale to large datasets”. 2018.PDF icon CBMM-Memo-092.pdf (1.31 MB)
CBMM Funded
M. S. Livingstone, Arcaro, M. J., and Schade, P. F., “Cortex Is Cortex: Ubiquitous Principles Drive Face-Domain Development”, Trends in Cognitive Sciences, 2018.PDF icon 1-s2.0-S1364661318302572-main.pdf (260.4 KB)
CBMM Related
J. P. McCoy and Ullman, T. D., “A Minimal Turing Test”, Journal of Experimental Social Psychology, vol. 79, pp. 1 - 8, 2018.
CBMM Related

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