All Publications

2019

S. Ullman, “Using neuroscience to develop artificial intelligence”, Science, vol. 363, no. 6428, pp. 692 - 693, 2019.
CBMM Related

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
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
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 (1.92 MB)
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 Asian Conference for Computer Vision (ACCV), 2018.PDF icon partially-occluded-hands-6.pdf (8.29 MB)
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
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
N. O'Brien, Latessa, S., Evangelopoulos, G., and Boix, X., “The Language of Fake News: Opening the Black-Box of Deep Learning Based Detectors”, in workshop on "AI for Social Good", NIPS 2018, Montreal, Canada, 2018.PDF icon fake-news-paper-NIPS.pdf (147.36 KB)PDF icon fake-news-paper-NIPS_2018_v2.pdf (147.36 KB)
CBMM Funded
Y. - L. Kuo, Barbu, A., and Katz, B., “Deep sequential models for sampling-based planning”, in International Conference on Intelligent Robots, 2018.PDF icon kuo2018planning.pdf (628.98 KB)
CBMM Funded
E. Meyers, “Dynamic population coding and its relationship to working memory”, Journal of Neurophysiology, vol. 120, no. 5, pp. 2260 - 2268, 2018.
CBMM Funded
C. Ross, Barbu, A., Berzak, Y., Myanganbayar, B., and Katz, B., “Grounding language acquisition by training semantic parsers using captioned videos”, in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018.PDF icon D18-1285.pdf (3.5 MB)
A. Tacchetti, Isik, L., and Poggio, T., “Invariant Recognition Shapes Neural Representations of Visual Input”, Annual Review of Vision Science, vol. 4, no. 1, pp. 403 - 422, 2018.PDF icon annurev-vision-091517-034103.pdf (1.55 MB)
CBMM Funded
E. Zisselman, Adler, A., and Elad, M., “Compressed Learning for Image Classification: A Deep Neural Network Approach”, in Handbook of Numerical Analysis, vol. 19, Elsevier, 2018, pp. 3 - 17.
CBMM Related
M. Zhang, Feng, J., Ma, K. Teck, Lim, J. Hwee, Zhao, Q., and Kreiman, G., “Finding any Waldo with zero-shot invariant and efficient visual search”, Nature Communications, vol. 9, 2018.
CBMM Funded
A. C. Spokes and Spelke, E. S., “At 4.5 but not 5.5 years, children favor kin when the stakes are moderately high”, PLOS ONE, vol. 13, no. 8, 2018.
CBMM Related
H. Tang, Schrimpf, M., Lotter, W., Moerman, C., Paredes, A., Caro, J. Ortega, Hardesty, W., Cox, D., and Kreiman, G., “Recurrent computations for visual pattern completion”, Proceedings of the National Academy of Sciences, 2018.PDF icon 1719397115.full_.pdf (1.1 MB)
CBMM Funded
CBMM Memo No.
096
M. Zhang, Feng, J., Lim, J. Hwee, Zhao, Q., and Gabriel, K., “What am I searching for?”. 2018.PDF icon CBMM-Memo-096.pdf (1.74 MB)
CBMM Funded
CBMM Memo No.
091
Q. Liao, Miranda, B., Hidary, J., and Poggio, T., “Classical generalization bounds are surprisingly tight for Deep Networks”. 2018.PDF icon CBMM-Memo-091.pdf (1.43 MB)PDF icon CBMM-Memo-091-v2.pdf (1.88 MB)
CBMM Funded
R. Saxe, “Imaging the infant brain”, Japanese Society for Neuroscience, vol. Kobe Japan. 2018.
CBMM Related
CBMM Memo No.
090
T. Poggio, Liao, Q., Miranda, B., Banburski, A., Boix, X., and Hidary, J., “Theory III: Dynamics and Generalization in Deep Networks”. 2018.PDF icon CBMM-Memo-090.pdf (2.67 MB)PDF icon CBMM Memo 090 v2 (revised on 1/3/2019) (601.18 KB)
CBMM Funded
CBMM Memo No.
085
W. Shen, Guo, Y., Wang, Y., Zhao, K., Wang, B., and Yuille, A. L., “Deep Regression Forests for Age Estimation”. 2018.PDF icon CBMM-Memo-085.pdf (2.2 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

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