All Publications

2018

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
T. Owaki, Vidal-Naquet, M., Nam, Y., Uchida, G., Sato, T., Câteau, H., Ullman, S., and Tanifuji, M., “Searching for visual features that explain response variance of face neurons in inferior temporal cortex”, PLOS ONE, vol. 13, no. 9, p. e0201192, 2018.
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
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, 2018.
CBMM Funded
Y. Hart, Dillon, M. R., Marantan, A., Cardenas, A. L., Spelke, E. S., and Mahadevan, L., “The statistical shape of geometric reasoning”, Scientific Reports, vol. 8, no. 1, 2018.
CBMM Funded
J. Sliwa, Marvel, S. R., Ianni, G. A., and Freiwald, W. A., “Comparing human and monkey neural circuits for processing social scenes”, Organization for Computational Neurosciences - CNS 2018. Seattle, WA, 2018.
CBMM Related
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 Kreiman, G., “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
W. Mlynarski and Hermundstad, A. M., “Adaptive Coding for Dynamic Sensory Inference”, eLife, 2018.
CBMM Funded
A. Palepu and Kreiman, G., “Development of automated interictal spike detector”, 40th International Conference of the IEEE Engineering in Medicine and Biology Society - EMBC 2018. Honolulu, HI, 2018.
CBMM Funded
R. Saxe, “Imaging the infant brain”, Japanese Society for Neuroscience, vol. Kobe Japan. 2018.
CBMM Related
CBMM Memo No.
090
A. Banburski, Liao, Q., Miranda, B., Poggio, T., Rosasco, L., Hidary, J., and De La Torre, F., “Theory III: Dynamics and Generalization in Deep Networks”. 2018.PDF icon Original, intermediate versions are available under request (2.67 MB)PDF icon CBMM Memo 90 v12.pdf (4.74 MB)PDF icon Theory_III_ver44.pdf Update Hessian (4.12 MB)PDF icon Theory_III_ver48 (Updated discussion of convergence to max margin) (2.56 MB)PDF icon fixing errors and sharpening some proofs (2.45 MB)
CBMM Funded
CBMM Memo No.
085
W. Shen, Guo, Y., Wang, Y., Zhao, K., Wang, B., and Yuille, A., “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., “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 Yuille, A., “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
G. Ben-Yosef and Ullman, S., “Image interpretation above and below the object level”, Proceedings of the Royal Society: Interface Focus, 2018.PDF icon 2018-BenYosef_Ullman-Image_interpretation_above_and_below the object_level.pdf (3.26 MB)
CBMM Funded
G. Ben-Yosef and Ullman, S., “Image interpretation above and below the object level”, Interface Focus, vol. 8, no. 4, p. 20180020, 2018.
CBMM Funded
Y. Berzak, Katz, B., and Levy, R., “Assessing Language Proficiency from Eye Movements in Reading”, 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. New Orleans, 2018.PDF icon 1804.07329.pdf (350.43 KB)
CBMM Funded
J. B. Hamrick, Allen, K., Bapst, V., Zhu, T., McKee, K. R., Tenenbaum, J. B., and Battaglia, P., “Relational inductive bias for physical construction in humans and machines”, In Proceedings of the Annual Meeting of the Cognitive Science Society (CogSci 2018). 2018.PDF icon 1806.01203.pdf (1022.51 KB)
CBMM Related
M. Toussaint, Allen, K., Smith, K. A., and Tenenbaum, J. B., “Differentiable physics and stable modes for tool-use and manipulation planning”, Robotics: Science and Systems 2018. 2018.PDF icon ToussaintEtAl_DiffPhysStable.pdf (1.97 MB)
CBMM Related
Z. Zhang, Xie, C., Wang, J., Xie, L., and Yuille, A., “DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion”, in Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, 2018.
CBMM Funded
Z. Zhang, Qiao, S., Xie, C., Shen, W., Wang, B., and Yuille, A., “Single-Shot Object Detection with Enriched Semantics”, in Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, 2018.
CBMM Funded
CBMM Memo No.
089
G. Ben-Yosef and Ullman, S., “Image interpretation above and below the object level”. 2018.PDF icon CBMM-Memo-089.pdf (2.06 MB)
CBMM Funded

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