All Publications

2018

L. Isik, Singer, J., Kanwisher, N., ,, WS, A., and Kreiman, G., “What is changing when: decoding visual information in movies from human intracranial recordings”, NeuroImage, vol. 180, Part A, pp. 147-159, 2018.PDF icon Human neurophysiological responses during movies (2.78 MB)
CBMM Funded
W. Kool, Gershman, S. J., and Cushman, F. A., “Planning Complexity Registers as a Cost in Metacontrol”, Journal of Cognitive Neuroscience, vol. 30, no. 10, pp. 1391 - 1404, 2018.
CBMM Funded
Y. - L. Kuo, Barbu, A., and Katz, B., “Deep sequential models for sampling-based planning”, in The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018), Madrid, Spain, 2018.PDF icon kuo2018planning.pdf (637.67 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 parsersusing captioned videos”, in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP 2018), , Brussels, Belgium, 2018.PDF icon Ross-et-al_ACL2018_Grounding language acquisition by training semantic parsing using caption videos.pdf (3.5 MB)
CBMM Funded
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
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

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