All Publications

2018

T. D. Ullman, Stuhlmüller, A., Goodman, N. D., and Tenenebaum, 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
A. Tacchetti, Isik, L., and Poggio, T. A., “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
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.
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 IIIb: Generalization in Deep Networks”. 2018.PDF icon CBMM-Memo-090.pdf (2.67 MB)
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
CBMM Memo No.
083
Z. Zhang, Xie, C., Wang, J., Xie, L., and Yuill, A. L., “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
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
CBMM Memo No.
088
A. L. Yuille and Liu, C., “Deep Nets: What have they ever done for Vision?”. 2018.PDF icon CBMM-Memo-088.pdf (7.88 MB)
CBMM Funded
CBMM Memo No.
082
Y. - S. Wang, Liu, C., Zeng, X., and Yuille, A. L., “Scene Graph Parsing as Dependency Parsing”. 2018.PDF icon CBMM-Memo-082.pdf (869 KB)
CBMM Funded
CBMM Memo No.
079
C. Liu, Lin, Z., Shen, X., Yang, J., Lu, X., and Yuille, A. L., “Recurrent Multimodal Interaction for Referring Image Segmentation”. 2018.PDF icon CBMM-Memo-079.pdf (10.16 MB)
CBMM Funded
J. Sliwa, Marvel, S. R., Ianni, G. A., and Freiwald, W. A., “Comparing human and monkey neural circuits for processing social scenes”, Social & Affective Neuroscience Society (SANS). Brooklyn, NY , 2018.
CBMM Related
CBMM Memo No.
077
A. Adler and Wax, M., “Constant Modulus Algorithms via Low-Rank Approximation”. 2018.PDF icon CBMM-Memo-077.pdf (795.61 KB)
CBMM Funded
D. Harari, Tenenabum, J. B., and Ullman, S., “Discovery and usage of joint attention in images”, arXiv.org, 2018.PDF icon 1804.04604v1.pdf (488.85 KB)
CBMM Funded
Y. Wu, Baker, C. L., Tenenbaum, J. B., and Schulz, L. E., “Rational inference of beliefs and desires from emotional expressions”, Cognitive Science, vol. 42, no. 3, 2018.PDF icon Wu_Baker_Tenenbaum_Schulz_in_press_cognitive_science.pdf (1.65 MB)
CBMM Funded
CBMM Memo No.
087
J. Wang, Zhang, Z., Xie, C., Zhou, Y., Premachandran, V., Zhu, J., Xie, L., and Yuill, A. L., “Visual concepts and compositional voting”. 2018.PDF icon CBMM-Memo-087.pdf (3.37 MB)
CBMM Funded
M. Dillon and Spelke, E. S., “From Map Reading to Geometric Intuitions”, Developmental Psychology, 2018.
CBMM Related
W. Mlynarski and McDermott, J., “Learning Mid-Level Auditory Codes from Natural Sound Statistics”, Neural Computation, vol. 30, no. 3, pp. 631-669, 2018.
CBMM Funded
W. Mlynarski and McDermott, J., “Co-occurrence statistics of natural sound features predict perceptual grouping”, Computational and Systems Neuroscience (COSYNE). Denver, Colorado, 2018.
CBMM Funded
J. Traer and McDermott, J. H., “Human inference of force from impact sounds: Perceptual evidence for inverse physics”, Annual Meeting of the Acoustical Society, vol. 143, no. 3. 2018.
CBMM Related
CBMM Memo No.
076
H. Mhaskar and Poggio, T., “An analysis of training and generalization errors in shallow and deep networks”. 2018.PDF icon CBMM-Memo-076.pdf (772.61 KB)
CBMM Funded

Pages