All Publications

2018

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. 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., “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., “Recurrent Multimodal Interaction for Referring Image Segmentation”. 2018.PDF icon CBMM-Memo-079.pdf (10.16 MB)
CBMM Funded
W. Eric, Kevin, W., and Kreiman, G., “Learning scene gist with convolutional neural networks to improve object recognition”, 2018 52nd Annual Conference on Information Sciences and Systems (CISS). Princeton, NJ, 2018.PDF icon 08362305.pdf (3.17 MB)
CBMM Funded
W. Lotter, Kreiman, G., and Cox, D., “A neural network trained to predict future videoframes mimics critical properties of biologicalneuronal responses and perception”, arXiv | Cornell University, arXiv:1805.10734v2, 2018.PDF icon 1805.10734.pdf (9.59 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, Tenenbaum, 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
A. J. E. Kell, Yamins, D. L. K., Shook, E. N., Norman-Haignere, S. V., and McDermott, J. H., “A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy”, Neuron, vol. 98, 2018.
CBMM Related
Y. Wu, Baker, C., Tenenbaum, J. B., and Schulz, L., “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
I. Yildirim, Freiwald, W. A., and J., T., “Efficient inverse graphics in biological face processing”, bioRxiv. 2018.
CBMM Funded
CBMM Memo No.
087
J. Wang, Zhang, Z., Xie, C., Zhou, Y., Premachandran, V., Zhu, J., Xie, L., and Yuille, A., “Visual concepts and compositional voting”. 2018.PDF icon CBMM-Memo-087.pdf (3.37 MB)
CBMM Funded
M. R. Dillon and Spelke, E. S., “From Map Reading to Geometric Intuitions”, Developmental Psychology, 2018.
CBMM Related
W. Mlynarski and McDermott, J. H., “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. H., “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
K. Wu, Wu, E., and Kreiman, G., “Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition”, arXiv | Cornell University, vol. arXiv:1803.01967, 2018.
CBMM Funded
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)PDF icon CBMM-Memo-076v2.pdf (2.67 MB)
CBMM Funded
G. Ben-Yosef, Assif, L., and Ullman, S., “Full interpretation of minimal images.”, Cognition, vol. 171, pp. 65-84, 2018.PDF icon Full interpretation of minimal images.pdf (4.55 MB)
CBMM Funded
B. Katz, Borchardt, G., Felshin, S., and Mora, F., “A Natural Language Interface for Mobile Devices”, in The Wiley Handbook of Human Computer Interaction, First., vol. 2, John Wiley & Sons , 2018, pp. 539-559.
CBMM Funded
M. Araya-Polo, Jennings, J., Adler, A., and Dahlke, T., “Deep-learning tomography”, The Leading Edge, vol. 37, no. 1, pp. 58 - 66, 2018.PDF icon TLE2018.pdf (1.9 MB)
CBMM Funded
G. Ben-Yosef, Assif, L., and Ullman, S., “Full interpretation of minimal images”, Cognition, vol. 171, pp. 65 - 84, 2018.
CBMM Funded
D. Muir, Fang, X., and Meyers, E., “Brain-Observatory-Toolbox”. 2018.
CBMM Funded
A. Adler and Wax, M., “Constant Modulus Beamforming Via Low-Rank Approximation”, in 2018 IEEE Statistical Signal Processing Workshop (SSP), Freiburg im Breisgau, Germany, 2018.
CBMM Related

Pages