All Publications

2021

A. Uri Yaari, Sherman, M., Priebe, O. Clarke, Loh, P. - R., Katz, B., Barbu, A., and Berger, B., “Multi-resolution modeling of a discrete stochastic process identifies causes of cancer”, in International Conference on Learning Representations, 2021.
CBMM Funded
Y. Du, Smith, K. A., Ullman, T., Tenenbaum, J. B., and Wu, J., “Unsupervised Discovery of 3D Physical Objects”, in International Conference on Learning Representations, 2021.
CBMM Related
C. Yang, Zhou, Y., Marcus, S., Formenti, G., Bergeron, L. A., Song, Z., Bi, X., Bergman, J., Rousselle, M. Marie C., Zhou, C., Zhou, L., Deng, Y., Fang, M., Xie, D., Zhu, Y., Tan, S., Mountcastle, J., Haase, B., Balacco, J., Wood, J., Chow, W., Rhie, A., Pippel, M., Fabiszak, M. M., Koren, S., Fedrigo, O., Freiwald, W. A., Howe, K., Yang, H., Phillippy, A. M., Schierup, M. Heide, Jarvis, E. D., and Zhang, G., “Evolutionary and biomedical insights from a marmoset diploid genome assemblyAbstract”, Nature, 2021.
CBMM Funded
A. Adler, Araya-Polo, M., and Poggio, T., “Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows”, IEEE Signal Processing Magazine, vol. 38, no. 2, pp. 89 - 119, 2021.
CBMM Funded
I. Dasgupta and Gershman, S. J., “Memory as a Computational Resource”, Trends in Cognitive Sciences, vol. 25, no. 3, pp. 240 - 251, 2021.
CBMM Funded
J. Traer, Norman-Haignere, S., and McDermott, J. H., “Causal inference in environmental sound recognition”, Cognition, 2021.
CBMM Funded
CBMM Memo No.
116
O. Kunhardt, Deza, A., and Poggio, T., “The Effects of Image Distribution and Task on Adversarial Robustness”. 2021.PDF icon CBMM_Memo_116.pdf (5.44 MB)
CBMM Funded
M. A. Cohen, Ostrand, C., Frontero, N., and Pham, P. - N., “Characterizing a snapshot of perceptual experience.”, Journal of Experimental Psychology: General, 2021.
CBMM Funded
G. Kreiman, Biological and Computer Vision. Cambridge, UK: Cambridge University Press, 2021.
CBMM Related
I. Sani, Stemmann, H., Caron, B., Bullock, D., Stemmler, T., Fahle, M., Pestilli, F., and Freiwald, W. A., “The human endogenous attentional control network includes a ventro-temporal cortical node”, Nature Communications, vol. 12, no. 1, 2021.
CBMM Funded
S. Yang, Bill, J., Drugowitsch, J., and Gershman, S. J., “Human visual motion perception shows hallmarks of Bayesian structural inference”, Scientific Reports, vol. 11, no. 1, 2021.
CBMM Funded
M. S. Tomov, Schulz, E., and Gershman, S. J., “Multi-task reinforcement learning in humans”, Nature Human Behaviour, 2021.
CBMM Funded
CBMM Memo No.
114
T. Poggio, “From Associative Memories to Powerful Machines”. 2021.PDF icon CBMM-Memo-114.pdf (1.01 MB)PDF icon Adding a second part (1.85 MB)
CBMM Funded
W. A. Freiwald and Hosoya, H., “Neuroscience: A Face’s Journey through Space and Time”, Current Biology, vol. 31, no. 1, pp. R13 - R15, 2021.
CBMM Funded
T. Pouncy, Tsividis, P., and Gershman, S. J., “What Is the Model in Model‐Based Planning?”, Cognitive Science, vol. 45, no. 1, 2021.
CBMM Funded
T. L. Griffiths and Zaslavsky, N., Encyclopedia of Color Science and TechnologyBayesian Approaches to Color Category Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2021, pp. 1 - 5.
CBMM Related
S. Valente, Marques, T., and Lima, S. Q., “No evidence for prolactin’s involvement in the post-ejaculatory refractory periodAbstract”, Communications Biology, vol. 4, no. 1, 2021.
CBMM Related
B. Wang and Ponce, C. R., “A Geometric Analysis of Deep Generative Image Models and Its Applications”, in Proc. International Conference on Learning Representations, 2021, 2021.
CBMM Related
CBMM Memo No.
115
A. Banburski, De La Torre, F., Pant, N., Shastri, I., and Poggio, T., “Distribution of Classification Margins: Are All Data Equal?”. 2021.PDF icon CBMM Memo 115.pdf (9.56 MB)
CBMM Funded
C. Conwell, Mayo, D., Buice, M., Katz, B., Alvarez, G., and Barbu, A., “Large-scale benchmarking of deep neural network models in mouse visual cortex reveals patterns similar to those observed in macaque visual cortex”, Cosyne. 2021.
CBMM Funded
A. Netanyahu, Shu, T., Katz, B., Barbu, A., and Tenenabum, J. B., “PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception”, in AAAI-21, 2021.
CBMM Funded
C. Ross, Katz, B., and Barbu, A., “Measuring Social Biases in Grounded Vision and Language Embeddings”, NAACL (Annual Conference of the North American Chapter of the Association for Computational Linguistics). 2021.
CBMM Funded

2020

T. Poggio, Banburski, A., and Liao, Q., “Theoretical issues in deep networks”, Proceedings of the National Academy of Sciences, p. 201907369, 2020.PDF icon PNASlast.pdf (915.3 KB)
CBMM Funded
L. Isik, Mynick, A., Pantazis, D., and Kanwisher, N., “The speed of human social interaction perception”, NeuroImage, p. 116844, 2020.
CBMM Funded
H. Richardson, Gweon, H., Dodell-Feder, D., Malloy, C., Pelton, H., Keil, B., Kanwisher, N., and Saxe, R., “Response patterns in the developing social brain are organized by social and emotion features and disrupted in children diagnosed with autism spectrum disorder”, Cortex, vol. 125, pp. 12 - 29, 2020.
CBMM Funded

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