All Publications

2021

CBMM Memo No.
118
T. A. Poggio, “From Marr’s Vision to the Problem of Human Intelligence”. 2021.PDF icon CBMM-Memo-118.pdf (362.19 KB)
CBMM Funded
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
J. Hu, Zaslavsky, N., and Levy, R., “Competition from novel features drives scalar inferences in reference games”, Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 43. 2021.
CBMM Related
J. Jumper, Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., Back, T., Petersen, S., Reiman, D., Clancy, E., Zielinski, M., Steinegger, M., Pacholska, M., Berghammer, T., Bodenstein, S., Silver, D., Vinyals, O., Senior, A. W., Kavukcuoglu, K., Kohli, P., and Hassabis, D., “Highly accurate protein structure prediction with AlphaFold”, Nature, 2021.
CBMM Related
S. M. Landi, Viswanathan, P., Serene, S., and Freiwald, W. A., “A fast link between face perception and memory in the temporal pole”, Science, p. eabi6671, 2021.
CBMM Funded
N. Zaslavsky, Maldonado, M., and Culbertson, J., “Let's talk (efficiently) about us: Person systems achieve near-optimal compression”, Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 43. 2021.
CBMM Related
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 The appendix is now a set of old and new remarks on topics that are not always related to the memo. (3.88 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.
117
A. Rangamani, Xu, M., Banburski, A., Liao, Q., and Poggio, T., “Dynamics and Neural Collapse in Deep Classifiers trained with the Square Loss”. 2021.PDF icon JMLR__2021-22.pdf (4.61 MB)
CBMM Funded
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)PDF icon arXiv version (23.05 MB)
CBMM Funded

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