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2021
Kreiman, G. Biological and Computer Vision. (Cambridge University Press, 2021). doi:10.1017/9781108649995
Traer, J., Norman-Haignere, S. & McDermott, J. H. Causal inference in environmental sound recognition. Cognition (2021).
Cohen, M. A., Ostrand, C., Frontero, N. & Pham, P. - N. Characterizing a snapshot of perceptual experience. Journal of Experimental Psychology: General (2021). doi:10.1037/xge0000864
Adler, A., Araya-Polo, M. & Poggio, T. Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows. IEEE Signal Processing Magazine 38, 89 - 119 (2021).
Banburski, A., De La Torre, F., Pant, N., Shastri, I. & 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)
Rangamani, A., Xu, M., Banburski, A., Liao, Q. & Poggio, T. Dynamics and Neural Collapse in Deep Classifiers trained with the Square Loss. (2021).PDF icon CBMM Memo 117.pdf (5.05 MB)
Kunhardt, O., Deza, A. & Poggio, T. The Effects of Image Distribution and Task on Adversarial Robustness. (2021).PDF icon CBMM_Memo_116.pdf (5.44 MB)
Griffiths, T. L. & Zaslavsky, N. Encyclopedia of Color Science and TechnologyBayesian Approaches to Color Category Learning. 1 - 5 (Springer Berlin Heidelberg, 2021). doi:10.1007/978-3-642-27851-810.1007/978-3-642-27851-8_60-9
Yang, C. et al. Evolutionary and biomedical insights from a marmoset diploid genome assemblyAbstract. Nature (2021). doi:10.1038/s41586-021-03535-x
Landi, S. M., Viswanathan, P., Serene, S. & Freiwald, W. A. A fast link between face perception and memory in the temporal pole. Science eabi6671 (2021). doi:10.1126/science.abi6671
Poggio, T. 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)
Wang, B. & Ponce, C. R. A Geometric Analysis of Deep Generative Image Models and Its Applications. Proc. International Conference on Learning Representations, 2021 (2021).
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature (2021). doi:10.1038/s41586-021-03819-2
Sani, I. et al. The human endogenous attentional control network includes a ventro-temporal cortical node. Nature Communications 12, (2021).
Yang, S., Bill, J., Drugowitsch, J. & Gershman, S. J. Human visual motion perception shows hallmarks of Bayesian structural inference. Scientific Reports 11, (2021).
Conwell, C. et al. Large-scale benchmarking of deep neural network models in mouse visual cortex reveals patterns similar to those observed in macaque visual cortex. Cosyne (2021).
Ross, C., Katz, B. & 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).
Dasgupta, I. & Gershman, S. J. Memory as a Computational Resource. Trends in Cognitive Sciences 25, 240 - 251 (2021).
Yaari, A. Uri et al. Multi-resolution modeling of a discrete stochastic process identifies causes of cancer. International Conference on Learning Representations (2021). at <https://openreview.net/forum?id=KtH8W3S_RE>
Tomov, M. S., Schulz, E. & Gershman, S. J. Multi-task reinforcement learning in humans. Nature Human Behaviour (2021). doi:10.1038/s41562-020-01035-y
Freiwald, W. A. & Hosoya, H. Neuroscience: A Face’s Journey through Space and Time. Current Biology 31, R13 - R15 (2021).
Valente, S., Marques, T. & Lima, S. Q. No evidence for prolactin’s involvement in the post-ejaculatory refractory periodAbstract. Communications Biology 4, (2021).
Netanyahu, A., Shu, T., Katz, B., Barbu, A. & Tenenabum, J. B. PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception. AAAI-21 (2021).
Du, Y., Smith, K. A., Ullman, T., Tenenbaum, J. B. & Wu, J. Unsupervised Discovery of 3D Physical Objects. International Conference on Learning Representations (2021). at <https://openreview.net/forum?id=lf7st0bJIA5>

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