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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)
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
Poggio, T. 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)
Wang, B. & Ponce, C. R. A Geometric Analysis of Deep Generative Image Models and Its Applications. Proc. International Conference on Learning Representations, 2021 (2021).
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 <>
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 <>
Pouncy, T., Tsividis, P. & Gershman, S. J. What Is the Model in Model‐Based Planning?. Cognitive Science 45, (2021).