All Publications

2021

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
CBMM Memo No.
114
T. Poggio, “From Associative Memories to Powerful Machines”. 2021.PDF icon v1.0 (1.01 MB)PDF icon v1.3Section added August 6 on self attention (3.9 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
K. Kar and DiCarlo, J. J., “Fast Recurrent Processing via Ventrolateral Prefrontal Cortex Is Needed by the Primate Ventral Stream for Robust Core Visual Object Recognition”, Neuron, vol. 109, no. 1, pp. 164 - 176.e5, 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
M. S. Tomov, Schulz, E., and Gershman, S. J., “Multi-task reinforcement learning in humans”, Nature Human Behaviour, 2021.
CBMM Funded
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
J. Zheng, Schjetnan, A. Gómez Pal, Yebra, M., Mosher, C., Kalia, S., Valiante, T. A., Mamelak, A. N., Kreiman, G., and Rutishauser, U., “Cognitive boundary signals in the human medial temporal lobe shape episodic memory representation”, bioRxiv, 2021.
CBMM Related
CBMM Memo No.
131
C. Conwell, Mayo, D., Buice, M. A., Katz, B., Alvarez, G. A., and Barbu, A., “Neural Regression, Representational Similarity, Model Zoology Neural Taskonomy at Scale in Rodent Visual Cortex”. 2021.PDF icon CBMM-Memo-131.pdf (9.37 MB)
CBMM Funded
CBMM Memo No.
130
R. Tejwani, Kuo, Y. - L., Shu, T., Katz, B., and Barbu, A., “Social Interactions as Recursive MDPs”. 2021.PDF icon CBMM-Memo-130.pdf (1.52 MB)
CBMM Funded
CBMM Memo No.
129
Y. - L. Kuo, Katz, B., and Barbu, A., “Compositional Networks Enable Systematic Generalization for Grounded Language Understanding”. 2021.PDF icon CBMM-Memo-129.pdf (1.2 MB)
CBMM Funded
CBMM Memo No.
128
I. Palmer, Rouditchenko, A., Barbu, A., Katz, B., and Glass, J., “Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset”. 2021.PDF icon CBMM-Memo-128.pdf (2.91 MB)
CBMM Funded
CBMM Memo No.
127
Y. - L. Kuo, Barbu, A., and Katz, B., “Compositional RL Agents That Follow Language Commands in Temporal Logic”. 2021.PDF icon CBMM-Memo-127.pdf (2.12 MB)
CBMM Funded
CBMM Memo No.
126
C. Ross, Barbu, A., and Katz, B., “Measuring Social Biases in Grounded Vision and Language Embeddings”. 2021.PDF icon CBMM-Memo-126.pdf (1.32 MB)
CBMM Funded
CBMM Memo No.
117
M. Xu, Rangamani, A., Banburski, A., Liao, Q., Galanti, T., and Poggio, T., “Dynamics and Neural Collapse in Deep Classifiers trained with the Square Loss”. 2021.PDF icon v1.0 (4.61 MB)PDF icon v1.4corrections to generalization section (5.85 MB)PDF icon v1.7Small edits (22.65 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
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
C. Li and Deza, A., “What Matters In Branch Specialization? Using a Toy Task to Make Predictions”, in Shared Visual Representations in Human and Machine Intelligence (SVRHM) Workshop at NeurIPS, 2021.
CBMM Funded
A. Netanyahu, Shu, T., Katz, B., Barbu, A., and Tenenbaum, 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
B. Wang, Mayo, D., Deza, A., Barbu, A., and Conwell, C., “On the use of Cortical Magnification and Saccades as Biological Proxies for Data Augmentation”, in Shared Visual Representations in Human and Machine Intelligence (SVRHM) Workshop at NeurIPS, 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

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