Publications

CBMM Memos were established in 2014 as a mechanism for our center to share research results with the wider scientific community. Click here to read more about the memos and to see a full list of the memos.

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Sliwa, J., Marvel, S. R. & Freiwald, W. A. Comparing human and monkey neural circuits for processing social scenes. Society for Neuroscience's Annual Meeting - SfN 2017 (2017).

Sliwa, J., Marvel, S. R., Ianni, G. A. & Freiwald, W. A. Comparing human and monkey neural circuits for processing social scenes. Société Francophone de Primatologie (SFDP) Annual Meeting, Paris, France (2018).

Sliwa, J., Marvel, S. R., Ianni, G. A. & Freiwald, W. A. Comparing human and monkey neural circuits for processing social scenes. Cognitive Neuroscience Society Annual Meeting (CNS), Boston, MA (2018).

Hu, J., Zaslavsky, N. & Levy, R. Competition from novel features drives scalar inferences in reference games. Proceedings of the Annual Meeting of the Cognitive Science Society 43, (2021).

Poggio, T., Liao, Q. & Banburski, A. Complexity Control by Gradient Descent in Deep Networks. Nature Communications 11, (2020).

s41467-020-14663-9.pdf (431.68 KB)

Yuille, A. & Mottaghi, R. Complexity of Representation and Inference in Compositional Models with Part Sharing. (2015).

CBMM Memo 031.pdf (1.14 MB)

Yu, H., Siddharth, N., Barbu, A. & Siskind, J. Mark. A Compositional Framework for Grounding Language Inference, Generation, and Acquisition in Video. (2015). doi:doi:10.1613/jair.4556

Schulz, E., Tenenbaum, J. B., Duvenaud, D., Speekenbrink, M. & Gershman, S. J. Compositional inductive biases in function learning. Cogn Psychol 99, 44-79 (2017).

Barbu, A. et al. The Compositional Nature of Event Representations in the Human Brain. (2014).

CBMM Memo 011.pdf (3.95 MB)

Kuo, Y. - L., Katz, B. & Barbu, A. Compositional Networks Enable Systematic Generalization for Grounded Language Understanding. (2021).

CBMM-Memo-129.pdf (1.2 MB)

Kuo, Y. - L., Barbu, A. & Katz, B. Compositional RL Agents That Follow Language Commands in Temporal Logic. (2021).

CBMM-Memo-127.pdf (2.12 MB)

Kuo, Y. - L., Katz, B. & Barbu, A. Compositional RL Agents That Follow Language Commands in Temporal Logic. Frontiers in Robotics and AI 8, (2021).

frobt-08-689550.pdf (1.57 MB)

Poggio, T. & Fraser, M. Compositional Sparsity of Learnable Functions. (2024).

This is an update of the AMS paper (230.72 KB)

Poggio, T. & Fraser, M. Compositional sparsity of learnable functions. Bulletin of the American Mathematical Society 61, 438-456 (2024).

Zisselman, E., Adler, A. & Elad, M. Handbook of Numerical Analysis 19, 3 - 17 (Elsevier, 2018).

Chandrasekhar, V. et al. Compression of Deep Neural Networks for Image Instance Retrieval. (2017). at <https://arxiv.org/abs/1701.04923>

1701.04923.pdf (614.33 KB)

N. Murty, A. Ratan, Bashivan, P., Abate, A., DiCarlo, J. J. & Kanwisher, N. Computational models of category-selective brain regions enable high-throughput tests of selectivity. Nature Communications 12, (2021).

s41467-021-25409-6.pdf (6.47 MB)

Kreiman, G. Principles of neural coding (2013).

Dehghani, N. & Wimmer, R. A computational perspective of the role of Thalamus in cognition. arxiv (2018). at <https://arxiv.org/abs/1803.00997>

ThalamusComputationArxiv.pdf (5.12 MB)

Kar, K. A computational probe into the behavioral and neural markers of atypical facial emotion processing in autism. The Journal of Neuroscience JN-RM-2229-21 (2022). doi:10.1523/JNEUROSCI.2229-21.2022

Gershman, S. J., Horvitz, E. J. & Tenenbaum, J. B. Computational rationality: A converging paradigm for intelligence in brains, minds, and machines. Science 349, 273-278 (2015).

Poggio, T., Mutch, J. & Isik, L. Computational role of eccentricity dependent cortical magnification. (2014).

CBMM-Memo-017.pdf (1.04 MB)

Goodman, N. D., Tenenbaum, J. B. & Gerstenberg, T. Concepts in a Probabilistic Language of Thought. (2014).

CBMM-Memo-010.pdf (902.53 KB)

Xiang, Y., Graeber, T., Enke, B. & Gershman, S. J. Confidence and central tendency in perceptual judgment. Attention, Perception, & Psychophysics 83, 3024 - 3034 (2021).

Dillon, M. R. & Spelke, E. S. Connecting core cognition, spatial symbols, and the abstract concepts of formal geometry. Cognitive Development Society Post-Conference, More on Development (2015).

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