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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Schrimpf, M. & Kubilius, J. Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?. bioRxiv preprint (2018). doi:10.1101/407007

Brain-Score bioRxiv.pdf (789.83 KB)

Schulz, E., Quiroga, F. & Gershman, S. J. Communicating Compositional Patterns. Open Mind 4, 25 - 39 (2020).

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

Schulz, E., Tenenbaum, J. B., Duvenaud, D., Speekenbrink, M. & Gershman, S. J. Probing the compositionality of intuitive functions. (2016).

CBMM-Memo-048.pdf (815.72 KB)

Schwartz, J. et al. ThreeDWorld (TDW): A High-Fidelity, Multi-Modal Platform for Interactive Physical Simulation. (2020). at <http://www.threedworld.org/>

Schwettmann, S., Tenenbaum, J. B. & Kanwisher, N. Invariant representations of mass in the human brain. eLife 8, (2019).

Schwiedrzik, C. M., Zarco, W., Everling, S. & Freiwald, W. A. Face Patch Resting State Networks Link Face Processing to Social Cognition. PLoS Biology 13, e1002245 (2015).

Scott, K. M., Chu, J. & Schulz, L. Lookit (Part 2): Assessing the viability of online developmental research, Results from three case studies. Open Mind 1, (2017).

lookitpart2.pdf (464.02 KB)

Scott, K. Moving the lab home: validation of a web-based system for developmental studies. (2015).

Scott, K. M. & Schulz, L. Lookit (Part 1): a new online platform for developmental research. Open Mind 1, (2017).

UNCORRECTED PROOF (561.21 KB)

Serrino, J., Kleiman-Weiner, M., Parkes, D. C. & Tenenbaum, J. B. Finding Friend and Foe in Multi-Agent Games. Neural Information Processing Systems (NeurIPS 2019) (2019).

Max KW paper.pdf (928.96 KB)

Shaham, N., Chandra, J., Kreiman, G. & Sompolinsky, H. Stochastic consolidation of lifelong memoryAbstract. Scientific Reports 12, (2022).

s41598-022-16407-9.pdf (2.54 MB)

Shalev-Shwartz, S. & Shashua, A. Can we Contain Covid-19 without Locking-down the Economy?. (2020).

CBMM Memo 104 v4 (Apr. 6, 2020) (418.25 KB)

CBMM Memo 104 v3 (Apr. 1, 2020) (452.94 KB)

CBMM Memo 104 v2 (Mar. 28, 2020) (427.39 KB)

CBMM-Memo-104.pdf (425.12 KB)

Shalev-Shwartz, S. & Shashua, A. An Exit Strategy from the Covid-19 Lockdown based on Risk-sensitive Resource Allocation. (2020).

CBMM-Memo-106.pdf (431.13 KB)

Griffiths, T. L. & Zaslavsky, N. Encyclopedia of Color Science and TechnologyBayesian Approaches to Color Category Learning. 1 - 5 (Springer Berlin Heidelberg, 2021). doi:10.1007/978-3-642-27851-8

Shen, W. et al. Deep Regression Forests for Age Estimation. (2018).

CBMM-Memo-085.pdf (2.2 MB)

Shen, W., Wang, B., Jiang, Y., Wang, Y. & Yuille, A. Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection. (2017).

CBMM-Memo-080.pdf (2.51 MB)

Sherman, M. A. et al. Genome-wide mapping of somatic mutation rates uncovers drivers of cancerAbstract. Nature Biotechnology 40, 1634 - 1643 (2022).

Sheskin, M. et al. Online Developmental Science to Foster Innovation, Access, and Impact. Trends in Cognitive Sciences 24, 675 - 678 (2020).

Shrobe, H., Katz, B. & Davis, R. Towards a Programmer's Apprentice (Again). (2015).

CBMM-memo-030.pdf (294.27 KB)

Shu, T. et al. AGENT: A Benchmark for Core Psychological Reasoning. Proceedings of the 38th International Conference on Machine Learning (2021).

Siddharth, N., Barbu, A. & Siskind, J. Mark. Seeing What You’re Told: Sentence-Guided Activity Recognition In Video. CVPR (IEEE, 2014).

Publication (453.54 KB)

Siddharth, N., Barbu, A. & Siskind, J. Mark. Seeing what you're told, sentence guided activity recognition in video. Appeared at CVPR (2014).

poster-1701.pdf (4.61 MB)

Siddharth, N., Barbu, A. & Siskind, J. Mark. Seeing What You’re Told: Sentence-Guided Activity Recognition In Video. (2014).

CBMM-Memo-006.pdf (1.2 MB)

Siegle, J. H., Hale, G. J., Newman, J. P. & Voigts, J. Neural ensemble communities: open-source approaches to hardware for large-scale electrophysiology. Current Opinion in Neurobiology 32, 53 - 59 (2015).

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