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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Found 910 results

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Lewis, O. & Poggio, T. From Neuron to Cognition via Computational Neuroscience (The MIT Press, 2016). at <https://mitpress.mit.edu/neuron-cognition>

Rosasco, L. Object recognition data sets (iCub/IIT). (2013).

Barbu, A. et al. ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models. Neural Information Processing Systems (NeurIPS 2019) (2019).

9142-objectnet-a-large-scale-bias-controlled-dataset-for-pushing-the-limits-of-object-recognition-models.pdf (16.31 MB)

Liao, Q. & Poggio, T. Object-Oriented Deep Learning. (2017).

CBMM-Memo-070.pdf (963.54 KB)

Kamps, F. S., Julian, J. B., Kubilius, J., Kanwisher, N. & Dilks, D. D. The occipital place area represents the local elements of scenes. NeuroImage 132, 417 - 424 (2016).

Wong, A. & Yuille, A. One Shot Learning by Composition of Meaningful Patches. International Conference on Computer Vision (ICCV) (2015).

AlexWongOneShotCVPR2015.pdf (1.83 MB)

Wong, A. & Yuille, A. One Shot Learning via Compositions of Meaningful Patches. International Conference on Computer Vision (ICCV) (2015).

AlexWongOneShotCVPR2015.pdf (1.83 MB)

Casper, S., Nadeau, M. & Kreiman, G. One thing to fool them all: generating interpretable, universal, and physically-realizable adversarial features. arXiv (2022). doi:10.48550/arXiv.2110.03605

2110.03605.pdf (6.7 MB)

Wu, Y., Muentener, P. & Schulz, L. One- to Four-year-olds’ Ability to Connect Diverse Positive Emotional Expressions to Their Probable Causes . Society for Research in Child Development (2017).

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

Online learning of symbolic concepts. (2017).

Brewer, K., Mittman, B., Kominsky, J. & Henes, J. Open Source Subject Database Project (OSSDP). (2019).

Rando, M., Molinari, C., Villa, S. & Rosasco, L. An Optimal Structured Zeroth-order Algorithm for Non-smooth Optimization. 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (2023). at <https://proceedings.neurips.cc/paper_files/paper/2023/file/7429f4c1b267cf619f28c4d4f1532f99-Paper-Conference.pdf>

Newman, J. P. et al. Optogenetic feedback control of neural activity. Elife 4, e07192 (2015).

elife-07192-v1-download.pdf (5.92 MB)

Deen, B. et al. Organization of high-level visual cortex in human infants. Nature Communications (2017). doi:10.1038/ncomms13995

Gershman, S. J. Origin of perseveration in the trade-off between reward and complexity. Cognition 204, 104394 (2020).

Liu, S., Brooks, N. B. & Spelke, E. S. Origins of the concepts cause, cost, and goal in prereaching infants. Cognitive Development Society (2019).

liu_etal_lumi_cds2019_final.pdf (22.95 MB)

Liu, S., Brooks, N. B. & Spelke, E. S. Origins of the concepts cause, cost, and goal in prereaching infants. PNAS (2019). doi:https://doi.org/10.1073/pnas.1904410116

Author's last draft (2.58 MB)

Penagos, H., Varela, C. & Wilson, M. A. Oscillations, neural computations and learning during wake and sleep. Current Opinion in Neurobiology 44C, (2017).

Rockmore, D. Our Mother the Machine, by Dan Rockmore [Huffpost] . (2015). at <http://www.huffingtonpost.com/dan-rockmore/our-mother-the-machine_b_7273504.html>

Our Mother the Machine.pdf (199.73 KB)

Xiao, W., Sharma, S., Kreiman, G. & Livingstone, M. S. Out of sight, out of mind: Responses in primate ventral visual cortex track individual fixations during natural vision. bioRxiv (2023). doi:10.1101/2023.02.08.527666

Poggio, T. & Banburski, A. An Overview of Some Issues in the Theory of Deep Networks. IEEJ Transactions on Electrical and Electronic Engineering 15, 1560 - 1571 (2020).

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Chen, X. & Yuille, A. Parsing Occluded People by Flexible Compositions. Computer Vision and Pattern Recognition (CVPR) (2015).

CBMM Memo 034.pdf (5.54 MB)

Lu, W., Lian, X. & Yuille, A. Parsing Semantic Parts of Cars Using Graphical Models and Segment Appearance Consistency. (2014).

CBMM-Memo-018_opt.pdf (5.02 MB)

Bass, I., Smith, K. A., Bonawitz, E. & Ullman, T. Partial Mental Simulation Explains Fallacies in Physical Reasoning. psyArXiv (2021). at <https://psyarxiv.com/y4a8x>

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