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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2019

Liu, S., Cushman, F. A., Gershman, S. J., Kool, W. & Spelke, E. S. Hard choices: Children’s understanding of the cost of action selection. . Cognitive Science Society (2019).

phk_cogsci_2019_final.pdf (276.14 KB)

Leonard, J. A., Garcia, A. & Schulz, L. How Adults’ Actions, Outcomes, and Testimony Affect Preschoolers’ Persistence. Child Development (2019). doi:10.1111/cdev.13305

Idiart, M. A. P., Villavicencio, A., Katz, B., Rennó-Costa, C. & Lisman, J. How Does the Brain Represents Language and Answers Questions? Using an AI System to Understand the Underlying Neurobiological Mechanisms. Frontiers in Computational Neuroscience 13, (2019).

Jozwik, K. M., Lee, M., Marques, T., Schrimpf, M. & Bashivan, P. Large-scale hyperparameter search for predicting human brain responses in the Algonauts challenge. The Algonauts Project: Explaining the Human Visual Brain Workshop 2019 (2019). doi:10.1101/689844

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)

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)

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., McCoy, J. P. & Ullman, T. D. People's perceptions of others’ risk preferences. Cognitive Science Society (2019).

risk_cogsci_2019_final.pdf (899.8 KB)

Chu, J., Gauthier, J., Levy, R., Tenenbaum, J. B. & Schulz, L. Query-guided visual search . 41st Annual conference of the Cognitive Science Society (2019).

Poggio, T., Banburski, A. & Liao, Q. Theoretical Issues in Deep Networks. (2019).

CBMM Memo 100 v1 (1.71 MB)

CBMM Memo 100 v3 (8/25/2019) (1.31 MB)

CBMM Memo 100 v4 (11/19/2019) (1008.23 KB)

Liao, Q., Banburski, A. & Poggio, T. Theories of Deep Learning: Approximation, Optimization and Generalization . TECHCON 2019 (2019).

Banburski, A. et al. Weight and Batch Normalization implement Classical Generalization Bounds . ICML (2019).

2018

Berzak, Y., Katz, B. & Levy, R. Assessing Language Proficiency from Eye Movements in Reading. 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2018). at <http://naacl2018.org/>

1804.07329.pdf (350.43 KB)

Xiao, W., Chen, H., Liao, Q. & Poggio, T. Biologically-plausible learning algorithms can scale to large datasets. (2018).

CBMM-Memo-092.pdf (1.31 MB)

Liao, Q., Miranda, B., Hidary, J. & Poggio, T. Classical generalization bounds are surprisingly tight for Deep Networks. (2018).

CBMM-Memo-091.pdf (1.43 MB)

CBMM-Memo-091-v2.pdf (1.88 MB)

Livingstone, M. S., Arcaro, M. J. & Schade, P. F. Cortex Is Cortex: Ubiquitous Principles Drive Face-Domain Development. Trends in Cognitive Sciences (2018). doi:10.1016/j.tics.2018.10.009

1-s2.0-S1364661318302572-main.pdf (260.4 KB)

Yuille, A. & Liu, C. Deep Nets: What have they ever done for Vision?. (2018).

CBMM-Memo-088.pdf (7.88 MB)

Zhang, M. et al. Finding any Waldo with zero-shot invariant and efficient visual search. Nature Communications 9, (2018).

O'Brien, N., Latessa, S., Evangelopoulos, G. & Boix, X. The Language of Fake News: Opening the Black-Box of Deep Learning Based Detectors. workshop on "AI for Social Good", NIPS 2018 (2018). at <http://hdl.handle.net/1721.1/120056>

fake-news-paper-NIPS.pdf (147.36 KB)

fake-news-paper-NIPS_2018_v2.pdf (147.36 KB)

Gerstenberg, T. et al. Lucky or clever? From changed expectations to attributions of responsibility. Cognition (2018).

Lotter, W., Kreiman, G. & Cox, D. A neural network trained to predict future videoframes mimics critical properties of biologicalneuronal responses and perception. ( arXiv | Cornell University, 2018). at <https://arxiv.org/pdf/1805.10734.pdf>

1805.10734.pdf (9.59 MB)

Tang, H. et al. Recurrent computations for visual pattern completion. Proceedings of the National Academy of Sciences (2018). doi:10.1073/pnas.1719397115

1719397115.full_.pdf (1.1 MB)

Liu, C. et al. Recurrent Multimodal Interaction for Referring Image Segmentation. (2018).

CBMM-Memo-079.pdf (10.16 MB)

Liu, C. et al. Recurrent Multimodal Interaction for Referring Image Segmentation. (2018).

CBMM-Memo-079.pdf (10.16 MB)

Liu, C. et al. Recurrent Multimodal Interaction for Referring Image Segmentation. (2018).

CBMM-Memo-079.pdf (10.16 MB)

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