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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Liao, Q. & Poggio, T. Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex. (2016).

CBMM Memo No. 047 (1.29 MB)

Liao, Q., Leibo, J. Z. & Poggio, T. How Important Is Weight Symmetry in Backpropagation?. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) (Association for the Advancement of Artificial Intelligence, 2016).

liao-leibo-poggio.pdf (191.91 KB)

Lifshitz, I., Fetaya, E. & Ullman, S. Human Pose Estimation Using Deep Consensus Voting. ECCV 2016 (2016).

1603.08212.pdf (6.05 MB)

Lin, H. & Tegmark, M. Why does deep and cheap learning work so well?. Journal of Statistical Physics 168, 1223–1247 (2017).

1608.08225.pdf (2.14 MB)

Lin, H. & Tegmark, M. Critical Behavior from Deep Dynamics: A Hidden Dimension in Natural Language. arXiv.org (2016).

Critical Behavior from Deep Dynamics: A Hidden Dimension in Natural Language (1.64 MB)

Linderman, S. W., Stock, C. & Adams, R. A framework for studying synaptic plasticity with neural spike train data. Neural Information Processing Systems (2014).

5274-a-framework-for-studying-synaptic-plasticity-with-neural-spike-train-data.pdf (4.6 MB)

Linderman, S. W., Johnson, M. J., Wilson, M. A. & Chen, Z. A Bayesian nonparametric approach for uncovering rat hippocampal population codes during spatial navigation. Journal of Neuroscience Methods 263, (2016).

Journal of Neuroscience Methods (2.27 MB)

Linderman, S. W., Johnson, M. J., Wilson, M. A. & Chen, Z. A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation. (2014).

CBMM-Memo-027.pdf (9.44 MB)

Linderman, S. W., Adams, R. & Pillow, J. Inferring structured connectivity from spike trains under negative-binomial generalized linear models. (2015).

cosyne2015a.pdf (384.83 KB)

Liu, S. & Spelke, E. S. Continuous representations of action efficiency in infancy. CEU Conference on Cognitive Development (BCCCD16) (2016).

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., Ullman, T., Tenenbaum, J. B. & Spelke, E. S. Ten-month-old infants infer value from effort. Society for Research in Child Development (2017).

Liu, S. & Spelke, E. S. Six-month-old infants expect agents to minimize the cost of their actions. Cognition 160, 35-42 (2017).

Liu, Y. et al. Temporally delayed linear modelling (TDLM) measures replay in both animals and humans. eLife 10, (2021).

Liu, S., Ullman, T. D., Tenenbaum, J. B. & Spelke, E. S. Ten-month-old infants infer the value of goals from the costs of actions. Science 358, 1038-1041 (2017).

ivc_full_preprint_withsm.pdf (1.6 MB)

Liu, S. Nature and origins of intuitive psychology in human infants. (2020).

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

CBMM-Memo-079.pdf (10.16 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., 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)

Liu, S., Brooks, N. B. & Spelke, E. S. Pre-reaching infants expect causal agents to act efficiently without motor training. 20th Biennial International Conference on Infant Studies (ICIS) (2016).

Liu, S. et al. Dangerous Ground: One-Year-Old Infants are Sensitive to Peril in Other Agents’ Action PlansAbstract. Open Mind 6, 211 - 231 (2022).

Liu, C., Mao, J., Sha, F. & Yuille, A. Attention Correctness in Neural Image Captioning. AAAI 2017 (2017).

1605.09553.pdf (2.22 MB)

Liu, S., Ullman, T., Tenenbaum, J. B. & Spelke, E. S. Ten-month-old infants infer value from effort. SRCD (2017).

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)

Liu, H., Agam, Y., Madsen, J. & Kreiman, G. Timing, timing, timing: Fast decoding of object inforrmation from intracranial field potentials in human visual cortex. (2009). at <http://klab.tch.harvard.edu/resources/liuetal_timing3.html>

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