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., Leibo, J. Z. & Poggio, T. How Important is Weight Symmetry in Backpropagation?. (2015).

1510.05067v3.pdf (615.32 KB)

Liao, Q., Leibo, J. Z. & Poggio, T. Unsupervised learning of clutter-resistant visual representations from natural videos. (2014).

1409.3879v2.pdf (3.64 MB)

Liao, Q., Kawaguchi, K. & Poggio, T. Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning. (2016).

CBMM-Memo-057.pdf (1.27 MB)

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

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

cosyne2015a.pdf (384.83 KB)

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)

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

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, 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>

Liu, S. & Spelke, E. S. Six-month-old infants represent action efficiency on a continuous scale. 9th Biennial Meeting of the Cognitive Development Society (CDS) (2015).

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

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

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

CBMM-Memo-079.pdf (10.16 MB)

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, 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).

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