Publication

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2017
A del Molino, G., Boix, X., Lim, J. & Tan, A. Active Video Summarization: Customized Summaries via On-line Interaction. AAAI Conference on Artificial Intelligence (2017).PDF icon 21-Garcia-del-Molino-14856.pdf (413.77 KB)
Mlynarski, W. & McDermott, J. H. Adaptive Compression of Statistically Homogenous Sensory Signals. Computational and Systems Neuroscience (COSYNE) (2017).
Liu, C., Mao, J., Sha, F. & Yuille, A. Attention Correctness in Neural Image Captioning. AAAI 2017 (2017).PDF icon 1605.09553.pdf (2.22 MB)
Traer, J. & McDermott, J. H. Auditory Perception of Material and Force from Impact Sounds. Annual Meeting of Association for Research in Otolaryngology (2017).
N. Murty, A. Ratan & Arun, S. P. A Balanced Comparison of Object Invariances in Monkey IT Neurons. eneuro 4, ENEURO.0333-16.2017 (2017).
Lake, B. M., Ullman, T. D., Tenenbaum, J. B. & Gershman, S. J. Building machines that learn and think like people. Behavioral and Brain Sciences 40, e253 (2017).
Yildirim, I. et al. Causal and compositional generative models in online perception. 39th Annual Meeting of the Cognitive Science Society - COGSCI 2017 (2017). at <https://mindmodeling.org/cogsci2017/papers/0266/index.html>
Bramley, N. R., Mayrhofer, R., Gerstenberg, T. & Lagnado, D. A. Causal learning from interventions and dynamics in continuous time. Cognitive Science Conference (2017).PDF icon Bramley et al. - 2017 - Causal learning from interventions and dynamics in.pdf (1.78 MB)
Sadagopan, S., Zarco, W. & Freiwald, W. A. A Causal Relationship Between Face-Patch Activity and Face-Detection Behavior. eLife (2017). doi:https://doi.org/10.7554/eLife.18558.001PDF icon elife-18558-v1.pdf (813.71 KB)
Magid, R., Yan, P., Siegel, M., Tenenbaum, J. B. & Schulz, L. Changing minds: Children’s inferences about third party belief revision. Developmental Science e12553 (2017). doi:10.1111/desc.12553PDF icon Changing Minds_MagidYanSiegelTenenbaumSchulz_in press.pdf (915.8 KB)
Winston, P. & Holmes, D. Character-building stories. Advances in Cognitive Systems (2017).
Jara-Ettinger, J., Floyd, S., Tenenbaum, J. B. & Schulz, L. Children understand that agents maximize expected utilities. Journal of Experimental Psychology: General 146, 1574 - 1585 (2017).PDF icon ExpectedUtilities_Final.pdf (950.09 KB)
Sliwa, J., Marvel, S. R. & Freiwald, W. A. Comparing human and monkey neural circuits for processing social scenes. Society for Neuroscience's Annual Meeting - SfN 2017 (2017).
Schulz, E., Tenenbaum, J. B., Duvenaud, D., Speekenbrink, M. & Gershman, S. J. Compositional inductive biases in function learning. Cogn Psychol 99, 44-79 (2017).
Chandrasekhar, V. et al. Compression of Deep Neural Networks for Image Instance Retrieval. (2017). at <https://arxiv.org/abs/1701.04923>PDF icon 1701.04923.pdf (614.33 KB)
Kool, W., Gershman, S. J. & Cushman, F. A. Cost-Benefit Arbitration Between Multiple Reinforcement-Learning Systems. Psychol Sci 28, 1321-1333 (2017).
Spokes, A. C. & Spelke, E. S. The cradle of social knowledge: Infants' reasoning about caregiving and affiliation. Cognition 159, 102-116 (2017).
Ullman, T., Tenenbaum, J. B. & Spelke, E. S. Critical Cues in Early Physical Reasoning. SRCD (2017).
Meyers, E. A Data Science approach to analyzing neural data. Joint Statistical Meetings (2017).
Chen, Z. & Wilson, M. A. Deciphering neural codes of memory during sleep. Trends in Neurosciences (2017).PDF icon proof (2.98 MB)
Sliwa, J. & Freiwald, W. A. A Dedicated Network for Social Interaction Processing in the Primate Brain. Science Vol. 356, pp. 745-749 (2017).
Lotter, W., Kreiman, G. & Cox, D. Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. ICLR (2017).PDF icon 1605.08104.pdf (2.9 MB)
Lotter, W., Kreiman, G. & Cox, D. Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. (2017).PDF icon CBMM-Memo-064.pdf (3 MB)

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