Publication

Export 558 results:
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. & Janner, M. Causal and compositional generative models in online perception. 39th Annual Conference of the Cognitive Science Society (Belledonne, M., Wallraven, C., Freiwald, W. A. & Tenenbaum, J. B.) (2017).PDF icon yildirim_janner_2_1.pdf (6.88 MB)
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., 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. Henry & 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. (2017).PDF icon CBMM-Memo-064.pdf (3 MB)
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)
Dehghani, N. Design of the Artificial: lessons from the biological roots of general intelligence. (2017). at <https://arxiv.org/pdf/1703.02245>PDF icon DesignArtificial_Dehghani_arXiv.pdf (222.47 KB)
Wang, J. et al. Detecting Semantic Parts on Partially Occluded Objects. (2017).PDF icon CBMM-Memo-078.pdf (1.74 MB)
Wang, J. et al. Detecting Semantic Parts on Partially Occluded Objects. British Machine Vision Conference (BMVC) (2017). at <https://bmvc2017.london/proceedings/>

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