Publication

Found 195 results
Author Title Type [ Year(Desc)]
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2015
Nickel, M., Murphy, K., Tresp, V. & Gabrilovich, E. A Review of Relational Machine Learning for Knowledge Graphs: From Multi-Relational Link Prediction to Automated Knowledge Graph Construction. (2015).PDF icon CBMM Memo No. 028 (878.56 KB)
Powell, L. J., Deen, B., Guo, L. & Saxe, R. Using fNIRS to Map Functional Specificity in the Infant Brain: An fROI Approach. (2015).PDF icon SRCD2015_NIRS_poster.pdf (2.14 MB)
2016
Gómez-Laberge, C., Smolyanskaya, A., Nassi, J. J., Kreiman, G. & Born, R. T. Bottom-up and Top-down Input Augment the Variability of Cortical Neurons. Neuron 91(3), 540-547 (2016).
Lake, B. M., Ullman, T., Tenenbaum, J. B. & Gershman, S. J. Building machines that learn and think like people. (2016).PDF icon machines_that_think.pdf (3.45 MB)
Gerstenberg, T. & Tenenbaum, J. B. Oxford Handbook of Causal Reasoning (Oxford University Press, 2016).PDF icon Intuitive Theories (Gerstenberg, Tenenbaum, 2016.pdf (6.06 MB)
Jara-Ettinger, J., Piantadosi, S., Spelke, E. S., Levy, R. & Gibson, E. Mastery of the logic of natural numbers is not the result of mastery of counting: Evidence from late counters. . Developmental Science (2016). doi:10.1111/desc.12459
Harari, D., Gao, T., Kanwisher, N., Tenenbaum, J. B. & Ullman, S. Measuring and modeling the perception of natural and unconstrained gaze in humans and machines. (2016).PDF icon CBMM-Memo-059.pdf (1.71 MB)
Jara-Ettinger, J., Gweon, H., Schulz, L. & Tenenbaum, J. B. The naive utility calculus: computational principles underlying social cognition. Trends Cogn Sci. (2016). doi:10.1016/j.tics.2016.05.011
Bramley, N., Gerstenberg, T. & Tenenbaum, J. B. Natural science: Active learning in dynamic physical microworlds. 38th Annual Meeting of the Cognitive Science Society (2016).PDF icon Natural Science (Bramley, Gerstenberg, Tenenbaum, 2016).pdf (5.39 MB)
Bergen, L., Levy, R. & Goodman, N. D. Pragmatic Reasoning through Semantic Inference. Semantics and Pragmatics Vol 9 (2016) , (2016).PDF icon BergenLevyGoodman2015.pdf (1.12 MB)
Schulz, E., Tenenbaum, J. B., Duvenaud, D., Speekenbrink, M. & Gershman, S. J. Probing the compositionality of intuitive functions. (2016).PDF icon CBMM-Memo-048.pdf (815.72 KB)
Nickel, M., Murphy, K., Tresp, V. & Gabrilovich, E. A Review of Relational Machine Learning for Knowledge Graphs. Proceedings of the IEEE 104, 11 - 33 (2016).PDF icon 1503.00759v3.pdf (1.53 MB)
Miconi, T., Groomes, L. & Kreiman, G. There’s Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task [dataset]. (2016).
Miconi, T., Groomes, L. & Kreiman, G. There’s Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task [code]. (2016).
Miconi, T., Groomes, L. & Kreiman, G. There's Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task. Cerebral Cortex 26(7), 26:3064-3082 (2016).
Berzak, Y. et al. Treebank of Learner English (TLE). (2016). at <http://esltreebank.org/>PDF icon acl2016.pdf (163.86 KB)
Chen, Z., Grosmark, A. D., Penagos, H. & Wilson, M. A. Uncovering representations of sleep-associated hippocampal ensemble spike activity. Scientific Reports 6, (2016).
Gerstenberg, T. & Tenenbaum, J. B. Understanding "almost": Empirical and computational studies of near misses. 38th Annual Meeting of the Cognitive Science Society (2016).PDF icon Understanding almost (Gerstenberg, Tenenbaum, 2016).pdf (4.08 MB)
Berzak, Y. et al. Universal Dependencies for Learner English. (2016).PDF icon memo-52_rev1.pdf (472.67 KB)
Kool, W., Cushman, F. A. & Gershman, S. J. When Does Model-Based Control Pay Off?. PLoS Comput Biol 12, e1005090 (2016).PDF icon KoolEtAl_PLOS_CB.PDF (5.85 MB)
Dasgupta, I., Schulz, E. & Gershman, S. J. Where do hypotheses come from?. (2016).PDF icon CBMM-Memo-056-v2.pdf (733.35 KB)
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).
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)
Schulz, E., Tenenbaum, J. B., Duvenaud, D., Speekenbrink, M. & Gershman, S. J. Compositional inductive biases in function learning. Cogn Psychol 99, 44-79 (2017).
Kool, W., Gershman, S. J. & Cushman, F. A. Cost-Benefit Arbitration Between Multiple Reinforcement-Learning Systems. Psychol Sci 28, 1321-1333 (2017).

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