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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)
Morère, O., Veillard, A., Chandrasekhar, V. & Poggio, T. Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval. (2016). at <>PDF icon 1603.04595.pdf (2.9 MB)
Kreiman, G. Neural Information Processing Systems (NIPS) 2015 Review. (2016).PDF icon Read the Views & Review article by Gabriel Kreiman (443.87 KB)
Robertson, C. E., Hermann, K., Mynick, A., Kravitz, D. J. & Kanwisher, N. Neural Representations Integrate the Current Field of View with the Remembered 360° Panorama. Current Biology (2016). doi:10.1016/j.cub.2016.07.002
Tan, C. & Poggio, T. Neural Tuning Size in a Model of Primate Visual Processing Accounts for Three Key Markers of Holistic Face Processing. Public Library of Science | PLoS ONE 1(3): e0150980, (2016).PDF icon journal.pone_.0150980.PDF (384.15 KB)
Meyers, E., Dean, M. & Hale, G. J. New Data Science tools for analyzing neural data and computational models. Society for Neuroscience (2016).
Lewis, O. & Poggio, T. From Neuron to Cognition via Computational Neuroscience (The MIT Press, 2016). at <>
Kamps, F. S., Julian, J. B., Kubilius, J., Kanwisher, N. & Dilks, D. D. The occipital place area represents the local elements of scenes. NeuroImage 132, 417 - 424 (2016).
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)
Tang, H. et al. Predicting episodic memory formation for movie events. Scientific Reports (2016). doi:10.1038/srep30175
Tang, H. et al. Predicting episodic memory formation for movie events [code]. (2016).
Tang, H. et al. Predicting episodic memory formation for movie events [dataset]. (2016).
Lotter, W., Kreiman, G. & Cox, D. PredNet - "Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning" [code]. (2016).
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).
Kosakowski, H. L., Powell, L. J. & Spelke, E. S. Preverbal Infants' Third-Party Imitator Preferences: Animated Displays versus Filmed Actors. International Conference on Infant Studies (ICIS) (2016).PDF icon Preverbal Infants' Third-Party Imitator Preferences: Animated Displays versus Filmed Actors (45.21 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)
Peres, F., Smith, K. A. & Tenenbaum, J. B. Rapid Physical Predictions from Convolutional Neural Networks. Neural Information Processing Systems, Intuitive Physics Workshop (2016). at <>PDF icon Rapid Physical Predictions - NIPS Physics Workshop Poster (1.47 MB)
Ben-Yosef, G., Yachin, A. & Ullman, S. Recognizing and Interpreting Social Interactions in Local Image Regions. The 24th Annual Workshop on Object Perception, Attention, and Memory (OPAM), Boston, MA (2016).
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)
Meyers, E. Review of the CBMM workshop on the Turing++ Question: 'who is there?'. (2016).PDF icon Review of the CBMM workshop on the Turing++ Question- 'who is there?' .pdf (555.71 KB)
Tacchetti, A., Isik, L. & Poggio, T. Spatio-temporal convolutional networks explain neural representations of human actions. (2016).
Traer, J. & McDermott, J. H. Statistics of natural reverberation enable perceptual separation of sound and space. Proceedings of the National Academy of Sciences 113, E7856 - E7865 (2016).
Liao, Q., Kawaguchi, K. & Poggio, T. Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning. (2016).PDF icon CBMM-Memo-057.pdf (1.27 MB)
Poggio, T., Mhaskar, H., Rosasco, L., Miranda, B. & Liao, Q. Theory I: Why and When Can Deep Networks Avoid the Curse of Dimensionality?. (2016).PDF icon CBMM-Memo-058v1.pdf (2.42 MB)PDF icon CBMM-Memo-058v5.pdf (2.45 MB)PDF icon CBMM-Memo-058-v6.pdf (2.74 MB)PDF icon Proposition 4 has been deleted (2.75 MB)