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2016
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)
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).
N. Murty, A. Ratan & Pramod, R. T. To What Extent Does Global Shape Influence Category Representation in the Brain?. Journal of Neuroscience 36, 4149 - 4151 (2016).
Mao, J., Xu, J., Jing, Y. & Yuille, A. Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images. NIPS 2016 (2016).PDF icon 6590-training-and-evaluating-multimodal-word-embeddings-with-large-scale-web-annotated-images.pdf (1.57 MB)
Berzak, Y. et al. Treebank of Learner English (TLE). (2016). at <http://esltreebank.org/>PDF icon acl2016.pdf (163.86 KB)
Rockmore, D. The Trolley Problem [Edge.com]. (2016). at <https://www.edge.org/response-detail/27051>PDF icon The Trolley Problem.pdf (343.3 KB)
Poggio, T. & Meyers, E. Turing++ Questions: A Test for the Science of (Human) Intelligence. AI Magazine 37 , 73-77 (2016).PDF icon Turing_Plus_Questions.pdf (424.91 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)
Lotter, W., Kreiman, G. & Cox, D. Unsupervised Learning of Visual Structure using Predictive Generative Networks. International Conference on Learning Representations (ICLR) (2016). at <http://arxiv.org/pdf/1511.06380v2.pdf>
Hartshorne, J. K. VerbCorner: Testing theories of argument structure through crowdsourcing. Workshop on Events in Language (2016).PDF icon VerbCorner_EventsInLanguage.pdf (1.14 MB)
Leibo, J. Z., Liao, Q., Freiwald, W. A., Anselmi, F. & Poggio, T. View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation. (2016).PDF icon faceMirrorSymmetry_memo_ver01.pdf (3.93 MB)
Rosenfeld, A. & Ullman, S. Visual Concept Recognition and Localization via Iterative Introspection. . Asian Conference on Computer Vision (2016).PDF icon Focusing on parts of interest  (910.14 KB)
Poggio, T. & Anselmi, F. Visual Cortex and Deep Networks: Learning Invariant Representations. 136 (The MIT Press, 2016). at <https://mitpress.mit.edu/books/visual-cortex-and-deep-networks>
Owens, A. et al. Visually indicated sounds. Conference on Computer Vision and Pattern Recognition (2016).PDF icon Owens_etal_2016_visually_indicated_sounds_CVPR.pdf (7.57 MB)
Spokes, A. C., Howard, R., Mehr, S. A. & Krasnow, M. M. Welfare-tradeoff ratios in children. Human Behavior and Evolution Society (2016).
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)
Dillon, M. R. & Spelke, E. S. Young Children’s Use of Surface and Object Information in Drawings of Everyday Scenes. Child Development (2016). doi:10.1111/cdev.12658

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