Publication

Found 287 results
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2016
Liao, Q., Leibo, J. Z. & Poggio, T. How Important Is Weight Symmetry in Backpropagation?. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) (Association for the Advancement of Artificial Intelligence, 2016).PDF icon liao-leibo-poggio.pdf (191.91 KB)
Peterson, M. F., Lin, J., Zaun, I. & Kanwisher, N. Individual Differences in Face Looking Behavior Generalize from the Lab to the World. Journal of Vision 16, (2016).PDF icon Real World Face Fixations, Journal of Vision article, 2016 (20.25 MB)
Peterson, M. F., Lin, J., Zaun, I. & Kanwisher, N. Individual differences in face-looking behavior generalize from the lab to the world. Journal of Vision (2016).
Bach, F. & Poggio, T. Introduction Special issue: Deep learning. Information and Inference 5, 103-104 (2016).
Anselmi, F., Rosasco, L. & Poggio, T. On invariance and selectivity in representation learning. Information and Inference: A Journal of the IMA iaw009 (2016). doi:10.1093/imaiai/iaw009PDF icon imaiai.iaw009.full_.pdf (267.87 KB)
Mhaskar, H., Liao, Q. & Poggio, T. Learning Functions: When Is Deep Better Than Shallow. (2016). at <https://arxiv.org/pdf/1603.00988v4.pdf>
Morales, A., Premtoon, V., Avery, C., Felshin, S. & Katz, B. Learning to Answer Questions from Wikipedia Infoboxes. The 2016 Conference on Empirical Methods on Natural Language Processing (EMNLP 2016) (2016).PDF icon Morales-EMNLP2016.pdf (197.28 KB)
Tang, H. et al. A machine learning approach to predict episodic memory formation. 2016 Annual Conference on Information Science and Systems (CISS) 539 - 544 (2016). doi:10.1109/CISS.2016.7460560
Tang, H. et al. A machine learning approach to predict episodic memory formation. 2016 Annual Conference on Information Science and Systems (CISS) 539 - 544 (2016). doi:10.1109/CISS.2016.7460560
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
Krafft, P., Baker, C., Pentland, A. & Tenenbaum, J. B. Modeling Human Ad Hoc Coordination. AAAI (2016).PDF icon krafft_aaai2016.pdf (247.58 KB)
Morère, O., Veillard, A., Chandrasekhar, V. & Poggio, T. Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval. arXiv.org (2016). at <https://arxiv.org/abs/1603.04595>PDF icon 1603.04595.pdf (2.9 MB)
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)
Lewis, O. & Poggio, T. From Neuron to Cognition via Computational Neuroscience (The MIT Press, 2016). at <https://mitpress.mit.edu/neuron-cognition>
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. 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 [code]. (2016).
Tang, H. et al. Predicting episodic memory formation for movie events [dataset]. (2016).
Tang, H. et al. Predicting episodic memory formation for movie events [dataset]. (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)
Peres, F., Smith, K. A. & Tenenbaum, J. B. Rapid Physical Predictions from Convolutional Neural Networks. Neural Information Processing Systems, Intuitive Physics Workshop (2016). at <http://phys.csail.mit.edu/papers/9.pdf>PDF icon Rapid Physical Predictions - NIPS Physics Workshop Poster (1.47 MB)
Tacchetti, A., Isik, L. & Poggio, T. Spatio-temporal convolutional networks explain neural representations of human actions. (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)

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