Publication

Export 543 results:
2017
Cano-Córdoba, F., Sarma, S. & Subirana, B. Theory of Intelligence with Forgetting: Mathematical Theorems Explaining Human Universal Forgetting using “Forgetting Neural Networks”. (2017).PDF icon CBMM-Memo-071.pdf (2.54 MB)
Kool, W., Gershman, S. J. & Cushman, F. A. Thinking fast or slow? A reinforcement-learning approach. Society for Personality and Social Psychology (2017).PDF icon KoolEtAl_SPSP_2017.pdf (670.35 KB)
Jing, L. et al. Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNN. 34th International Conference on Machine Learning 70, 1733-1741 (2017).PDF icon 1612.05231.pdf (2.3 MB)
Landi, S. M. & Freiwald, W. A. Two areas for familiar face recognition in the primate brain. Science 357, 591 - 595 (2017).PDF icon 591.full_.pdf (928.29 KB)
Leibo, J. Z., Liao, Q., Anselmi, F., Freiwald, W. A. & Poggio, T. View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation. Current Biology 27, 1-6 (2017).
Isik, L., Singer, J., Madsen, J., Kanwisher, N. & Kreiman, G. What is changing when: Decoding visual information in movies from human intracranial recordings. Neuroimage (2017). doi:https://doi.org/10.1016/j.neuroimage.2017.08.027
Mhaskar, H., Liao, Q. & Poggio, T. When and Why Are Deep Networks Better Than Shallow Ones?. AAAI-17: Thirty-First AAAI Conference on Artificial Intelligence (2017).
Poggio, T., Mhaskar, H., Rosasco, L., Miranda, B. & Liao, Q. Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review. International Journal of Automation and Computing 1-17 (2017). doi:10.1007/s11633-017-1054-2PDF icon art%3A10.1007%2Fs11633-017-1054-2.pdf (1.68 MB)
Lin, H. & Tegmark, M. Why does deep and cheap learning work so well?. Journal of Statistical Physics 168, 1223–1247 (2017).PDF icon 1608.08225.pdf (2.14 MB)
Dillon, M. R. & Spelke, E. S. Young children's use of distance and angle information during map reading. SRCD (2017).
2016
Berzak, Y., Huang, Y., Barbu, A., Korhonen, A. & Katz, B. Anchoring and Agreement in Syntactic Annotations. (2016).PDF icon CBMM-Memo-055.pdf (768.54 KB)
Ullman, S., Assif, L., Fetaya, E. & Harari, D. Atoms of recognition in human and computer vision. PNAS 113, 2744–2749 (2016).PDF icon mirc_author_manuscript_with_figures_and_SI-2.pdf (1.65 MB)
Linderman, S. W., Johnson, M. J., Wilson, M. A. & Chen, Z. A Bayesian nonparametric approach for uncovering rat hippocampal population codes during spatial navigation. Journal of Neuroscience Methods 263, (2016).PDF icon Journal of Neuroscience Methods (2.27 MB)
Chen, Z., Linderman, S. W. & Wilson, M. A. Bayesian nonparametric methods for discovering latent structures of rat hippocampal ensemble spikes. IEEE Workshop on Machine Learning for Signal Processing (2016).PDF icon MLSP16 (1).pdf (1.04 MB)
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).
Liao, Q. & Poggio, T. Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex. (2016).PDF icon CBMM Memo No. 047 (1.29 MB)
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)
Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex [dataset]. (2016). at <http://klab.tch.harvard.edu/resources/tangetal_stroop_2016.html>
Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex [code]. (2016). at <http://klab.tch.harvard.edu/resources/tangetal_stroop_2016.html>
Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex. eLIFE (2016). doi:10.7554/eLife.12352PDF icon Manuscript  (1.83 MB)
Spokes, A. C. & Spelke, E. S. Children’s Expectations and Understanding of Kinship as a Social Category. Frontiers in Psychology 7, 1664-1078 (2016).
Spelke, E. S., Sternberg, R. J., Fiske, S. T. & Foss, D. J. Scientists Making a Difference: One Hundred Eminent Behavioral and Brain Scientists Talk about Their Most Important Contributions (Cambridge University Press, 2016).
Lafer-Sousa, R., Conway, B. R. & Kanwisher, N. Color-Biased Regions of the Ventral Visual Pathway Lie between Face- and Place-Selective Regions in Humans, as in Macaques. Journal of Neuroscience 36, 1682 - 1697 (2016).
Liu, S. & Spelke, E. S. Continuous representations of action efficiency in infancy. CEU Conference on Cognitive Development (BCCCD16) (2016).
Berzak, Y., Reichart, R. & Katz, B. Contrastive Analysis with Predictive Power: Typology Driven Estimation of Grammatical Error Distributions in ESL. (2016).PDF icon memo-50.pdf (493.74 KB)

Pages