Publication

Export 638 results:
2020
Kim, D. et al. The ability to predict actions of others from distributed cues is still developing in children. PsyArXiv Preprints (2020). doi:10.31234/osf.io/pu3tfPDF icon Action_prediction_in_children.pdf (427.84 KB)
Kreiman, G. & Serre, T. Beyond the feedforward sweep: feedback computations in the visual cortex. Ann. N.Y. Acad. Sci. | Special Issue: The Year in Cognitive Neuroscience 1464, 222-241 (2020).PDF icon gk7812.pdf (1.93 MB)
Jacquot, V., Ying, J. & Kreiman, G. Can Deep Learning Recognize Subtle Human Activities?. CVPR 2020 (2020).
Shalev-Shwartz, S. & Shashua, A. Can we Contain Covid-19 without Locking-down the Economy?. (2020).PDF icon CBMM Memo 104 v4 (Apr. 6, 2020) (418.25 KB)PDF icon CBMM Memo 104 v3 (Apr. 1, 2020) (452.94 KB)PDF icon CBMM Memo 104 v2 (Mar. 28, 2020) (427.39 KB)PDF icon CBMM-Memo-104.pdf (425.12 KB)
Poggio, T. A., Liao, Q. & Banburski, A. Complexity Control by Gradient Descent in Deep Networks. Nature Communications 11, (2020).PDF icon s41467-020-14663-9.pdf (431.68 KB)
Kuo, Y. - L., Katz, B. & Barbu, A. Deep compositional robotic planners that follow natural language commands . International Conference on Robotics and Automation (ICRA) (2020).
Villalobos, K. M. et al. Do Neural Networks for Segmentation Understand Insideness?. (2020).PDF icon CBMM-Memo-105.pdf (4.63 MB)
Yildirim, I., Belledonne, M., Freiwald, W. & Tenenbaum, J. Efficient inverse graphics in biological face processing. Science Advances 6, eaax5979 (2020).PDF icon eaax5979.full_.pdf (3.22 MB)
Zaslavsky, N., Hu, J. & Levy, R. Emergence of Pragmatic Reasoning From Least-Effort Optimization . 13th International Conference on the Evolution of Language (EvoLang) (2020).
Kar, K. & DiCarlo, J. J. Evidence that recurrent pathways between the prefrontal and inferior temporal cortex is critical during core object recognition . COSYNE (2020).
Shalev-Shwartz, S. & Shashua, A. An Exit Strategy from the Covid-19 Lockdown based on Risk-sensitive Resource Allocation. (2020).PDF icon CBMM-Memo-106.pdf (431.13 KB)
Marques, T., Schrimpf, M. & DiCarlo, J. J. Hierarchical neural network models that more closely match primary visual cortex tend to better explain higher level visual cortical responses . COSYNE (2020).
Thomas, A. J., Saxe, R. & Spelke, E. S. Infants represent 'like-kin' affiliation . Budapest Conference on Cognitive Development (2020).
Ben-Yosef, G., Kreiman, G. & Ullman, S. Minimal videos: Trade-off between spatial and temporal information in human and machine vision. Cognition (2020). doi:10.1016/j.cognition.2020.104263
Liu, S. Nature and origins of intuitive psychology in human infants. (2020).
Freiwald, W. A. The neural mechanisms of face processing: cells, areas, networks, and models. Current Opinion in Neurobiology 60, 184 - 191 (2020).
Lotter, W., Kreiman, G. & Cox, D. A neural network trained for prediction mimics diverse features of biological neurons and perception. Nature Machine Intelligence 2, 210 - 219 (2020).
Lotter, W., Kreiman, G. & Cox, D. A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception. Nature Machine Learning (2020).PDF icon 1805.10734.pdf (9.59 MB)
Zhang, M., Tseng, C. & Kreiman, G. Putting visual object recognition in context. CVPR 2020 (2020).PDF icon gk7876.pdf (3.12 MB)
Richardson, H. et al. Response patterns in the developing social brain are organized by social and emotion features and disrupted in children diagnosed with autism spectrum disorder. Cortex 125, 12 - 29 (2020).
Han, Y., Roig, G., Geiger, G. & Poggio, T. Scale and translation-invariance for novel objects in human vision. Scientific Reports 10, (2020).PDF icon s41598-019-57261-6.pdf (1.46 MB)
Hicks, J. M. & McDermott, J. H. Segregation from Noise as Outlier Detection . Association for Research in Otolaryngology (2020).
Isik, L., Mynick, A., Pantazis, D. & Kanwisher, N. The speed of human social interaction perception. NeuroImage 116844 (2020). doi:10.1016/j.neuroimage.2020.116844
Poggio, T. A. Stable Foundations for Learning: a foundational framework for learning theory in both the classical and modern regime. (2020).PDF icon Original file (584.54 KB)PDF icon Corrected typos and details of "equivalence" CV stability and expected error for interpolating machines. Added Appendix on SGD.  (905.29 KB)
Schrimpf, M., Sato, F., Sanghavi, S. & DiCarlo, J. J. Temporal information for action recognition only needs to be integrated at a choice level in neural networks and primates . COSYNE (2020).

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