Export 652 results:
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 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)PDF icon Edited Appendix on SGD. (909.19 KB)PDF icon Deleted Appendix. Corrected typos etc (880.27 KB)PDF icon Added result about square loss and min norm (898.03 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).
Poggio, T., Banburski, A. & Liao, Q. Theoretical issues in deep networks. Proceedings of the National Academy of Sciences 201907369 (2020). doi:10.1073/pnas.1907369117PDF icon PNASlast.pdf (915.3 KB)
Dasgupta, I., Schulz, E., Tenenbaum, J. B. & Gershman, S. J. A theory of learning to infer. Psychological Review 127, 412 - 441 (2020).
Gen, C. et al. ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. arXiv (2020). at <>PDF icon 2007.04954.pdf (7.06 MB)
Schwartz, J. et al. ThreeDWorld (TDW): A High-Fidelity, Multi-Modal Platform for Interactive Physical Simulation. (2020). at <>
Eisape, T., Levy, R., Tenenabum, J. B. & Zaslavsky, N. Toward human-like object naming in artificial neural systems . International Conference on Learning Representations (ICLR 2020), Bridging AI and Cognitive Science workshop (2020).
Dobs, K., Kell, A. J. E., Martinez, J., Cohen, M. & Kanwisher, N. Using task-optimized neural networks to understand why brains have specialized processing for faces . Computational and Systems Neurosciences (2020).
Ben-Yosef, G., Kreiman, G. & Ullman, S. What can human minimal videos tell us about dynamic recognition models?. International Conference on Learning Representations (ICLR 2020) (2020). at <>PDF icon Authors' final version (516.09 KB)
Dobs, K., Kell, A. J. E., Martinez, J., Cohen, M. & Kanwisher, N. Why Are Face and Object Processing Segregated in the Human Brain? Testing Computational Hypotheses with Deep Convolutional Neural Networks . Conference on Cognitive Computational Neuroscience (2020).
Xiao, W. & Kreiman, G. XDream: Finding preferred stimuli for visual neurons using generative networks and gradient-free optimization. PLOS Computational Biology 16, e1007973 (2020).PDF icon gk7791.pdf (2.39 MB)