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Found 906 results
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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)
Rosasco, L. & Villa, S. Learning with incremental iterative regularization. NIPS 2015 (2015). at <https://papers.nips.cc/paper/6015-learning-with-incremental-iterative-regularization>PDF icon Learning with Incremental Iterative Regularization_1405.0042v2.pdf (504.66 KB)
Rosasco, L. Object recognition data sets (iCub/IIT). (2013).
Roig, G., Chen, F., Boix, X. & Poggio, T. Eccentricity Dependent Deep Neural Networks for Modeling Human Vision. Vision Sciences Society (2017).
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)
Rockmore, D. Our Mother the Machine, by Dan Rockmore [Huffpost] . (2015). at <http://www.huffingtonpost.com/dan-rockmore/our-mother-the-machine_b_7273504.html>PDF icon Our Mother the Machine.pdf (199.73 KB)
Rockmore, D. Is it time for a presidential technoethics commission. (2016). at <https://theconversation.com/is-it-time-for-a-presidential-technoethics-commission-58846>PDF icon rockmore - Is it time for a presidential technoethics commission.pdf (280.2 KB)
Robertson, C. E., Hermann, K., Mynick, A., Kravitz, D. J. & Kanwisher, N. Neural Representations Integrate the Current Field of View with the Remembered 360° Panorama. Current Biology (2016). doi:10.1016/j.cub.2016.07.002
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).
Ren, Z., Wang, C. & Yuille, A. Scene-Domain Active Part Models for Object Representation. IEEE International Conference on Computer Vision (ICCV) 2497 - 2505 (2015). doi:10.1109/ICCV.2015.287PDF icon Ren_ICCV15.pdf (3.37 MB)
Reddy, M. Vuyyuru, Banburski, A., Pant, N. & Poggio, T. Biologically Inspired Mechanisms for Adversarial Robustness. (2020).PDF icon CBMM_Memo_110.pdf (3.14 MB)
Rangamani, A., Rosasco, L. & Poggio, T. For interpolating kernel machines, minimizing the norm of the ERM solution maximizes stability. Analysis and Applications 21, 193 - 215 (2023).
Rangamani, A., Rosasco, L. & Poggio, T. For interpolating kernel machines, the minimum norm ERM solution is the most stable. (2020).PDF icon CBMM_Memo_108.pdf (1015.14 KB)PDF icon Better bound (without inequalities!) (1.03 MB)
Rangamani, A. & Xie, Y. Understanding the Role of Recurrent Connections in Assembly Calculus. (2022).PDF icon CBMM-Memo-137.pdf (1.49 MB)
Rangamani, A., Lindegaard, M., Galanti, T. & Poggio, T. Feature learning in deep classifiers through Intermediate Neural Collapse. (2023).PDF icon Feature_Learning_memo.pdf (2.16 MB)
Rando, M., Molinari, C., Villa, S. & Rosasco, L. An Optimal Structured Zeroth-order Algorithm for Non-smooth Optimization. 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (2023). at <https://proceedings.neurips.cc/paper_files/paper/2023/file/7429f4c1b267cf619f28c4d4f1532f99-Paper-Conference.pdf>
Rajalingham, R., Kar, K., Sanghavi, S., Dehaene, S. & DiCarlo, J. J. The inferior temporal cortex is a potential cortical precursor of orthographic processing in untrained monkeys. Nature Communications 11, (2020).PDF icon s41467-020-17714-3.pdf (25.01 MB)
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Puig, X., Shu, T., Tenenbaum, J. B. & Torralba, A. NOPA: Neurally-guided Online Probabilistic Assistance for Building Socially Intelligent Home Assistants. arXiv (2023). at <https://arxiv.org/abs/2301.05223>
Puig, X., Shu, T., Tenenbaum, J. B. & Torralba, A. NOPA: Neurally-guided Online Probabilistic Assistance for Building Socially Intelligent Home Assistants. 2023 IEEE International Conference on Robotics and Automation (ICRA) (2023). doi:10.1109/ICRA48891.2023.10161352
Prevedel, R. et al. Simultaneous whole-animal 3D imaging of neuronal activity using light-field microscopy. Nature Methods 11, 727 - 730 (2014).
Pramod, R. T., Mieczkowski, E., Fang, C. X., Tenenbaum, J. B. & Kanwisher, N. Decoding predicted future states from the brain’s “physics engine”. Science Advances 11, (2025).
Pramod, R. T., Cohen, M. A., Tenenbaum, J. B. & Kanwisher, N. Invariant representation of physical stability in the human brain. eLife 11, (2022).
Powell, L. J. & Spelke, E. S. Third-Party Preferences for Imitators in Preverbal Infants. Open Mind 2, 61 - 71 (2018).
Powell, L. J., Deen, B., Guo, L. & Saxe, R. Using fNIRS to Map Functional Specificity in the Infant Brain: An fROI Approach. (2015).PDF icon SRCD2015_NIRS_poster.pdf (2.14 MB)
Powell, L. J. & Spelke, E. S. Infants’ Categorization of Social Actions. Cognitive Development Society (CDS) (2015).

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