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Found 904 results
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Owaki, T. et al. Searching for visual features that explain response variance of face neurons in inferior temporal cortex. PLOS ONE 13, e0201192 (2018).
Owens, A. et al. Visually indicated sounds. Conference on Computer Vision and Pattern Recognition (2016).PDF icon Owens_etal_2016_visually_indicated_sounds_CVPR.pdf (7.57 MB)
Owens, A., Isola, P., McDermott, J. H., Freeman, W. T. & Torralba, A. Lecture Notes in Computer ScienceComputer Vision – ECCV 2016Ambient Sound Provides Supervision for Visual Learning. 14th European Conference on Computer Vision 801 - 816 (2016). doi:10.1007/978-3-319-46448-010.1007/978-3-319-46448-0_48
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Pagliana, N. & Rosasco, L. Implicit Regularization of Accelerated Methods in Hilbert Spaces. Neural Information Processing Systems (NeurIPS 2019) (2019).PDF icon 9591-implicit-regularization-of-accelerated-methods-in-hilbert-spaces.pdf (451.14 KB)
Palepu, A. & Kreiman, G. Development of automated interictal spike detector. 40th International Conference of the IEEE Engineering in Medicine and Biology Society - EMBC 2018 (2018). at <https://embc.embs.org/2018/>
Palmer, I., Rouditchenko, A., Barbu, A., Katz, B. & Glass, J. Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset. (2021).PDF icon CBMM-Memo-128.pdf (2.91 MB)
Palmer, I., Rouditchenko, A., Barbu, A., Katz, B. & Glass, J. Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset. Interspeech 2021 (2021). doi:10.21437/Interspeech.2021
Patzelt, E. H., Kool, W., Millner, A. J. & Gershman, S. J. The transdiagnostic structure of mental effort avoidance. Scientific Reports 9, (2019).
Patzelt, E. H., Kool, W., Millner, A. J. & Gershman, S. J. Incentives Boost Model-Based Control Across a Range of Severity on Several Psychiatric Constructs. Biological Psychiatry 85, 425 - 433 (2019).
Paul, R., Barbu, A., Felshin, S., Katz, B. & Roy, N. Temporal Grounding Graphs for Language Understanding with Accrued Visual-Linguistic Context. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI 2017) (2017). at <c>
Penagos, H., Varela, C. & Wilson, M. A. Oscillations, neural computations and learning during wake and sleep. Current Opinion in Neurobiology 44C, (2017).
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)
Peters, B. et al. How does the primate brain combine generative and discriminative computations in vision?. arXiv (2024). at <https://arxiv.org/abs/2401.06005>
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).
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. et al. Eye movements and retinotopic tuning in developmental prosopagnosia. Journal of Vision 19, 7 (2019).
Peyrache, A. et al. Spatiotemporal dynamics of neocortical excitation and inhibition during human sleep. Proceedings of the National Academy of Sciences (2012). doi:10.1073/pnas.1109895109PDF icon SpatiotemporalDynamic.pdf (2.56 MB)
Phillips-Jones, T., Coronel, S. Otero, Sani, I. & Freiwald, W. A. A Virtual Reality Experimental Approach for Studying How the Brain Implements Attentive Behaviors. Tri-Institute 2019 Gateways to the Laboratory Summer Program (2019).
Pinto, A., Rangamani, A. & Poggio, T. On Generalization Bounds for Neural Networks with Low Rank Layers. (2024).PDF icon CBMM-Memo-151.pdf (697.31 KB)
Poggio, T., Anselmi, F. & Rosasco, L. I-theory on depth vs width: hierarchical function composition. (2015).PDF icon cbmm_memo_041.pdf (1.18 MB)
Poggio, T. & Banburski, A. An Overview of Some Issues in the Theory of Deep Networks. IEEJ Transactions on Electrical and Electronic Engineering 15, 1560 - 1571 (2020).
Poggio, T. & Liao, Q. Theory I: Deep networks and the curse of dimensionality. Bulletin of the Polish Academy of Sciences: Technical Sciences 66, (2018).PDF icon 02_761-774_00966_Bpast.No_.66-6_28.12.18_K1.pdf (1.18 MB)
Poggio, T. & Meyers, E. Turing++ Questions: A Test for the Science of (Human) Intelligence. AI Magazine 37 , 73-77 (2016).PDF icon Turing_Plus_Questions.pdf (424.91 KB)
Poggio, T. & Squire, L. R. The History of Neuroscience in Autobiography Volume 8 8, (Society for Neuroscience, 2014).PDF icon Volume Introduction and Preface (232.8 KB)PDF icon TomasoPoggio.pdf (1.43 MB)
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)

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