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CBMM Memos were established in 2014 as a mechanism for our center to share research results with the wider scientific community. Click here to read more about the memos and to see a full list of the memos.

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O'Connell, T. P. et al. Approaching human 3D shape perception with neurally mappable models. arXiv (2023). at <https://arxiv.org/abs/2308.11300>

Obiajulu, D., Vazquez, Y., Ianni, G. A., Yazdani, F. & Freiwald, W. A. Facial Expression Scoring and Assessment of Facial Movement Kinematics in Non-Human Primates. The Rockefeller University 2019 Summer Science Research Program (SSRP) (2019).

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., 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

Owens, A. et al. Visually indicated sounds. Conference on Computer Vision and Pattern Recognition (2016).

Owens_etal_2016_visually_indicated_sounds_CVPR.pdf (7.57 MB)

P

Pagliana, N. & Rosasco, L. Implicit Regularization of Accelerated Methods in Hilbert Spaces. Neural Information Processing Systems (NeurIPS 2019) (2019).

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).

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>

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. et al. Eye movements and retinotopic tuning in developmental prosopagnosia. Journal of Vision 19, 7 (2019).

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).

Real World Face Fixations, Journal of Vision article, 2016 (20.25 MB)

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.1109895109

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).

CBMM-Memo-151.pdf (697.31 KB)

Poggio, T. et al. Theory of Deep Learning III: explaining the non-overfitting puzzle. (2017).

CBMM-Memo-073.pdf (2.65 MB)

CBMM Memo 073 v2 (revised 1/15/2018) (2.81 MB)

CBMM Memo 073 v3 (revised 1/30/2018) (2.72 MB)

CBMM Memo 073 v4 (revised 12/30/2018) (575.72 KB)

Poggio, T. & Fraser, M. Compositional Sparsity of Learnable Functions. (2024).

This is an update of the AMS paper (230.72 KB)

Poggio, T. & Liao, Q. Theory I: Deep networks and the curse of dimensionality. Bulletin of the Polish Academy of Sciences: Technical Sciences 66, (2018).

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).

Turing_Plus_Questions.pdf (424.91 KB)

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