Publications

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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Poggio, T. From Marr’s Vision to the Problem of Human Intelligence. (2021).

CBMM-Memo-118.pdf (362.19 KB)

Poggio, T., Mhaskar, H., Rosasco, L., Miranda, B. & Liao, Q. Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review. International Journal of Automation and Computing 1-17 (2017). doi:10.1007/s11633-017-1054-2

art%3A10.1007%2Fs11633-017-1054-2.pdf (1.68 MB)

Poggio, T., Mutch, J. & Isik, L. Computational role of eccentricity dependent cortical magnification. (2014).

CBMM-Memo-017.pdf (1.04 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)

Poggio, T., Mhaskar, H., Rosasco, L., Miranda, B. & Liao, Q. Theory I: Why and When Can Deep Networks Avoid the Curse of Dimensionality?. (2016).

CBMM-Memo-058v1.pdf (2.42 MB)

CBMM-Memo-058v5.pdf (2.45 MB)

CBMM-Memo-058-v6.pdf (2.74 MB)

Proposition 4 has been deleted (2.75 MB)

Poggio, T., Kur, G. & Banburski, A. Double descent in the condition number. (2019).

Fixing typos, clarifying error in y, best approach is crossvalidation (837.18 KB)

Incorporated footnote in text plus other edits (854.05 KB)

Deleted previous discussion on kernel regression and deep nets: it will appear, extended, in a separate paper (795.28 KB)

correcting a bad typo (261.24 KB)

Deleted plot of condition number of kernel matrix: we cannot get a double descent curve (769.32 KB)

Poggio, T. What if.. (2015).

What if.pdf (2.09 MB)

Poggio, T., Liao, Q. & Xu, M. Implicit dynamic regularization in deep networks. (2020).

v1.2 (2.29 MB)

v.59 Update on rank (2.43 MB)

Poggio, T. Stable Foundations for Learning: a framework for learning theory (in both the classical and modern regime). (2020).

Original file (584.54 KB)

Corrected typos and details of "equivalence" CV stability and expected error for interpolating machines. Added Appendix on SGD. (905.29 KB)

Edited Appendix on SGD. (909.19 KB)

Deleted Appendix. Corrected typos etc (880.27 KB)

Added result about square loss and min norm (898.03 KB)

Poggio, T., Rosasco, L., Shashua, A., Cohen, N. & Anselmi, F. Notes on Hierarchical Splines, DCLNs and i-theory. (2015).

CBMM Memo 037 (1.83 MB)

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. & Banburski, A. Complexity Control by Gradient Descent in Deep Networks. Nature Communications 11, (2020).

s41467-020-14663-9.pdf (431.68 KB)

Pinto, A., Rangamani, A. & Poggio, T. On Generalization Bounds for Neural Networks with Low Rank Layers. (2024).

CBMM-Memo-151.pdf (697.31 KB)

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).

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)

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 16, (2016).

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

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).

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>

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)

Penagos, H., Varela, C. & Wilson, M. A. Oscillations, neural computations and learning during wake and sleep. Current Opinion in Neurobiology 44C, (2017).

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>

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).

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)

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