All Publications

2020

Y. Han, Roig, G., Geiger, G., and Poggio, T., “Scale and translation-invariance for novel objects in human vision”, Scientific Reports, vol. 10, no. 1411, 2020.PDF icon s41598-019-57261-6.pdf (1.46 MB)
CBMM Funded
H. Mhaskar and Poggio, T., “An analysis of training and generalization errors in shallow and deep networks”, Neural Networks, vol. 121, pp. 229 - 241, 2020.
CBMM Funded

2019

K. M. Jozwik, Lee, M., Marques, T., Schrimpf, M., and Bashivan, P., “Large-scale hyperparameter search for predicting human brain responses in the Algonauts challenge”, in The Algonauts Project: Explaining the Human Visual Brain Workshop 2019 , MIT, Cambridge MA, 2019.
CBMM Funded
CBMM Memo No.
102
T. Poggio, Kur, G., and Banburski, A., “Double descent in the condition number”. 2019.PDF icon Fixing typos, clarifying error in y, best approach is crossvalidation (837.18 KB)PDF icon Incorporated footnote in text plus other edits (854.05 KB)PDF icon Deleted previous discussion on kernel regression and deep nets: it will appear, extended, in a separate paper (795.28 KB)PDF icon correcting a bad typo (261.24 KB)PDF icon Deleted plot of condition number of kernel matrix: we cannot get a double descent curve  (769.32 KB)
CBMM Funded
W. Młynarski and McDermott, J. H., “Ecological origins of perceptual grouping principles in the auditory system”, Proceedings of the National Academy of Sciences, vol. 116, no. 50, pp. 25355 - 25364, 2019.
CBMM Funded
R. McWalter and McDermott, J. H., “Illusory sound texture reveals multi-second statistical completion in auditory scene analysis”, Nature Communications, vol. 10, no. 1, 2019.
CBMM Related
N. Muecke, Neu, G., and Rosasco, L., “Beating SGD Saturation with Tail-Averaging and Minibatching”, Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019.PDF icon 9422-beating-sgd-saturation-with-tail-averaging-and-minibatching.pdf (389.35 KB)
CBMM Funded
N. Pagliana and Rosasco, L., “Implicit Regularization of Accelerated Methods in Hilbert Spaces”, Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019.PDF icon 9591-implicit-regularization-of-accelerated-methods-in-hilbert-spaces.pdf (451.14 KB)
CBMM Funded
K. Kar and DiCarlo, J. J., “Evidence that recurrent pathways between the prefrontal and inferior temporal cortex is critical during core object recognition ”, in Society for Neuroscience, Chicago, IL, USA, 2019.
CBMM Funded
J. Kubilius, Schrimpf, M., Kar, K., Rajalingham, R., Hong, H., Majaj, N. J., Issa, E. B., Bashivan, P., Prescott-Roy, J., Schmidt, K., Nayebi, A., Bear, D., Yamins, D. L. K., and DiCarlo, J. J., “Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs”, 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019.PDF icon 2019-10-28 NeurIPS-camera_ready.pdf (1.88 MB)
CBMM Funded
A. J. E. Kell and McDermott, J. H., “Invariance to background noise as a signature of non-primary auditory cortex”, Nature Communications, vol. 10, no. 1, 2019.
CBMM Related
Q. Liao, Banburski, A., and Poggio, T., “Theories of Deep Learning: Approximation, Optimization and Generalization ”, TECHCON 2019. 2019.
CBMM Funded
T. Marques and DiCarlo, J. J., “A meta-analysis of ANNs as models of primate V1 ”, in Bernstein, Berlin, Germany, 2019.
CBMM Funded
J. Traer, Cusimano, M., and McDermott, J. H., “A perceptually inspired generative model of rigid-body contact sounds”, Proceedings of the 22nd International Conference on Digital Audio Effects (DAFx-19), 2019.
CBMM Funded
CBMM Memo No.
100
T. Poggio, Banburski, A., and Liao, Q., “Theoretical Issues in Deep Networks”. 2019.PDF icon CBMM Memo 100 v1 (1.71 MB)PDF icon CBMM Memo 100 v3 (8/25/2019) (1.31 MB)PDF icon CBMM Memo 100 v4 (11/19/2019) (1008.23 KB)
CBMM Funded
CBMM Memo No.
099
A. Adler, Wax, M., and Pantazis, D., “Brain Signals Localization by Alternating Projections”, arXiv. 2019.PDF icon CBMM-Memo-099.pdf (421.67 KB)
CBMM Funded
A. Adler and Wax, M., “Constant modulus algorithms via low-rank approximation”, Signal Processing, vol. 160, pp. 263 - 270, 2019.
CBMM Funded
A. Adler, Araya-Polo, M., and Poggio, T., “Deep Recurrent Architectures for Seismic Tomography”, in 81st EAGE Conference and Exhibition 2019, 2019.
CBMM Funded
A. Banburski, Liao, Q., Miranda, B., Rosasco, L., Hidary, J., and Poggio, T., “Weight and Batch Normalization implement Classical Generalization Bounds ”, in ICML, Long Beach/California, 2019.
CBMM Funded
CBMM Memo No.
098
H. Mhaskar and Poggio, T., “An analysis of training and generalization errors in shallow and deep networks”. 2019.PDF icon CBMM-Memo-098.pdf (687.36 KB)PDF icon CBMM Memo 098 v4 (08/2019) (2.63 MB)
CBMM Funded
P. Bashivan, Kar, K., and DiCarlo, J. J., “Neural Population Control via Deep Image Synthesis”, Science, vol. 364, no. 6439, 2019.PDF icon Author's last draft (18.45 MB)
CBMM Funded
Y. Han, Roig, G., Geiger, G., and Poggio, T., “Properties of invariant object recognition in human one-shot learning suggests a hierarchical architecture different from deep convolutional neural networks”, in Vision Science Society, Florida, USA, 2019.
CBMM Funded
Y. Han, Roig, G., Geiger, G., and Poggio, T., “Properties of invariant object recognition in human oneshot learning suggests a hierarchical architecture different from deep convolutional neural networks ”, in Vision Science Society, St Pete Beach, FL, USA, 2019.
CBMM Funded
J. Zhang, Han, Y., Poggio, T., and Roig, G., “Eccentricity Dependent Neural Network with Recurrent Attention for Scale, Translation and Clutter Invariance ”, in Vision Science Society, Florida, USA, 2019.
CBMM Funded
K. Kar, Kubilius, J., Schmidt, K., Issa, E. B., and DiCarlo, J. J., “Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior”, Nature Neuroscience, 2019.PDF icon Author's last draft (1.74 MB)
CBMM Funded

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