All Publications

2019

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
A. Banburski, Liao, Q., Miranda, B., Rosasco, L., Hidary, J., and Poggio, T., “Dynamics & Generalization in Deep Networks -Minimizing the Norm”, in NAS Sackler Colloquium on Science of Deep Learning, Washington D.C., 2019.
CBMM Funded
A. J. E. Kell and McDermott, J. H., “Deep neural network models of sensory systems: windows onto the role of task constraints”, Current Opinion in Neurobiology, vol. 55, pp. 121 - 132, 2019.
CBMM Related
I. Yildirim, Wu, J., Kanwisher, N., and Tenenbaum, J. B., “An integrative computational architecture for object-driven cortex”, Current Opinion in Neurobiology, vol. 55, pp. 73 - 81, 2019.
CBMM Funded
M. Araya-Polo, Adler, A., Farris, S., and Jennings, J., “Fast and Accurate Seismic Tomography via Deep Learning”, in Deep Learning: Algorithms and Applications, SPRINGER-VERLAG, 2019.
CBMM Related
K. M. Jozwik, Schrimpf, M., Kanwisher, N., and DiCarlo, J. J., “To find better neural network models of human vision, find better neural network models of primate vision”, in BioRxiv, 2019.
CBMM Funded

2018

CBMM Memo No.
092
W. Xiao, Chen, H., Liao, Q., and Poggio, T., “Biologically-plausible learning algorithms can scale to large datasets”. 2018.PDF icon CBMM-Memo-092.pdf (1.31 MB)
CBMM Funded
T. Owaki, Vidal-Naquet, M., Nam, Y., Uchida, G., Sato, T., Câteau, H., Ullman, S., and Tanifuji, M., “Searching for visual features that explain response variance of face neurons in inferior temporal cortex”, PLOS ONE, vol. 13, no. 9, p. e0201192, 2018.
CBMM Related
CBMM Memo No.
090
A. Banburski, Liao, Q., Miranda, B., Poggio, T., Rosasco, L., Hidary, J., and De La Torre, F., “Theory III: Dynamics and Generalization in Deep Networks”. 2018.PDF icon Original, intermediate versions are available under request (2.67 MB)PDF icon CBMM Memo 90 v12.pdf (4.74 MB)PDF icon Theory_III_ver44.pdf Update Hessian (4.12 MB)PDF icon Theory_III_ver48 (Updated discussion of convergence to max margin) (2.56 MB)PDF icon fixing errors and sharpening some proofs (2.45 MB)
CBMM Funded
T. Poggio and Liao, Q., “Theory I: Deep networks and the curse of dimensionality”, Bulletin of the Polish Academy of Sciences: Technical Sciences, vol. 66, no. 6, 2018.PDF icon 02_761-774_00966_Bpast.No_.66-6_28.12.18_K1.pdf (1.18 MB)
CBMM Funded
T. Poggio and Liao, Q., “Theory II: Deep learning and optimization”, Bulletin of the Polish Academy of Sciences: Technical Sciences, vol. 66, no. 6, 2018.PDF icon 03_775-788_00920_Bpast.No_.66-6_31.12.18_K2.pdf (5.43 MB)
CBMM Funded

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