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An analysis of training and generalization errors in shallow and deep networks. Neural Networks 121, 229 - 241 (2020).
An analysis of training and generalization errors in shallow and deep networks. (2019).
CBMM-Memo-098.pdf (687.36 KB)
CBMM Memo 098 v4 (08/2019) (2.63 MB)


An analysis of training and generalization errors in shallow and deep networks. (2018).
CBMM-Memo-076.pdf (772.61 KB)
CBMM-Memo-076v2.pdf (2.67 MB)


Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?. bioRxiv preprint (2018). doi:10.1101/407007
Brain-Score bioRxiv.pdf (789.83 KB)

Handbook of Numerical Analysis 19, 3 - 17 (Elsevier, 2018).
A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy. Neuron 98, (2018).
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)

Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. arXiv.org (2017). at <https://arxiv.org/abs/1711.01530>
1711.01530.pdf (966.99 KB)

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)

Why does deep and cheap learning work so well?. Journal of Statistical Physics 168, 1223–1247 (2017).
1608.08225.pdf (2.14 MB)
