Publication
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)
Implicit dynamic regularization in deep networks. (2020).
v1.2 (2.29 MB)
v.59 Update on rank (2.43 MB)
On efficiently computable functions, deep networks and sparse compositionality. (2025).
Deep_sparse_networks_approximate_efficiently_computable_functions.pdf (223.15 KB)
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)
The History of Neuroscience in Autobiography Volume 8 8, (Society for Neuroscience, 2014).
Volume Introduction and Preface (232.8 KB)
TomasoPoggio.pdf (1.43 MB)
Explicit regularization and implicit bias in deep network classifiers trained with the square loss. arXiv (2020). at <https://arxiv.org/abs/2101.00072>
Turing++ Questions: A Test for the Science of (Human) Intelligence. AI Magazine 37 , 73-77 (2016).
Turing_Plus_Questions.pdf (424.91 KB)
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)
Loss landscape: SGD has a better view. (2020).
CBMM-Memo-107.pdf (1.03 MB)
Typos and small edits, ver11 (955.08 KB)
Small edits, corrected Hessian for spurious case (337.19 KB)
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