Publication
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)
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)
Cervelli menti algoritmi. 272 (Sperling & Kupfer, 2023). at <https://www.sperling.it/libri/cervelli-menti-algoritmi-marco-magrini>
What if.. (2015).
What if.pdf (2.09 MB)
Theory II: Deep learning and optimization. Bulletin of the Polish Academy of Sciences: Technical Sciences 66, (2018).
03_775-788_00920_Bpast.No_.66-6_31.12.18_K2.pdf (5.43 MB)
An Overview of Some Issues in the Theory of Deep Networks. IEEJ Transactions on Electrical and Electronic Engineering 15, 1560 - 1571 (2020).
Computational role of eccentricity dependent cortical magnification. (2014).
CBMM-Memo-017.pdf (1.04 MB)
Associative Memory as the Core of Intelligence in Technology and Evolution. (2026).
Review_On_Associative_Memories-14.pdf (245.78 KB)
Deep Leaning: Mathematics and Neuroscience. A Sponsored Supplement to Science Brain-Inspired intelligent robotics: The intersection of robotics and neuroscience, 9-12 (2016).
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