M. Aldridge, O. Johnson, and J. Scarlett. (2019)cite arxiv:1902.06002Comment: Survey paper, 140 pages, 19 figures. To be published in Foundations and Trends in Communications and Information Theory.
A. Genevay, L. Chizat, F. Bach, M. Cuturi, and G. Peyré. Proceedings of Machine Learning Research, volume 89 of Proceedings of Machine Learning Research, page 1574--1583. PMLR, (16--18 Apr 2019)
J. Helton, T. Mai, and R. Speicher. (2015)cite arxiv:1511.05330Comment: We have undertaken a major revision, mainly for the sake of clarity and readability.
J. Negrea, M. Haghifam, G. Dziugaite, A. Khisti, and D. Roy. (2019)cite arxiv:1911.02151Comment: 23 pages, 1 figure. To appear in, Advances in Neural Information Processing Systems (33), 2019.
F. Nielsen, and R. Nock. (2013)cite arxiv:1309.3029Comment: 11 pages, two tables, no figure. Java(TM) code available online at http://www.informationgeometry.org/fDivergence/.
V. Papyan, J. Sulam, and M. Elad. (2017)cite arxiv:1707.06066Comment: This is the journal version of arXiv:1607.02005 and arXiv:1607.02009, accepted to IEEE Transactions on Signal Processing.
I. Sason, and S. Verdú. (2015)cite arxiv:1508.00335Comment: IEEE Trans. on Information Theory, vol. 62, no. 11, pp. 5973--6006, November 2016. This manuscript is identical to the journal paper, apart of some additional material which includes Sections III-C and IV-F, and three technical proofs.
D. Soudry, E. Hoffer, M. Nacson, S. Gunasekar, and N. Srebro. (2017)cite arxiv:1710.10345Comment: Final JMLR version, with improved discussions over v3. Main improvements in journal version over conference version (v2 appeared in ICLR): We proved the measure zero case for main theorem (with implications for the rates), and the multi-class case.
C. Wei, J. Lee, Q. Liu, and T. Ma. (2018)cite arxiv:1810.05369Comment: version 2: title changed from originally Ön the Margin Theory of Feedforward Neural Networks". Substantial changes from old version of paper, including a new lower bound on NTK sample complexity version 3: reorganized NTK lower bound proof.