D. DiVincenzo, and IBM. (2000)cite arxiv:quant-ph/0002077Comment: Prepared for Fortschritte der Physik special issue, Experimental Proposals for Quantum Computation. Version 2: many small corrections. Version 3: more small corrections & references added.
A. Roy. (2022)cite arxiv:2211.15021Comment: Contribution to the Proceedings of 41st International Conference on High Energy Physics - ICHEP2022, Presented on behalf of the FAIR4HEP collaboration.
M. Cranmer. (2023)cite arxiv:2305.01582Comment: 24 pages, 5 figures, 3 tables. Feedback welcome. Paper source found at https://github.com/MilesCranmer/pysr_paper ; PySR at https://github.com/MilesCranmer/PySR ; SymbolicRegression.jl at https://github.com/MilesCranmer/SymbolicRegression.jl.
J. Kaddour, A. Lynch, Q. Liu, M. Kusner, and R. Silva. (2022)cite arxiv:2206.15475Comment: 191 pages. v02. Work in progress. Feedback and comments are highly appreciated!.
J. Rauber, W. Brendel, and M. Bethge. (2017)cite arxiv:1707.04131Comment: Code and examples available at https://github.com/bethgelab/foolbox and documentation available at http://foolbox.readthedocs.io.
A. Rogers, O. Kovaleva, and A. Rumshisky. (2020)cite arxiv:2002.12327Comment: Accepted to TACL. Please note that the multilingual BERT section is only available in version 1.
J. Meyer, G. Passante, S. Pollock, and B. Wilcox. (2023)cite arxiv:2301.10882Comment: Accepted to The Physics Teacher. This copy has not yet been copy-edited.
C. Duhr, A. Huss, A. Mazeliauskas, and R. Szafron. (2021)cite arxiv:2106.04585Comment: 63 pages, 27 figures, for MiHO code see https://github.com/aykhuss/miho v2: updated references, other small changes, published version.
D. Hogg, and S. Villar. (2021)cite arxiv:2101.07256Comment: all code used to make the figures is available at https://github.com/davidwhogg/FlexibleLinearModels.
A. Hammad, M. Park, R. Ramos, and P. Saha. (2022)cite arxiv:2207.09959Comment: 15 pages, 5 figures. Code and instructions are available on https://github.com/AHamamd150/MLscanner.
T. Junk, and L. Lyons. (2020)cite arxiv:2009.06864Comment: 50 pages, 6 figures. Please see https://hdsr.mitpress.mit.edu/pub/32yz0u49/release/1 for a thoughtful comment by Andrew Fowlie, and https://hdsr.mitpress.mit.edu/pub/57tywz64/release/1 for the authors' response.
D. Roberts, S. Yaida, and B. Hanin. (2021)cite arxiv:2106.10165Comment: 471 pages, to be published by Cambridge University Press; v2: hyperlinks fixed, index added.
C. Farina, H. Tecocoatzi, A. Giachino, E. Santopinto, and E. Swanson. (2020)cite arxiv:2005.10850Comment: A numerical error in the state normalization has been corrected. New table and reference added. Discussion updated. Affiliation and acknowledgements updated.
S. Andreon, and M. Hurn. (2012)cite arxiv:1210.6232Comment: Invited review on "Statistical Analysis and Data Mining", a referred journal of the American Statistical Association. In press.
M. Sereno. (2015)cite arxiv:1509.05778Comment: 13 pages; LIRA package available from https://cran.r-project.org/web/packages/lira/index.html; further material at http://pico.bo.astro.it/~sereno/; v02: 14 pages, typos corrected, added references to change point analysis. In press on MNRAS.
A. Mantz. (2015)cite arxiv:1509.00908Comment: 11 pages, 5 figures, 2 tables. Code is available on GitHub at https://github.com/abmantz/lrgs and from CRAN.
R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra. (2016)cite arxiv:1610.02391Comment: This version was published in International Journal of Computer Vision (IJCV) in 2019; A previous version of the paper was published at International Conference on Computer Vision (ICCV'17).
J. Gasser, A. Rusetsky, and I. Scimemi. (2003)cite arxiv:hep-ph/0305260Comment: 19 pages (LaTex), 5 figures, published version. References in the introduction added, discussion shortened, 1 figure removed, conclusions unchanged.
J. Sanz-Serna. (2020)cite arxiv:2005.01336Comment: Some statements in the paper are misleading. It is possible to think of NUTS at not being a slice/Gibbs sampler and, with an alternative interpretation, it may be be possible to prove that the algorithm is correct. In addition the experiment reported in Figure 2 should have had many initial states drawn from the target rather than using a single value.
D. Hendrycks, and K. Gimpel. (2016)cite arxiv:1610.02136Comment: Published as a conference paper at ICLR 2017. 1 Figure in 1 Appendix. Minor changes from the previous version.
S. Szarek. (2003)cite arxiv:quant-ph/0310061Comment: 20 p., LATEX; an expanded version of the original submission: more background material from convexity and geometry of Banach spaces, more exhaustive bibliography and improved quality of references to the bibliography.
S. Udrescu, and M. Tegmark. (2019)cite arxiv:1905.11481Comment: 15 pages, 2 figs. Our code is available at https://github.com/SJ001/AI-Feynman and our Feynman Symbolic Regression Database for benchmarking can be downloaded at https://space.mit.edu/home/tegmark/aifeynman.html.