УДК 004.22+004.89

В.М. ТЕРЕЩЕНКО
Київський національний університет імені Тараса Шевченка, Київ, Україна,
 vtereshch@gmail.com

П.А. ЗАКАЛА
Київський національний університет імені Тараса Шевченка, Київ, Україна,
 pzakala@gmail.com

ПОШУК БАЗОВОЇ МНОЖИНИ ДЛЯ ЗАДАЧ
МАШИННОГО НАВЧАННЯ

СПИСОК ЛІТЕРАТУРИ

1. Pankaj K. Agarwal S. Har-Peled, K. R. Varadarajan. Geometric approximation via coresets. In: Combinatorial and Computational Geometry. Goodman J.E., Pach J., Welzl E. (Eds.). New York: Cambridge University Press, 2005. P. 1–30.


2. Jubran I., Maalouf A., Feldman D. Introduction to coresets: accurate coresets. arXiv:1910.08707v1 [cs.LG] 19 Oct 2019. https://doi.org/10.48550/arXiv.1910.08707.


3. Agarwal P.K., Har-Peled S., Varadarajan K.R. Approximating extent measures of points. Journal of the ACM. 2004. Vol. 51, Iss. 4. P. 606–635. https://doi.org/10.1145/1008731.1008736.


4. Barbiero P., Squillero G., Tonda A. Evolutionary discovery of coresets for classification. Proc. Genetic and Evolutionary Computation Conference Companion (13–17 July 2019, Prague, Czech Republic). Prague, 2019. P. 1747–1754. https://doi.org/10.1145/3319619.3326846.


5. Barbiero P., Squillero G., Tonda A. Uncovering coresets for classification with multi-objective evolutionary algorithms. arXiv:2002.08645v1 [cs.LG] 20 Feb 2020. https://doi.org/10.48550/arXiv.2002.08645.


6. Deb K., Pratap A., Agarwal S., Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation. 2002. Vol. 6, Iss. 2, P. 182–197. https://doi.org/10.1109/4235.996017.


7. Sklearn datasets: Blobs dataset. URL: https://scikit-learn.org/stable/.


8. Sklearn datasets: Circles dataset. URL: https://scikit-learn.org/stable/modules/generated/.


9. Sklearn datasets: Moons dataset. URL: https://scikit-learn.org/stable/modules/generated/.


10. Iris Data Set. URL: https://archive.ics.uci.edu/ml/datasets/iris .


11. Hoerl A.E., Kennard R.W. Ridge regression: biased estimation for nonorthogonal problems. Technometrics. 2000. Vol. 42, Iss. 1. P. 80–86. https://doi.org/10.2307/1271436.


12. Yu R., Liu S., Wang X. Dataset distillation: a comprehensive review. arXiv:2301.07014v2 [cs.LG] 21 Jan 2023. https://doi.org/10.48550/arXiv.2301.07014.


13. Maclaurin D., Duvenaud D., Adams R.P. Gradient-based hyperparameter optimization through reversible learning. arXiv:1502.03492v3 [stat.ML] 2 Apr 2015. https://doi.org/10.48550/arXiv.1502.03492.


14. Wang T., Zhu J.-Y., Torralba A., Efros A.A. Dataset distillation. arXiv:1811.10959v3 [cs.LG] 24 Feb 2018. https://doi.org/10.48550/arXiv.1811.10959.


15. Hinton G., Vinyals O., Dean J. Distilling the knowledge in a neural network. arXiv:1503.02531v1 [stat.ML] 9 Mar 2015. https://doi.org/10.48550/arXiv.1503.02531.


16. Zhao B., Mopuri K.R., Bilen H. Dataset condensation with gradient matching. arXiv:2006.05929v3 [cs.CV] 8 Mar 2021. https://doi.org/10.48550/arXiv.2006.05929.


17. Cazenavette G., Wang T., Torralba A., Efros A.A., Zhu J.-Y. Dataset distillation by matching training trajectories. arXiv:2203.11932v1 [cs.CV] 22 Mar 2022. https://doi.org/10.48550/arXiv.2203.11932.


18. Jiang Z., Gu J., Liu M., Pan D.Z. Delving into effective gradient matching for dataset condensation. arXiv:2208.00311v1 [cs.LG] 30 Jul 2022. https://doi.org/10.48550/arXiv.2208.00311.


19. Lee S., Chun S., Jung S., Yun S., Yoon S. Dataset condensation with contrastive signals. arXiv:2202.02916v3 [cs.CV] 16 Jun 2022. https://doi.org/10.48550/arXiv.2202.02916.


20. Zhao B., Bilen H. Dataset condensation with distribution matching. arXiv:2110.04181v3 [cs.LG] 22 Dec 2021. https://doi.org/10.48550/arXiv.2110.04181.


21. Gretton A., Borgwardt K.M, Rasch M., Scholkopf B., Smola A. A kernel two-sample test. The Journal of Machine Learning Research. 2012. N 13. P. 723–773.


22. Wang K., Zhao B., Peng X., Zhu Z., Yang S., Wang S., Huang G., Bilen H., Wang X., You Y. CAFE: learning to condense dataset by aligning features. arXiv:2203.01531v2 [cs.CV] 27 Mar 2022. https://doi.org/10.48550/arXiv.2203.01531.


23. Zhao B., Bilen H. Synthesizing informative training samples with GAN. arXiv:2204.07513v2 [cs.LG] 21 Dec 2022. https://doi.org/10.48550/arXiv.2204.07513.