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Библиография

Нумерация согласована с PDF-отчётом (docs/pdf_builder/ch08_conclusion.py) начиная с [13]. Пункты [1]–[12] — ядро MkDocs (NEAT, DARTS, CIFAR, Adam, AMP).

  1. Stanley, K. O., & Miikkulainen, R. (2002). Evolving Neural Networks through Augmenting Topologies. Evolutionary Computation, 10(2), 99–127. doi

  2. Liu, H., Simonyan, K., & Yang, Y. (2019). DARTS: Differentiable Architecture Search. ICLR 2019. arXiv:1806.09055

  3. Pham, H., et al. (2018). Efficient Neural Architecture Search via Parameter Sharing. ICML 2018. arXiv:1802.03268

  4. Li, L., & Talwalkar, A. (2020). Random Search and Reproducibility for Neural Architecture Search. UAI 2020. arXiv:1902.07638

  5. Real, E., et al. (2019). Regularized Evolution for Image Classifier Architecture Search. AAAI 2019. arXiv:1802.01548

  6. Krizhevsky, A. (2009). Learning Multiple Layers of Features from Tiny Images. University of Toronto. CIFAR

  7. He, K., et al. (2015). Delving Deep into Rectifiers. ICCV 2015. arXiv:1502.01852

  8. Glorot, X., & Bengio, Y. (2010). Understanding the Difficulty of Training Deep Feedforward Neural Networks. AISTATS 2010.

  9. Kingma, D. P., & Ba, J. (2015). Adam: A Method for Stochastic Optimization. ICLR 2015. arXiv:1412.6980

  10. Elsken, T., Metzen, J. H., & Hutter, F. (2019). Neural Architecture Search: A Survey. JMLR 20(55). arXiv:1808.05377

  11. Such, F. P., et al. (2017). Deep Neuroevolution. arXiv:1712.06567

  12. Micikevicius, P., et al. (2018). Mixed Precision Training. ICLR 2018. arXiv:1710.03740

  13. Friedman, J. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics.

  14. Freund, Y., & Schapire, R. (1997). A Decision-Theoretic Generalization of On-Line Learning. Journal of Computer and System Sciences.

  15. Micikevicius, P., et al. (2018). Mixed Precision Training (повтор PDF-нумерации). ICLR. arXiv:1710.03740

  16. Chen, T., & Guestrin, C. (2016). XGBoost. KDD. arXiv:1603.02754

  17. Ke, G., et al. (2017). LightGBM. NeurIPS.

  18. Prokhorenkova, L., et al. (2018). CatBoost. NeurIPS. arXiv:1706.09516

  19. Holland, J. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.

  20. De Jong, K. (1975). An Analysis of the Behavior of a Class of Genetic Adaptive Systems. PhD thesis, University of Michigan.

  21. Koza, J. (1992). Genetic Programming. MIT Press.

  22. Miller, J., & Thomson, P. (2000). Cartesian Genetic Programming. EuroGP.

  23. Suganuma, M., et al. (2017). A Genetic Programming Approach to Designing CNN Architectures. GECCO. arXiv:1704.00764

  24. Beyer, H., & Schwefel, H. (2002). Evolution Strategies. Natural Computing.

  25. Hansen, N. (2006). The CMA Evolution Strategy: A Comparing Review.

  26. Salimans, T., et al. (2017). Evolution Strategies as a Scalable Alternative to Reinforcement Learning. arXiv:1703.03864

  27. Belew, R., et al. (1991). Evolving Networks: Using the Genetic Algorithm with Connectionist Learning.

  28. Yao, X. (1999). Evolving Artificial Neural Networks. Proc. IEEE.

  29. Gruau, F. (1994). Automatic Definition of Modular Neural Networks. Adaptive Behavior.

  30. Stanley, K., et al. (2009). A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks (HyperNEAT). Artificial Life.

  31. Miikkulainen, R., et al. (2017/2019). Evolving Deep Neural Networks (CoDeepNEAT). arXiv:1703.00548

  32. Gaier, A., & Ha, D. (2019). Weight Agnostic Neural Networks. NeurIPS. arXiv:1906.04358

  33. Zoph, B., & Le, Q. (2017). Neural Architecture Search with Reinforcement Learning (NASNet). ICLR. arXiv:1611.01578

  34. Cai, H., et al. (2019). ProxylessNAS. ICLR. arXiv:1812.00332

  35. Xu, Y., et al. (2020). PC-DARTS. ICLR. arXiv:1907.05737

  36. Tan, M., & Le, Q. (2019). EfficientNet. ICML. arXiv:1905.11946

  37. He, K., et al. (2016). Deep Residual Learning for Image Recognition. CVPR. arXiv:1512.03385

  38. Razavian, A., et al. (2014). CNN Features Off-the-Shelf. CVPR Workshops. arXiv:1403.6382

  39. Zagoruyko, S., & Komodakis, N. (2016). Wide Residual Networks. BMVC. arXiv:1605.07146

  40. Huang, G., et al. (2017). Densely Connected Convolutional Networks. CVPR. arXiv:1608.06993

  41. Real, E., et al. (2017). Large-Scale Evolution of Image Classifiers. ICML. arXiv:1703.01041

Полный ГОСТ-список PDF: источники [13]–[52] в docs/pdf_builder/ch08_conclusion.py.