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Библиография

17. Библиография

  1. Stanley, K. O., & Miikkulainen, R. (2002). Evolving Neural Networks through Augmenting Topologies. Evolutionary Computation, 10(2), 99–127. https://doi.org/10.1162/106365602320169811

  2. Liu, H., Simonyan, K., & Yang, Y. (2019). DARTS: Differentiable Architecture Search. ICLR 2019. https://arxiv.org/abs/1806.09055

  3. Pham, H., Guan, M. Y., Zoph, B., Le, Q. V., & Dean, J. (2018). Efficient Neural Architecture Search via Parameter Sharing. ICML 2018. https://arxiv.org/abs/1802.03268

  4. Li, L., & Talwalkar, A. (2020). Random Search and Reproducibility for Neural Architecture Search. UAI 2020. https://arxiv.org/abs/1902.07638

  5. Real, E., Aggarwal, A., Huang, Y., & Le, Q. V. (2019). Regularized Evolution for Image Classifier Architecture Search. AAAI 2019. https://arxiv.org/abs/1802.01548

  6. Krizhevsky, A. (2009). Learning Multiple Layers of Features from Tiny Images. Technical Report, University of Toronto. https://www.cs.toronto.edu/~kriz/cifar.html

  7. He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. ICCV 2015. https://arxiv.org/abs/1502.01852 (Xavier/He инициализация)

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

  9. Kingma, D. P., & Ba, J. (2015). Adam: A Method for Stochastic Optimization. ICLR 2015. https://arxiv.org/abs/1412.6980

  10. Elsken, T., Metzen, J. H., & Hutter, F. (2019). Neural Architecture Search: A Survey. JMLR, 20(55), 1–21. https://arxiv.org/abs/1808.05377

  11. Such, F. P., Madhavan, V., Conti, E., Lehman, J., Stanley, K. O., & Clune, J. (2017). Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning. https://arxiv.org/abs/1712.06567

  12. Micikevicius, P., et al. (2018). Mixed Precision Training. ICLR 2018. https://arxiv.org/abs/1710.03740 (AMP)

Документ сгенерирован для проекта CNN-NEAT v0.1.0. Дата: 2026.