Читать книгу Computational Statistics in Data Science - Группа авторов - Страница 88

References

Оглавление

1 1 Larochelle, H., Bengio, Y., Louradour, J., and Lamblin, P. (2009) Exploring strategies for training deep neural networks. J. Mach. Learn. Res., 1, 1–40.

2 2 Hinton, G.E. and Salakhutdinov, R.R. (2006) Reducing the dimensionality of data with neural networks. Science, 313, 504–507.

3 3 Hastie, T., Tibshirani, R., and Friedman, J. (2002) The Elements of Statistical Learning, Springer, New York.

4 4 Boyd, S., Boyd, S.P., and Vandenberghe, L. (2004) Convex Optimization, Cambridge university press.

5 5 Nocedal, J. and Wright, S. (2006) Numerical Optimization, Springer Science & Business Media.

6 6 Izenman, A.J. (2008) Modern multivariate statistical techniques. Regression Classif. Manifold Learn., 10, 978–980.

7 7 Gori, M. and Tesi, A. (1992) On the problem of local minima in backpropagation. IEEE Trans. Pattern Anal. Mach. Intell., 14, 76–86.

8 8 LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998) Gradient‐based learning applied to document recognition. Proc. IEEE, 86, 2278–2324.

9 9 LeCun, Y. (1998) The MNIST Database of Handwritten Digits, http://yann.lecun.com/exdb/mnist/ (accessed 20 April 2021).

10 10 Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012) Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst., 25, 1097–1105.

11 11 Simonyan, K. and Zisserman, A. (2014) Very deep convolutional networks for large‐scale image recognition. arXiv preprint arXiv:1409.1556.

12 12 He, K., Zhang, X., Ren, S., and Sun, J. (2016) Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778.

13 13 Goodfellow, I., Bengio, Y., and Courville, A. (2016) Deep Learning, MIT Press.

14 14 Krizhevsky, A. (2009) Learning multiple layers of features from tiny images.

15 15 Bickel, P.J., Li, B., Tsybakov, A.B. et al. (2006) Regularization in statistics. Test, 15, 271–344.

16 16 Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1986) Learning Internal Representations by Error Propagation. Tech. report. California Univ San Diego La Jolla Inst for Cognitive Science.

17 17 Kingma, D.P. and Welling, M. (2014) Auto‐Encoding Variational Bayes. International Conference on Learning Representations.

18 18 Kullback, S. and Leibler, R.A. (1951) On information and sufficiency. Ann. Math. Stat., 22, 79–86.

19 19 Hochreiter, S. and Schmidhuber, J. (1997) Long short‐term memory. Neural Comput., 9, 1735–1780.

20 20 Gers, F., Schmidhuber, J., and Cummins, F. (1999) Learning to Forget: Continual Prediction with LSTM. 1999 Ninth International Conference on Artificial Neural Networks ICANN 99. (Conf. Publ. No. 470), vol. 2, pp. 850–855.

Computational Statistics in Data Science

Подняться наверх