|
|
|
|
References
|
|
| |
| 1. |
Graves A., “Practical variational inference for neural networks”, Advances in neural information processing systems, 24, eds. J. Shawe-Taylor, R. Zemel, P. Barlett, et al., ACM, 2011, 2348–2356 |
| 2. |
Ha D., Dai A. M., Le Q. V., HyperNetworks, 2016, 29 pp., arXiv: 1609.09106 [cs.LG] (accessed January 25, 2021) |
| 3. |
Kuznetsov M. P., Tokmakova A. A., Strijov V. V., “Analytic and stochastic methods of structure parameter estimation”, Informatica, 27 (2016), 607–624    |
| 4. |
Strijov V. V., O. Yu. Bakhteev, “Deep learning model selection of suboptimal complexity”, Automat. Rem. Contr., 79:8 (2018), 1474–1488  |
| 5. |
Saxena S., Verbeek J., “Convolutional neural fabrics”, Advances in neural information processing systems, 29, eds. D. Lee, M. Sugiyama, U. Luxburg, et al., ACM, 2016, 4053–4061 |
| 6. |
Xie S., Zheng H., Liu C., Lin L., SNAS: Stochastic neural architecture search, 2019, 17 pp., arXiv: 1812.09926 [cs.LG] (accessed January 25, 2021) |
| 7. |
Wu B., Dai X., Zhang P., Wang Y., Sun F., Wu Y., Tian Y., Vajda P., Jia Y., Keutzer K., “FBNet: Hardware-aware efficient convnet design via differentiable neural architecture search”, IEEE/CVF Conference on Computer Vision and Pattern Recognition, v. 1, IEEE, 2019, 10726–10734 |
| 8. |
Lorraine J., Duvenaud D., Stochastic hyperparameter optimization through hypernetworks, 2018, 9 pp., arXiv: 1802.09419 |
| 9. |
LeCun Y., Cortes C., Burges C., The MNIST dataset of handwritten digits, 1998 http://yann.lecun.com/exdb/ (accessed January 25, 2021) |
