Sobolev Training with Approximated Derivatives for Black-Box Function Regression with Neural Networks

Author:

Kissel Matthias,Diepold Klaus

Publisher

Springer International Publishing

Reference27 articles.

1. Czarnecki, W.M., Osindero, S., Jaderberg, M., Swirszcz, G., Pascanu, R.: Sobolev training for neural networks. In: Advances in Neural Information Processing Systems, pp. 4278–4287 (2017)

2. Drucker, H., Le Cun, Y.: Double backpropagation increasing generalization performance. In: IJCNN-91-Seattle International Joint Conference on Neural Networks, vol. 2, pp. 145–150. IEEE (1991)

3. Dua, D., Graff, C.: UCI machine learning repository (2017). http://archive.ics.uci.edu/ml

4. Gerritsma, J., Onnink, R., Versluis, A.: Geometry, resistance and stability of the Delft systematic Yacht hull series. Int. Shipbuild. Prog. 28(328), 276–297 (1981)

5. Hornik, K., Stinchcombe, M., White, H.: Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Netw. 3(5), 551–560 (1990)

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