Image Reconstruction Using Cellular Automata and Neural Networks

Author:

Loghin Mihai-AdrianORCID,Andreica Anca-MirelaORCID

Publisher

Springer Nature Switzerland

Reference12 articles.

1. Chai, T., Draxler, R.: Root mean square error (RMSE) or mean absolute error (MAE)? Geosci. Model Dev. 7, 1247–1250 (2014). https://doi.org/10.5194/gmdd-7-1525-2014

2. Geron, A.: Hands-on Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media, Sebastopol (2017)

3. Gilpin, W.: Cellular automata as convolutional neural networks. Phys. Rev. E 100(3), 032402 (2019). https://doi.org/10.1103/physreve.100.032402

4. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Teh, Y.W., Titterington, M. (eds.) Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 9, pp. 249–256. PMLR, Chia Laguna Resort, Sardinia (2010). https://proceedings.mlr.press/v9/glorot10a.html

5. LOUIS, P.Y., Nardi, F.R.: Probabilistic Cellular Automata. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-319-65558-1. https://hal.archives-ouvertes.fr/hal-01724251

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