Author:
Schmieg Tobias,Lanquillon Carsten
Publisher
Springer Nature Switzerland
Reference56 articles.
1. Al-Shabandar, R., Jaddoa, A., Liatsis, P., Hussain, A.J.: A deep gated recurrent neural network for petroleum production forecasting. Mach. Learn. Appli. 3, 100013 (2021). https://doi.org/10.1016/j.mlwa.2020.100013
2. Aouad, M., Hajj, H., Shaban, K., Jabr, R.A., El-Hajj, W.: A cnn-sequence-to-sequence network with attention for residential short-term load forecasting. Electric Power Sys. Res. 211 (2022).https://doi.org/10.1016/j.epsr.2022.108152
3. Bao, H., Dong, L., Piao, S., Wei, F.: BEiT: BERT Pre-Training of Image Transformers. In: The Tenth International Conference on Learning Representations, ICLR 2022. OpenReview.net (2022), https://openreview.net/forum?id=p-BhZSz59o4
4. Bengio, Y., Courville, A., Vincent, P.: Representation learning: A review and new perspectives. IEEE Trans. Pattern Analy. Mach. Intell. 35, 1798–1828 (2013). https://doi.org/10.1109/TPAMI.2013.50
5. Biondi, R., et al.: Multivariate multi-step convection nowcasting with deep neural networks: the novara case study. In: International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2022-July, pp. 6598–6601. Institute of Electrical and Electronics Engineers Inc. (2022).https://doi.org/10.1109/IGARSS46834.2022.9883665