Abstract
In this chapter we summarize the main points of our overview and draw our conclusions. We discuss our interpretations about the reasons behind the different results and performance achieved by the Recurrent Neural Network architectures analyzed. We conclude by hypothesizing possible guidlines for selecting suitable models depending on the specific task at hand.
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Reference
Kumar A, Irsoy O, Ondruska P, Iyyer M, Bradbury J, Gulrajani I, Zhong V, Paulus R, Socher R (2016) Ask me anything: Dynamic memory networks for natural language processing. In: Balcan MF, Weinberger KQ (eds) Proceedings of the 33rd International Conference on Machine Learning, PMLR, New York, USA, Proceedings of Machine Learning Research, vol 48, pp 1378–1387
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Bianchi, F.M., Maiorino, E., Kampffmeyer, M.C., Rizzi, A., Jenssen, R. (2017). Conclusions. In: Recurrent Neural Networks for Short-Term Load Forecasting. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-70338-1_8
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DOI: https://doi.org/10.1007/978-3-319-70338-1_8
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