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The contributions from the different papers can be integrated into a more general list of global contributions of the thesis: • The new deep learning models are based on the assembly of known layers in a lego-like form, which offer endless possibilities to explore new architectures. This creates opportunities for the application of these models to networking, as evidenced by the deep learning models proposed in this thesis [2][3][4][5]. • Many well-known ML models that were not originally thought to be applied in networking can be successfully modified to be used in this important area, by performing data transformations or adaptations of the original model. This thesis demonstrates several of these adaptations/transformations [1][2][3][4][5]. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 82PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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