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Figure 2. Details on the loss function elements for the VGM model. 3.3. Variants of the model We have explored three options for the general VGM model discussed in the previous section. 3.3.1. Option A. Model with Gaussian and Bernoulli distributions In this option we have the vector of labels as input to the network and the vector of features as output. Figure 3 presents option A. We use a multivariate Gaussian as the distribution for ๐(๐/๐ณ), with a mean ๐(๐ณ)โกand a diagonal covariance matrix: ๐ฎ(๐ณ) โ โกโก ๐๐(๐ณ),โกwith different values along the diagonal. We ๐ have a standard normal ๐ต(๐, ๐ฐ) as the prior distribution for Z. ฬ For the distribution ๐ฉ(๐/๐) we use a multivariate Bernoulli distribution. The nice property about the Bernoulli distribution is that we do not require doing sampling on it, as the output parameter that characterizes the distribution is the mean that is the same as the probability of ฬ success. Then, in this case, the output of the last layer is taken as our final output ๐ฟ. ฬ The selection of distributions for ๐(๐/๐ณ) and ๐ฉ(๐/๐) is similar to the distributions selected in [1], since they are simple and provide good results. In Figure 3 we show also (in squared boxes) the elements of the loss function to be minimized for this option. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 168PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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