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Fig. 2.


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Neural network architecture that we adopted in this work. The input is a vector of 16 variability features. Each linear unit contains one batch normalization layer and one fully connected layer. We use ReLU as an activation function between layers. The last linear unit has 21 outputs, which is the number of variable classes in our training set. We use the softmax function before the output layer to scale the outputs to lie between 0 and 1 in order to represent probabilities for the 21 output classes.

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