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I know that neural networks including the deeper one perform better when we augment the dataset size, I wonder which of the other machine learning algorithms used for regression is sensitive to...
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If we have a complex utility function: could we use an hybrid (two layer) approach for decomposition, such that for the first layer we use a primal decomposition (based on a resource variable),...
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If yes, so why this problem is always, in the best of my knowledge , resolved using the lagrangian, gradients and sub gradients? And for a theory of decomposition (master or dual), is it...
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I have data input for a neural network with one output, the data range between 0 and X, I do not know the exact value of X because it changes with a time, I mean may be at a time t the max value...
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