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Is there a classification scenario with cross-entropy loss such that the loss as a function of the predictor/neural net's parameters is a function s.t it satisfies the properties of (a) having a global minima (b) being not convex and (c) its gradient being upperbounded?


The first and the third conditions I wrote above are the basic requirements for a SGD to converge to criticality for non-convex objective.

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