Abstract:Classical results establish that ensembles of small models benefit when predictive diversity is encouraged, through bagging, boosting, and similar. Here we demonstrate that this intuition does not carry over to ensembles of deep neural networks used for classification, and-in fact-the opposite can be true. Unlike regression models or small (unconfident) classifiers, predictions from large (confident) neural networks concentrate in vertices of the probability simplex. Thus, decorrelating these points necessaril… Show more
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