Unfortunately, the notion is seriously flawed on two counts [8]. First, it doesn't ensure fairness.

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Machine learning has provided a universal toolbox enhancing the decision making in many disciplines from advertising and recommender systems toeducation andcriminal justice.

Consequently, optimization objectives for learning such high capacity models have many global minima that fit training data perfectly.

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Examples include AdaGrad, RMSProp, and Adam.

The problem of establishing maps (e.g., point correspondences or transformations) among a collection of objects is connected with a wide range of scientific problems, including fusing partially overlapped

This generalization ability cannot be explained by the capacity of the explicitly specified model class (namely, the functions representable in the chosen architecture). Instead, it seems that the optimization algorithm biases us toward a "simple" model, minimizing

We enourage readers to consult the more complete manuscript on the arXiv.

SGD, even with various recently suggested initialization schemes.