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A theoretical performance analysis of the graph neural network (GNN) is presented. For classification tasks, the neural network approach has the advantage in terms of flexibility that it can be employed in a data-driven manner, whereas Bayesian inference requires the assumption of a specific model. A fundamental question is then whether GNN has a high accuracy in addition to this flexibility.

Neural Information Processing Systems http://nips.cc/

We would like to point out that K-means is used only once to17 initialize the representativesets and isnot anintrinsic component ofthe online algorithm. What is important to observe21 is that N is kept constant throughout in order to reduce the storage footprint and to ensure low-complexity online22 processing. Tomaintain the list constant, for every added point another point is removed. Also, the reviewer is correct24 in observing thatN does not feature in the convergence results, which are asymptotic and do not imply anything25 about the convergence rate. Clearly, if the point dimensionm is large, it is beneficial to increaseN.

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(Theorem 4). We also consider Assumption 3. Note that this assumption does not restrict the class of considered Iterative algorithms are traditionally evaluated based on their gradient complexity .