Thanks for your [2] link paper, it's an interesting one.
If you think about it, the high-level idea makes some intuitive sense. Since NNs are known to be "equivalent" to kernel methods - which are, to oversimplify, essentially nearest-neighbour + a similarity function - then the ability of NNs to memorise specific training examples can be analogised to adding another "neighbour" to interpolate from. So maybe it's not too surprising that NNs which can do this can have better generalisation performance. (Although it is still surprising, since I certainly wouldn't predict it a priori!)
Really, what is the difference between a "feature" and "memorising a data point"? It seems only a matter of scale - how much of the input space is "relevant" to the learned representation.
If you think about it, the high-level idea makes some intuitive sense. Since NNs are known to be "equivalent" to kernel methods - which are, to oversimplify, essentially nearest-neighbour + a similarity function - then the ability of NNs to memorise specific training examples can be analogised to adding another "neighbour" to interpolate from. So maybe it's not too surprising that NNs which can do this can have better generalisation performance. (Although it is still surprising, since I certainly wouldn't predict it a priori!)
Really, what is the difference between a "feature" and "memorising a data point"? It seems only a matter of scale - how much of the input space is "relevant" to the learned representation.