Clearly lack of a sound theoretical basis or proof for why deep learning works has not stopped its proliferation. For a practitioner, the proof is in the pudding: generalized results, novel solutions that provably work, new designs that fulfill the given objective(s). At the end of the day, those are what really matter for practical applications.