  In this paper we study the learnability of deep random networks from both
theoretical and practical points of view. On the theoretical front, we show
that the learnability of random deep networks with sign activation drops
exponentially with its depth. On the practical front, we find that the
learnability drops sharply with depth even with the state-of-the-art training
methods, suggesting that our stylized theoretical results are closer to
reality.
