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On the Learnability of Deep Random Networks

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arxiv 1904.03866 v1 pith:32YMINR5 submitted 2019-04-08 cs.LG stat.ML

Abhimanyu Das , Sreenivas Gollapudi , Ravi Kumar , Rina Panigrahy This is my paper · ORCID
classification cs.LGstat.ML
keywords learnabilitydeepnetworksrandomtheoreticaldepthdropsfront
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 5 citations worldwide. Full citation record

  1. High Accuracy and High Fidelity Extraction of Neural Networks

    cs.LG 2019-09 conditional novelty 8.0 of 10

    Given only prediction access, an adversary can exactly recover the weights of a two-layer ReLU network, and semi-supervised learning makes accuracy extraction far more query-efficient.

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