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arXiv · 1903.09231

Recovering the Lowest Layer of Deep Networks with High Threshold Activations

Surbhi Goel·Rina Panigrahy
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Abstract

Giving provable guarantees for learning neural networks is a core challenge of machine learning theory. Most prior work gives parameter recovery guarantees for one hidden layer networks, however, the networks used in practice have multiple non-linear layers. In this work, we show how we can strengthen such results to deeper networks -- we address the problem of uncovering the lowest layer in a deep neural network under the assumption that the lowest layer uses a high threshold before applying the activation, the upper network can be modeled as a well-behaved polynomial and the input distribution is Gaussian.

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Surbhi Goel, Rina Panigrahy. 2019-03-21. Recovering the Lowest Layer of Deep Networks with High Threshold Activations. https://arxiv.org/abs/1903.09231

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