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Oct, 2018
学习具有对称输入的两层神经网络
Learning Two-layer Neural Networks with Symmetric Inputs
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Rong Ge, Rohith Kuditipudi, Zhize Li, Xiang Wang
TL;DR
提出了一种学习两层神经网络的新算法,仅需对称输入的条件下,使用基于矩估计的方法结合张量分解的扩展与谱算法,可以在许多对称输入分布下更少的样本数量下稳健地恢复神经网络的参数。
Abstract
We give a new algorithm for learning a
two-layer neural network
under a general class of
input distributions
. Assuming there is a ground-truth two-layer network $$ y = A \sigma(Wx) + \xi, $$ where $A,W$ are weigh
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