Learning One-hidden-layer ReLU Networks via Gradient Descent

20 Jun 2018Xiao ZhangYaodong YuLingxiao WangQuanquan Gu

We study the problem of learning one-hidden-layer neural networks with Rectified Linear Unit (ReLU) activation function, where the inputs are sampled from standard Gaussian distribution and the outputs are generated from a noisy teacher network. We analyze the performance of gradient descent for training such kind of neural networks based on empirical risk minimization, and provide algorithm-dependent guarantees... (read more)

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