Feb. 9, 2024, 5:42 a.m. | Zhengqing Wu Berfin Simsek Francois Ged

cs.LG updates on arXiv.org arxiv.org

In this paper, we investigate the loss landscape of one-hidden-layer neural networks with ReLU-like activation functions trained with the empirical squared loss. As the activation function is non-differentiable, it is so far unclear how to completely characterize the stationary points. We propose the conditions for stationarity that apply to both non-differentiable and differentiable cases. Additionally, we show that, if a stationary point does not contain "escape neurons", which are defined with first-order conditions, then it must be a local minimum. …

cs.lg differentiable embedding function functions hidden landscape layer loss network networks neural networks paper relu

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