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arXiv · 1605.07154

Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations

Behnam Neyshabur·Yuhuai Wu·Ruslan Salakhutdinov·Nathan Srebro
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Abstract

We investigate the parameter-space geometry of recurrent neural networks (RNNs), and develop an adaptation of path-SGD optimization method, attuned to this geometry, that can learn plain RNNs with ReLU activations. On several datasets that require capturing long-term dependency structure, we show that path-SGD can significantly improve trainability of ReLU RNNs compared to RNNs trained with SGD, even with various recently suggested initialization schemes.

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Behnam Neyshabur, Yuhuai Wu, Ruslan Salakhutdinov, Nathan Srebro. 2016-05-23. Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations. https://arxiv.org/abs/1605.07154

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