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The Ethereal
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations
May 23, 2016 ยท Declared Dead ยท ๐ Neural Information Processing Systems
"No code URL or promise found in abstract"
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Authors
Behnam Neyshabur, Yuhuai Wu, Ruslan Salakhutdinov, Nathan Srebro
arXiv ID
1605.07154
Category
cs.LG: Machine Learning
Cross-listed
cs.NE
Citations
32
Venue
Neural Information Processing Systems
Last Checked
3 months ago
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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