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The Ethereal
Data-Dependent Path Normalization in Neural Networks
November 20, 2015 ยท Declared Dead ยท ๐ International Conference on Learning Representations
"No code URL or promise found in abstract"
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Authors
Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro
arXiv ID
1511.06747
Category
cs.LG: Machine Learning
Citations
23
Venue
International Conference on Learning Representations
Last Checked
5 months ago
Abstract
We propose a unified framework for neural net normalization, regularization and optimization, which includes Path-SGD and Batch-Normalization and interpolates between them across two different dimensions. Through this framework we investigate issue of invariance of the optimization, data dependence and the connection with natural gradients.
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