Open AccessProceedings Article
No more pesky learning rates
Tom Schaul,Sixin Zhang,Yann LeCun +2 more
- pp 343-351
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TLDR
This paper proposed a method to automatically adjust multiple learning rates so as to minimize the expected error at any one time, which relies on local gradient variations across samples, making it suitable for non-stationary problems, and the resulting algorithm matches the performance of SGD or other adaptive approaches with their best settings obtained through systematic search.Abstract:
The performance of stochastic gradient descent (SGD) depends critically on how learning rates are tuned and decreased over time. We propose a method to automatically adjust multiple learning rates so as to minimize the expected error at any one time. The method relies on local gradient variations across samples. In our approach, learning rates can increase as well as decrease, making it suitable for non-stationary problems. Using a number of convex and non-convex learning tasks, we show that the resulting algorithm matches the performance of SGD or other adaptive approaches with their best settings obtained through systematic search, and effectively removes the need for learning rate tuning.read more
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Proceedings Article
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