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Spurious valleys in the error surface of recurrent networks--analysis and avoidance
Jason Horn1, Orlando De Jesús, Martin T Hagan
1Agilent Technologies High Frequency Technology Center, Santa Clara, CA 95051 USA. jason@jasonhorn.com
IEEE Transactions on Neural Networks
|March 11, 2009
Summary
Training recurrent neural networks is difficult due to spurious valleys in error surfaces. Analyzing random polynomial roots reveals the cause, offering improved batch training methods for faster, more reliable results.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Deep Learning
Background:
- Recurrent neural networks (RNNs) are powerful tools for sequential data but pose training challenges.
- Understanding the dynamics of RNN training is crucial for improving model performance and reliability.
Purpose of the Study:
- To analyze the error surfaces of recurrent networks and identify the causes of training difficulties.
- To investigate the role of spurious valleys in hindering effective network training.
- To propose enhanced batch training strategies to mitigate these issues.
Main Methods:
- Detailed analysis of error surface landscapes in recurrent networks.
- Mathematical investigation of random polynomial roots to understand valley formation mechanisms.
- Development and evaluation of modified batch training procedures.
Main Results:
- Identified numerous spurious valleys within the error surfaces of analyzed recurrent networks.
- Demonstrated that the emergence of these valleys can be explained by the properties of random polynomial roots.
- Proposed batch training improvements that effectively navigate or avoid spurious valleys.
Conclusions:
- Spurious valleys are a significant impediment to efficient recurrent network training.
- Analysis of random polynomial roots provides a theoretical framework for understanding these phenomena.
- The suggested batch training enhancements offer a practical solution for improved training speed and reliability in recurrent networks.
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