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About this paper
Julian D. Schiller, Malte Heinrich, Victor G. Lopez, Matthias A. Müller
Research from Leibniz University Hannover quantifies the performance loss of training recurrent neural networks with zero-initialized truncated backpropagation through time using optimal control theory. By introducing and systematically tuning a burn-in phase, the method reduces prediction errors by over 60% on real-world datasets.
Key insights
- The performance gap, or regret, between zero-initialized TBPTT and an ideal benchmark diminishes exponentially with the length of the burn-in phase, proportional to "lambda^m" where ">lambda" is the network's forgetting rate.
- Optimal control theory's output turnpike property can be effectively applied to analyze RNN training dynamics, providing a rigorous foundation for understanding and optimizing the burn-in phase.
- Even with zero hidden state initialization, properly tuned TBPTT can achieve performance comparable to or better than more complex alternatives like stateful TBPTT or full BPTT, while maintaining computational efficiency.
Summary by alphaXiv.
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