Title: Tuning the burn-in phase in training recurrent neural networks improves their performance
Primary area: learning theory
Keywords: RNN, training, learning theory, optimization

Abstract: Training recurrent neural networks (RNNs) with standard backpropagation through time (BPTT) can be challenging, especially in the presence of long input sequences. A practical alternative to reduce computational and memory overhead is to perform BPTT repeatedly over shorter segments of the training data set, corresponding to truncated BPTT. In this paper, we examine the training of RNNs when using such a truncated learning approach for time series tasks. Specifically, we establish theoretical bounds on the accuracy and performance loss when optimizing over subsequences instead of the full data sequence. This reveals that the burn-in phase of the RNN is an important tuning knob in its training, with significant impact on the performance guarantees. We validate our theoretical results through experiments on standard benchmarks from the fields of system identification and time series forecasting. In all experiments, tuning the burn-in phase during training improves the performance on the test data, suggesting that the burn-in phase should generally be considered as a hyperparameter of RNN training.

TL;DR: We establish theoretical bounds on the performance loss when using truncated sequences for RNN training and show how they improve when choosing a suitable burn-in phase.