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Recurrent neural network-induced Gaussian process
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DOI：10.1016/j.neucom.2022.07.066.png)
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摘要
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In this study, we develop a recurrent neural network-induced Gaussian process (RNNGP) to model sequence data. We derive the equivalence between infinitely wide neural networks and Gaussian pro-cesses (GPs) for a relaxed recurrent neural network (RNN) with untied weights. We compute the covari-ance function of the RNNGP using an analytical iteration formula derived through the RNN procedure with an error-function-based activation function. To simplify our discussion, we use the RNNGP to per-form Bayesian inference on vanilla RNNs for various problems, such as Modified National Institute of Standards and Technology digit identification, Mackey-Glass time-series forecasting, and lithium-ion battery state-of-health estimation. The results demonstrate the flexibility of the RNNGP in modeling sequence data. Furthermore, the RNNGP predictions typically outperform those of the original RNNs and GPs, demonstrating the efficiency of the RNNGP as a data-driven model. Moreover, the RNNGP can quantify the uncertainty in the predictions, which implies the significant potential of the RNNGP in uncertainty quantification analyses.(c) 2022 Published by Elsevier B.V.
Keyword:
Recurrent neural network
Gaussian process
Uncertainty quantification
Machine learning
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
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