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Bidirectional linear recurrent models for sequence-level multisource fusion
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DOI：10.1016/j.inffus.2025.103601.png)
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Abstract
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• Propose a bidirectional linear recurrent model to leverage forward and backward LRUs. • This method provably shows its universality in sequence approximation and stability. • Validate our method with sequential images and timeseries datasets and many baselines. • Introduce the first LRU based benchmark for timeseries forecasting.
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Universal approximation using feedforward neural networks: A survey of some existing methods, and some new results.png)
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