
Linear RNNs combined with MLPs provide a powerful architecture for sequence modeling, where the linear RNN encodes input sequences losslessly and the MLP performs non-linear processing. The use of complex eigenvalues in the recurrence enhances memory capabilities and information retention.


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universality-of-linear-recurrences-followed-by-non-linear-projections-understanding-complex-eigenvalues-in-rnns


Universality of Linear Recurrences Followed by Non-linear Projections: Understanding Complex Eigenvalues in RNNs


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Kombination von linearen RNNs und MLPs ermöglicht universelle Approximation von Sequenz-zu-Sequenz-Abbildungen.


universality-of-linear-recurrences-followed-by-non-linear-projections-insights-and-validation


Universality of Linear Recurrences Followed by Non-linear Projections: Insights and Validation
