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- W3199619089 abstract "Feed-Forward Neural Networks are known to be universal approximators, meaning that they can approach any measurable function at any given level of precision, provided that their hidden layer is large enough. However, such property does not ensure practical convergence via training, and certain types of problems have been found to be best solved via other neural structures. Time-series analysis, for example, has been frequently associated with Recurrent Neural Networks or more recently LSTM architectures. However, are those endowed with the same universality as FFNN? Given that RNN universality has already been proven on measurable dynamic systems, this paper shows that any multivariate continuous function can be written as a dynamic system uploaded once per variable, thanks to the Kolmogorov Representation Theorem, hence concluding to the continuous universality of RNN architectures." @default.
- W3199619089 created "2021-09-27" @default.
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- W3199619089 date "2021-01-01" @default.
- W3199619089 modified "2023-09-25" @default.
- W3199619089 title "Dynamic Kolmogorov Approach to RNN Universality" @default.
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- W3199619089 doi "https://doi.org/10.2139/ssrn.3871272" @default.
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