In this paper we propose an empirical analysis of deep recurrent neural networks (RNNs) with stacked layers. The analysis aims at the study and proposal of approaches to develop and enhance multiple time-scale and hierarchical dynamics in deep recurrent architectures, within the efficient Reservoir Computing (RC) approach for RNN modeling. Results point out the actual relevance of layering and RC parameters aspects on the diversification of temporal representations in deep recurrent models.
Deep reservoir computing: A critical analysis
GALLICCHIO, CLAUDIO;MICHELI, ALESSIO
2016-01-01
Abstract
In this paper we propose an empirical analysis of deep recurrent neural networks (RNNs) with stacked layers. The analysis aims at the study and proposal of approaches to develop and enhance multiple time-scale and hierarchical dynamics in deep recurrent architectures, within the efficient Reservoir Computing (RC) approach for RNN modeling. Results point out the actual relevance of layering and RC parameters aspects on the diversification of temporal representations in deep recurrent models.File in questo prodotto:
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