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Abstract In this paper, we provide a novel approach to the architectural design of deep Recurrent Neural Networks using signal frequency analysis. In particular, focusing on the Reservoir Computing framework and inspired by the principles related to the inherent effect of layering, we address a fundamental open issue in deep learning, namely the question of how to establish the number of layers in recurrent architectures in the form of deep echo state networks (DeepESNs). The proposed method is first analyzed and refined on a controlled scenario and then it is experimentally assessed on challenging real-world tasks. The achieved results also show the ability of properly designed DeepESNs to outperform RC approaches on a speech recognition task, and to compete with the state-of-the-art in time-series prediction on polyphonic music tasks. Highlights • We address the issue of architectural design for Deep Recurrent Neural Networks. • We focus on the frequency analysis for layering design. • We propose an efficient approach to choose the number of recurrent layers in DeepESN. • We show the empirical advantage of very Deep (> 30 recurrent layers) RNNs. [ABSTRACT FROM AUTHOR]

In this paper, we propose an empirical analysis of deep recurrent neural network (RNN) architectures with stacked layers. The main aim is to address some fundamental open research issues on the significance of creating deep layered architectures in RNN and to characterize the inherent hierarchical representation of time in such models, especially for efficient implementations. In particular, the analysis aims at the study and proposal of approaches to develop and enhance hierarchical dynamics in deep architectures within the efficient Reservoir Computing (RC) framework for RNN modeling. The effect of a deep layered organization of RC models is investigated in terms of both occurrence of multiple time-scale and increasing of richness of the dynamics. It turns out that a deep layering of recurrent models allows an effective diversification of temporal representations in the layers of the hierarchy, by amplifying the effects of the factors influencing the time-scales and the richness of the dynamics, measured as the entropy of recurrent units activations. The advantages of the proposed approach are also highlighted by measuring the increment of the short-term memory capacity of the RC models. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we present the Tree Echo State Network (TreeESN) model, generalizing the paradigm of Reservoir Computing to tree structured data. TreeESNs exploit an untrained generalized recursive reservoir, exhibiting extreme efficiency for learning in structured domains. In addition, we highlight through the paper other characteristics of the approach: First, we discuss the Markovian characterization of reservoir dynamics, extended to the case of tree domains, that is implied by the contractive setting of the TreeESN state transition function. Second, we study two types of state mapping functions to map the tree structured state of TreeESN into a fixed-size feature representation for classification or regression tasks. The critical role of the relation between the choice of the state mapping function and the Markovian characterization of the task is analyzed and experimentally investigated on both artificial and real-world tasks. Finally, experimental results on benchmark and real-world tasks show that the TreeESN approach, in spite of its efficiency, can achieve comparable results with state-of-the-art, although more complex, neural and kernel based models for tree structured data. [Copyright &y& Elsevier]

Abstract: Echo State Networks (ESNs) constitute an emerging approach for efficiently modeling Recurrent Neural Networks (RNNs). In this paper we investigate some of the main aspects that can be accounted for the success and limitations of this class of models. In particular, we propose complementary classes of factors related to contractivity and architecture of reservoirs and we study their relative relevance. First, we show the existence of a class of tasks for which ESN performance is independent of the architectural design. The effect of the Markovian factor, characterizing a significant class within these cases, is shown by introducing instances of easy/hard tasks for ESNs featured by contractivity of reservoir dynamics. In the complementary cases, for which architectural design is effective, we investigate and decompose the aspects of network design that allow a larger reservoir to progressively improve the predictive performance. In particular, we introduce four key architectural factors: input variability, multiple time-scales dynamics, non-linear interactions among units and regression in an augmented feature space. To investigate the quantitative effects of the different architectural factors within this class of tasks successfully approached by ESNs, variants of the basic ESN model are proposed and tested on instances of datasets of different nature and difficulty. Experimental evidences confirm the role of the Markovian factor and show that all the identified key architectural factors have a major role in determining ESN performances. [Copyright &y& Elsevier]

Recurrent Neural Networks (RNNs) represent a natural paradigm for modeling sequential data like text written in natural language. In fact, RNNs and their variations have long been the architecture of choice in many applications, however in practice they require the use of labored architectures (such as gating mechanisms) and computationally heavy training processes. In this paper we address the question of whether it is possible to generate sentence embeddings via completely untrained recurrent dynamics, on top of which to apply a simple learning algorithm for text classification. This would allow to obtain extremely efficient models in terms of training time. Our work investigates the extent to which this approach can be used, by analyzing the results on different tasks. Finally, we show that, within certain limits, it is possible to build extremely efficient models for text classification that remain competitive in accuracy with reference models in the state-of-the-art. [ABSTRACT FROM AUTHOR]

In this paper we address the problem of one day-ahead hourly electricity price forecast for smart grid applications. To this aim, we investigate the application of a number of predictive models for time-series, including methods based on empirical strategies frequently adopted in the smart grid community, Kalman Filters and Echo State Networks (ESNs). The considered methods have been suitably modified to address the electricity price forecast problem. Strategies based on daily re-adaptation of models’ parameters are taken into consideration as well. The predictive performance achieved by the considered models is assessed, and the methods are compared among each other on recent real data from the Italian electricity market. As a result of the comparison over three years data, ESN methods appear to provide the most accurate price predictions, which could imply significant economic savings in many smart grid activities, such as switching on power plants to support power generation from renewable sources, electric vehicle recharging or usage of household appliances. [ABSTRACT FROM AUTHOR]

Real-time user localization in indoor environments is an important issue in ambient assisted living (AAL). In this context, localization based on received signal strength (RSS) has received considerable interest in the recent literature, due to its low cost and energy consumption and to its availability on all wireless communication hardware. On the other hand, the RSS-based localization is characterized by a greater error with respect to other technologies. Restricting the problem to localization of AAL users in indoor environments, we demonstrate that forecasting with a little user movement advance (for example, when the user is about to leave a room) provides significant benefits to the accuracy of RSS-based localization systems. Specifically, we exploit echo state networks (ESNs) fed with RSS measurements and trained to recognize patterns of user's movements to feed back to the RSS-based localization system. [ABSTRACT FROM AUTHOR]