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Deep echo state network (deepesn): A brief survey
The study of deep recurrent neural networks (RNNs) and, in particular, of deep Reservoir
Computing (RC) is gaining an increasing research attention in the neural networks …
Computing (RC) is gaining an increasing research attention in the neural networks …
Deep randomized neural networks
Abstract Randomized Neural Networks explore the behavior of neural systems where the
majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical …
majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical …
Minimum complexity echo state network
Reservoir computing (RC) refers to a new class of state-space models with a fixed state
transition structure (the reservoir) and an adaptable readout form the state space. The …
transition structure (the reservoir) and an adaptable readout form the state space. The …
Echo state property of deep reservoir computing networks
In the last years, the Reservoir Computing (RC) framework has emerged as a state of-the-art
approach for efficient learning in temporal domains. Recently, within the RC context, deep …
approach for efficient learning in temporal domains. Recently, within the RC context, deep …
An experimental characterization of reservoir computing in ambient assisted living applications
In this paper, we present an introduction and critical experimental evaluation of a reservoir
computing (RC) approach for ambient assisted living (AAL) applications. Such an empirical …
computing (RC) approach for ambient assisted living (AAL) applications. Such an empirical …
Sparse random neural networks for online anomaly detection on sensor nodes
Whether it is used for predictive maintenance, intrusion detection or surveillance, on-device
anomaly detection is a very valuable functionality in sensor and Internet-of-things (IoT) …
anomaly detection is a very valuable functionality in sensor and Internet-of-things (IoT) …
Markovian architectural bias of recurrent neural networks
In this paper, we elaborate upon the claim that clustering in the recurrent layer of recurrent
neural networks (RNNs) reflects meaningful information processing states even prior to …
neural networks (RNNs) reflects meaningful information processing states even prior to …
Blind construction of optimal nonlinear recursive predictors for discrete sequences
We present a new method for nonlinear prediction of discrete random sequences under
minimal structural assumptions. We give a mathematical construction for optimal predictors …
minimal structural assumptions. We give a mathematical construction for optimal predictors …
Simple deterministically constructed cycle reservoirs with regular jumps
A new class of state-space models, reservoir models, with a fixed state transition structure
(the “reservoir”) and an adaptable readout from the state space, has recently emerged as a …
(the “reservoir”) and an adaptable readout from the state space, has recently emerged as a …
Financial volatility trading using recurrent neural networks
We simulate daily trading of straddles on financial indexes. The straddles are traded based
on predictions of daily volatility differences in the indexes. The main predictive models …
on predictions of daily volatility differences in the indexes. The main predictive models …