Results 41 to 50 of about 317,020 (282)
Dynamic clustering of time series with Echo State Networks [PDF]
, 2019 In this paper we introduce a novel methodology for unsupervised analysis of time series, based upon the iterative implementation of a clustering algorithm embedded into the evolution of a recurrent Echo State Network.
A Saxena +10 more
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Distance-Based Delays in Echo State Networks
, 2022 Physical reservoir computing, a paradigm bearing the promise of energy-efficient high-performance computing, has raised much attention in recent years. We argue though, that the effect of signal propagation delay on reservoir task performance, one of the most central aspects of physical reservoirs, is still insufficiently understood in a more general ...
Stefan Iacob +2 more
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Journal of Clinical and Translational ScienceOBJECTIVES/GOALS: Launch a case-based learning collaborative on best practices that meet social, emotional and physical health needs of underserved communities as they relate to environmental toxins—specifically those related to the train derailment in ...
R. Ellen Hogentogler +4 more
doaj +1 more source
Hyperparameter tuning in echo state networks
Proceedings of the Genetic and Evolutionary Computation Conference, 2022 Echo State Networks represent a type of recurrent neural network with a large randomly generated reservoir and a small number of readout connections trained via linear regression. The most common topology of the reservoir is a fully connected network of up to thousands of neurons.
openaire +2 more sources
Comparison of echo state network output layer classification methods on noisy data
, 2017 Echo state networks are a recently developed type of recurrent neural network where the internal layer is fixed with random weights, and only the output layer is trained on specific data.
Prater, Ashley
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Using Echo State Networks for Cryptography
, 2017 Echo state networks are simple recurrent neural networks that are easy to implement and train. Despite their simplicity, they show a form of memory and can predict or regenerate sequences of data.
Bauckhage, Christian +3 more
core +1 more source
Memory-Enhanced Evolutionary Robotics: The Echo State Network Approach [PDF]
, 2009 International audienceInterested in Evolutionary Robotics, this paper focuses on the acquisition and exploitation of memory skills. The targeted task is a well-studied benchmark problem, the Tolman maze, requiring in principle the robotic controller to ...
Bredeche, Nicolas +2 more
core +3 more sources
Fading memory echo state networks are universal
Neural Networks, 2021 Echo state networks (ESNs) have been recently proved to be universal approximants for input/output systems with respect to various $L ^p$-type criteria. When $1\leq p< \infty$, only $p$-integrability hypotheses need to be imposed, while in the case $p=\infty$ a uniform boundedness hypotheses on the inputs is required.
Lukas Gonon, Juan-Pablo Ortega
openaire +5 more sources
Gated Echo State Networks: a preliminary study [PDF]
2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA), 2020 Gating mechanisms are widely used in the context of Recurrent Neural Networks (RNNs) to improve the network's ability to deal with long-term dependencies within the data. The typical approach for training such networks involves the expensive algorithm of gradient descent and backpropagation.
Di Sarli D., Gallicchio C., Micheli A.
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Disrupted resting-state brain network properties in obesity: decreased global and putaminal cortico-striatal network efficiency. [PDF]
, 2016 BACKGROUND: The efficient organization and communication of brain networks underlie cognitive processing and their disruption can lead to pathological behaviours. Few studies have focused on whole-brain networks in obesity and binge eating disorder (BED).
Baek, K, Kundu, P, Morris, LS, Voon, V
core +2 more sources