Initializing reservoirs with exhibitory and inhibitory signals using unsupervised learning techniques

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F13%3A86089064" target="_blank" >RIV/61989100:27240/13:86089064 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/13:86089064

Výsledek na webu

<a href="http://dx.doi.org/10.1145/2542050.2542087" target="_blank" >http://dx.doi.org/10.1145/2542050.2542087</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/2542050.2542087" target="_blank" >10.1145/2542050.2542087</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Initializing reservoirs with exhibitory and inhibitory signals using unsupervised learning techniques

Popis výsledku v původním jazyce

The trend of Reservoir Computing (RC) has been gaining prominence in the Neural Computation community since the 2000s. In a RC model there are at least two well-differentiated structures. One is a recurrent part called reservoir, which expands the inputdata and historical information into a high-dimensional space. This projection is carried out in order to enhance the linear separability of the input data. Another part is a memory-less structure designed to be robust and fast in the learning process. RC models are an alternative of Turing Machines and Recurrent Neural Networks to model cognitive processing in the neural system. Additionally, they are interesting Machine Learning tools to Time Series Modeling and Forecasting. Recently a new RC model was introduced under the name of Echo State Queueing Networks (ESQN). In this model the reservoir is a dynamical system which arises from the Queueing Theory. The initialization of the reservoir parameters may influence the model performanc

Název v anglickém jazyce

Initializing reservoirs with exhibitory and inhibitory signals using unsupervised learning techniques

Popis výsledku anglicky

The trend of Reservoir Computing (RC) has been gaining prominence in the Neural Computation community since the 2000s. In a RC model there are at least two well-differentiated structures. One is a recurrent part called reservoir, which expands the inputdata and historical information into a high-dimensional space. This projection is carried out in order to enhance the linear separability of the input data. Another part is a memory-less structure designed to be robust and fast in the learning process. RC models are an alternative of Turing Machines and Recurrent Neural Networks to model cognitive processing in the neural system. Additionally, they are interesting Machine Learning tools to Time Series Modeling and Forecasting. Recently a new RC model was introduced under the name of Echo State Queueing Networks (ESQN). In this model the reservoir is a dynamical system which arises from the Queueing Theory. The initialization of the reservoir parameters may influence the model performanc

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/EE2.3.30.0055" target="_blank" >EE2.3.30.0055: Nové kreativní týmy v prioritách vědeckého bádání</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

ACM International Conference Proceeding Series 2013

ISBN

978-1-4503-2454-0

ISSN

—

e-ISSN

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Počet stran výsledku

8

Strana od-do

53-60

Název nakladatele

ACM

Místo vydání

New York

Místo konání akce

Da Nang

Datum konání akce

5. 12. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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