FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3096347622> ?p ?o ?g. }

• W3096347622 endingPage "192" @default.
• W3096347622 startingPage "177" @default.
• W3096347622 abstract "Echo State Networks (ESNs) are efficient recurrent neural networks (RNNs) which have been successfully applied to time series modeling tasks. However, ESNs are unable to capture the history information far from the current time step, since the echo state at the present step of ESNs mostly impacted by the previous one. Thus, ESN may have difficulty in capturing the long-term dependencies of temporal data. In this paper, we propose an end-to-end model named Echo Memory-Augmented Network (EMAN) for time series classification. An EMAN consists of an echo memory-augmented encoder and a multi-scale convolutional learner. First, the time series is fed into the reservoir of an ESN to produce the echo states, which are all collected into an echo memory matrix along with the time steps. After that, we design an echo memory-augmented mechanism employing the sparse learnable attention to the echo memory matrix to obtain the Echo Memory-Augmented Representations (EMARs). In this way, the input time series is encoded into the EMARs with enhancing the temporal memory of the ESN. We then use multi-scale convolutions with the max-over-time pooling to extract the most discriminative features from the EMARs. Finally, a fully-connected layer and a softmax layer calculate the probability distribution on categories. Experiments conducted on extensive time series datasets show that EMAN is state-of-the-art compared to existing time series classification methods. The visualization analysis also demonstrates the effectiveness of enhancing the temporal memory of the ESN." @default.
• W3096347622 created "2020-11-09" @default.
• W3096347622 creator A5000458161 @default.
• W3096347622 creator A5020818383 @default.
• W3096347622 creator A5041645613 @default.
• W3096347622 creator A5045588004 @default.
• W3096347622 creator A5076609933 @default.
• W3096347622 creator A5089803281 @default.
• W3096347622 date "2021-01-01" @default.
• W3096347622 modified "2023-10-15" @default.
• W3096347622 title "Echo Memory-Augmented Network for time series classification" @default.
• W3096347622 cites W117918019 @default.
• W3096347622 cites W1469859527 @default.
• W3096347622 cites W1968354112 @default.
• W3096347622 cites W1975257359 @default.
• W3096347622 cites W1984516841 @default.
• W3096347622 cites W1984674851 @default.
• W3096347622 cites W2001263627 @default.
• W3096347622 cites W2012743186 @default.
• W3096347622 cites W2016571104 @default.
• W3096347622 cites W2024300411 @default.
• W3096347622 cites W2033268097 @default.
• W3096347622 cites W2035104901 @default.
• W3096347622 cites W2041645455 @default.
• W3096347622 cites W2050493487 @default.
• W3096347622 cites W2052624719 @default.
• W3096347622 cites W2066224818 @default.
• W3096347622 cites W2074477564 @default.
• W3096347622 cites W2084001690 @default.
• W3096347622 cites W2118706537 @default.
• W3096347622 cites W2133565549 @default.
• W3096347622 cites W2139287577 @default.
• W3096347622 cites W2166547175 @default.
• W3096347622 cites W2397306716 @default.
• W3096347622 cites W2476751262 @default.
• W3096347622 cites W2477329675 @default.
• W3096347622 cites W2510434276 @default.
• W3096347622 cites W2554408731 @default.
• W3096347622 cites W2555077524 @default.
• W3096347622 cites W2619033034 @default.
• W3096347622 cites W2747894684 @default.
• W3096347622 cites W2779129326 @default.
• W3096347622 cites W2799211965 @default.
• W3096347622 cites W2905706796 @default.
• W3096347622 cites W2948012784 @default.
• W3096347622 cites W3103858256 @default.
• W3096347622 cites W3104901183 @default.
• W3096347622 cites W789250018 @default.
• W3096347622 doi "https://doi.org/10.1016/j.neunet.2020.10.015" @default.
• W3096347622 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33220642" @default.
• W3096347622 hasPublicationYear "2021" @default.
• W3096347622 type Work @default.
• W3096347622 sameAs 3096347622 @default.
• W3096347622 citedByCount "14" @default.
• W3096347622 countsByYear W30963476222021 @default.
• W3096347622 countsByYear W30963476222022 @default.
• W3096347622 countsByYear W30963476222023 @default.
• W3096347622 crossrefType "journal-article" @default.
• W3096347622 hasAuthorship W3096347622A5000458161 @default.
• W3096347622 hasAuthorship W3096347622A5020818383 @default.
• W3096347622 hasAuthorship W3096347622A5041645613 @default.
• W3096347622 hasAuthorship W3096347622A5045588004 @default.
• W3096347622 hasAuthorship W3096347622A5076609933 @default.
• W3096347622 hasAuthorship W3096347622A5089803281 @default.
• W3096347622 hasConcept C119857082 @default.
• W3096347622 hasConcept C143724316 @default.
• W3096347622 hasConcept C147168706 @default.
• W3096347622 hasConcept C151406439 @default.
• W3096347622 hasConcept C151730666 @default.
• W3096347622 hasConcept C153180895 @default.
• W3096347622 hasConcept C154945302 @default.
• W3096347622 hasConcept C172025690 @default.
• W3096347622 hasConcept C188441871 @default.
• W3096347622 hasConcept C23224414 @default.
• W3096347622 hasConcept C2779426996 @default.
• W3096347622 hasConcept C31258907 @default.
• W3096347622 hasConcept C41008148 @default.
• W3096347622 hasConcept C50644808 @default.
• W3096347622 hasConcept C70437156 @default.
• W3096347622 hasConcept C81363708 @default.
• W3096347622 hasConcept C86803240 @default.
• W3096347622 hasConcept C97931131 @default.
• W3096347622 hasConceptScore W3096347622C119857082 @default.
• W3096347622 hasConceptScore W3096347622C143724316 @default.
• W3096347622 hasConceptScore W3096347622C147168706 @default.
• W3096347622 hasConceptScore W3096347622C151406439 @default.
• W3096347622 hasConceptScore W3096347622C151730666 @default.
• W3096347622 hasConceptScore W3096347622C153180895 @default.
• W3096347622 hasConceptScore W3096347622C154945302 @default.
• W3096347622 hasConceptScore W3096347622C172025690 @default.
• W3096347622 hasConceptScore W3096347622C188441871 @default.
• W3096347622 hasConceptScore W3096347622C23224414 @default.
• W3096347622 hasConceptScore W3096347622C2779426996 @default.
• W3096347622 hasConceptScore W3096347622C31258907 @default.
• W3096347622 hasConceptScore W3096347622C41008148 @default.
• W3096347622 hasConceptScore W3096347622C50644808 @default.
• W3096347622 hasConceptScore W3096347622C70437156 @default.
• W3096347622 hasConceptScore W3096347622C81363708 @default.

• next