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FilterNet: A Many-to-Many Deep Learning Architecture for Time Series Classification
In this paper, we present and benchmark FilterNet, a flexible deep learning architecture for time series classification tasks, such as activity recognition via multichannel sensor data. It adapts popular convolutional neural network (CNN) and long short-term memory (LSTM) motifs which have excelled...
Tallennettuna:
| Julkaisussa: | Sensors (Basel) |
|---|---|
| Päätekijät: | , |
| Aineistotyyppi: | Artigo |
| Kieli: | Inglês |
| Julkaistu: |
MDPI
2020
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| Aiheet: | |
| Linkit: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7249062/ https://ncbi.nlm.nih.gov/pubmed/32354082 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/s20092498 |
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