LSTMVis : A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks
Recurrent neural networks, and in particular long short-term memory (LSTM) networks, are a remarkably effective tool for sequence modeling that learn a dense black-box hidden representation of their sequential input. Researchers interested in better understanding these models have studied the change...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on visualization and computer graphics. - 1996. - 24(2018), 1 vom: 30. Jan., Seite 667-676
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| 1. Verfasser: |
Strobelt, Hendrik
(VerfasserIn) |
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| Weitere Verfasser: |
Gehrmann, Sebastian,
Pfister, Hanspeter,
Rush, Alexander M |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2018
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on visualization and computer graphics
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| Schlagworte: | Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S. |
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