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Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics
Recurrent neural networks (RNNs) are a widely used tool for modeling sequential data, yet they are often treated as inscrutable black boxes. Given a trained recurrent network, we would like to reverse engineer it–to obtain a quantitative, interpretable description of how it solves a particular task....
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| Publicat a: | Adv Neural Inf Process Syst |
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| Autors principals: | , , , , |
| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
2019
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| Matèries: | |
| Accés en línia: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7416638/ https://ncbi.nlm.nih.gov/pubmed/32782423 |
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