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Exploring the Privacy-Preserving Properties of Word Embeddings: Algorithmic Validation Study
BACKGROUND: Word embeddings are dense numeric vectors used to represent language in neural networks. Until recently, there had been no publicly released embeddings trained on clinical data. Our work is the first to study the privacy implications of releasing these models. OBJECTIVE: This paper aims...
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| Gepubliceerd in: | J Med Internet Res |
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| Hoofdauteurs: | , , , |
| Formaat: | Artigo |
| Taal: | Inglês |
| Gepubliceerd in: |
JMIR Publications
2020
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| Onderwerpen: | |
| Online toegang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7391163/ https://ncbi.nlm.nih.gov/pubmed/32673230 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.2196/18055 |
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