Papers by Patricia Thaine
Privacy-Preserving Natural Language Processing (2023.eacl-tutorials)
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| Challenge: | This tutorial will help the NLP community to get familiar with current research in privacy-preserving methods. |
| Approach: | This tutorial will help the NLP community to get familiar with current research in privacy-preserving methods. |
| Outcome: | The tutorial will cover membership inference, differential privacy, homomorphic encryption, or federated learning, all with typical use-cases and potential pitfalls. |
The Chinese Remainder Theorem for Compact, Task-Precise, Efficient and Secure Word Embeddings (2021.eacl-main)
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| Challenge: | a new method for compressing word vector embeddings into integers is being developed . a high precision approach to compressing words into integer results in negligible performance gains . |
| Approach: | They propose a method for compressing word vector embeddings into integers using the Chinese Reminder Theorem. |
| Outcome: | The proposed method speeds up addition by 48.27% and compresses GloVe word embedding libraries by 25.86%. |