Cold-Start and Interpretability: Turning Regular Expressions into Trainable Recurrent Neural Networks

Research article (Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020) · cited 23× · AI/ML
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Cold-Start and Interpretability: Turning Regular Expressions into Trainable Recurrent Neural Networks

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Cold-Start and Interpretability: Turning Regular Expressions into Trainable Recurrent Neural Networks is a scholarly article[1].

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APA 4ort.xyz Knowledge Graph. (2026). Cold-Start and Interpretability: Turning Regular Expressions into Trainable Recurrent Neural Networks. Retrieved May 24, 2026, from https://4ort.xyz/entity/cold-start-and-interpretability-turning-regular-expressions-into-trainable-recurrent-neural-networks
MLA “Cold-Start and Interpretability: Turning Regular Expressions into Trainable Recurrent Neural Networks.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/cold-start-and-interpretability-turning-regular-expressions-into-trainable-recurrent-neural-networks.
BibTeX @misc{4ortxyz_cold-start-and-interpretability-turning-regular-expressions-into-trainable-recurrent-neural-networks_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Cold-Start and Interpretability: Turning Regular Expressions into Trainable Recurrent Neural Networks}}, year = {2026}, url = {https://4ort.xyz/entity/cold-start-and-interpretability-turning-regular-expressions-into-trainable-recurrent-neural-networks}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Cold-Start and Interpretability: Turning Regular Expressions into Trainable Recurrent Neural Networks — https://4ort.xyz/entity/cold-start-and-interpretability-turning-regular-expressions-into-trainable-recurrent-neural-networks (retrieved 2026-05-24)

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