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Machine learningDeep learning / NLP / CV

Fine-Tuned Recurrent Neural Network

A Fine-Tuned Recurrent Neural Network (RNN) starts from a model pre-trained on large corpora or time-series data and adapts its weights to a specific downstream task through controlled gradient updates. The approach dramatically cuts the labeled data needed for strong sequence modeling performance in text classification, named entity recognition, sentiment analysis, and related tasks.

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Sources

  1. Howard, J. & Ruder, S. (2018). Universal Language Model Fine-Tuning for Text Classification. Proceedings of ACL 2018, 328–339. DOI: 10.18653/v1/P18-1031 ↗
  2. Recurrent neural network. Wikipedia. link ↗

Related methods

Fine-Tuned LSTMFine-Tuned TransformerGated Recurrent UnitLong Short-Term MemoryRecurrent Neural NetworkTransfer Learning with Recurrent Neural Network

Referenced by

Domain-adaptive Recurrent Neural NetworkFine-Tuned Convolutional Neural NetworkFine-Tuned LSTMFine-Tuned TransformerTransfer Learning with Recurrent Neural Network

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ScholarGate — Fine-Tuned Recurrent Neural Network (Fine-Tuned Recurrent Neural Network (Transfer Learning for Sequence Models)). Retrieved 2026-06-04 from https://scholargate.app/tr/deep-learning/fine-tuned-recurrent-neural-network