方法对比
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| 弱监督门控循环单元 (Weakly Supervised GRU)× | 循环神经网络× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2014–2016 | 1986–1990 |
| 提出者≠ | Chung et al. (GRU); Ratner et al. (weak supervision framework) | Rumelhart, D. E.; Elman, J. L. |
| 类型≠ | Weakly supervised sequence model | Sequential neural network |
| 开创性文献≠ | Ratner, A. J., De Sa, C. M., Wu, S., Selsam, D., & Re, C. (2016). Data Programming: Creating Large Training Sets, Quickly. Advances in Neural Information Processing Systems (NeurIPS), 29. link ↗ | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ |
| 别名 | WS-GRU, GRU with weak supervision, weakly labeled GRU, noisy-label GRU | RNN, Elman network, Jordan network, simple recurrent network |
| 相关≠ | 6 | 3 |
| 摘要≠ | Weakly Supervised GRU trains a Gated Recurrent Unit network on sequences labeled by imperfect, heuristic, or programmatic sources rather than costly hand-annotated ground truth. It combines the GRU's efficiency at capturing temporal dependencies with weak-supervision techniques that aggregate noisy labels, enabling practical sequence modeling when large fully labeled datasets are unavailable. | A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models. |
| ScholarGate数据集 ↗ |
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