方法对比
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| 弱监督 LSTM× | 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≠ | 2016–2018 | 1986–1990 |
| 提出者≠ | Ratner et al. (data programming framework); Hochreiter & Schmidhuber (LSTM backbone) | Rumelhart, D. E.; Elman, J. L. |
| 类型≠ | Weakly supervised sequence model | Sequential neural network |
| 开创性文献≠ | Ratner, A., De Sa, C., 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-LSTM, noisy-label LSTM, distant-supervision LSTM, data-programming LSTM | RNN, Elman network, Jordan network, simple recurrent network |
| 相关≠ | 6 | 3 |
| 摘要≠ | Weakly supervised LSTM trains a Long Short-Term Memory network on sequence data where clean, manually annotated labels are scarce or absent. Instead, multiple imperfect label sources — heuristic rules, distant supervision, crowdsourcing, or programmatic labeling functions — are combined to produce probabilistic training labels, which are then used to supervise the LSTM. This allows scalable training on large unlabeled corpora without exhaustive human annotation. | 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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