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| 약한 지도 순환 신경망× | Gated Recurrent Unit (GRU)× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2009–2016 | 2014 |
| 창시자≠ | Broadly attributed to the weak supervision / distant supervision research community (Mintz et al., 2009; Ratner et al., 2016) | Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. |
| 유형≠ | Supervised learning under noisy or incomplete labels | Recurrent neural network with gating |
| 원전≠ | 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 ↗ | Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In Proceedings of EMNLP 2014, pp. 1724–1734. link ↗ |
| 별칭 | WS-RNN, distantly supervised RNN, noise-tolerant RNN, weakly supervised sequence model | GRU, GRU network, gated RNN, GRU cell |
| 관련≠ | 5 | 3 |
| 요약≠ | A weakly supervised RNN trains a recurrent neural network on sequences whose labels come from imperfect sources — heuristic rules, distant supervision, crowdsourcing, or generative label models — rather than expensive expert annotation. This lets researchers exploit large unlabeled corpora for sequential tasks such as text classification, named entity recognition, or time-series prediction when fully annotated data is scarce or costly. | The Gated Recurrent Unit (GRU), introduced by Cho et al. in 2014, is a streamlined recurrent neural network that uses two learned gates — an update gate and a reset gate — to selectively retain or discard information across time steps, enabling effective sequence modelling with fewer parameters than LSTM. |
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