Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Слабо контролируемый GRU× | Сверточный рекуррентный блок (GRU)× | |
|---|---|---|
| Область | Глубокое обучение | Глубокое обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2014–2016 | 2014 |
| Автор метода≠ | Chung et al. (GRU); Ratner et al. (weak supervision framework) | Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. |
| Тип≠ | Weakly supervised sequence model | Recurrent neural network with gating |
| Основополагающий источник≠ | 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 ↗ | 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 ↗ |
| Другие названия | WS-GRU, GRU with weak supervision, weakly labeled GRU, noisy-label GRU | GRU, GRU network, gated RNN, GRU cell |
| Связанные≠ | 6 | 3 |
| Сводка≠ | 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. | 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. |
| ScholarGateНабор данных ↗ |
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