Сравнение на методи
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| Слабо контролиран LSTM× | Рекурентна невронна мрежа× | |
|---|---|---|
| Област | Дълбоко обучение | Дълбоко обучение |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 2016–2018 | 1986–1990 |
| Създател≠ | Ratner et al. (data programming framework); Hochreiter & Schmidhuber (LSTM backbone) | Rumelhart, D. E.; Elman, J. L. |
| Тип≠ | Weakly supervised sequence model | Sequential neural network |
| Основополагащ източник≠ | 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 ↗ |
| Други названия | WS-LSTM, noisy-label LSTM, distant-supervision LSTM, data-programming LSTM | RNN, Elman network, Jordan network, simple recurrent network |
| Свързани≠ | 6 | 3 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
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