Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Zwakke gesuperviseerde LSTM× | Recurrent Neuraal Netwerk× | |
|---|---|---|
| Vakgebied | Deep learning | Deep learning |
| Familie | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 2016–2018 | 1986–1990 |
| Grondlegger≠ | Ratner et al. (data programming framework); Hochreiter & Schmidhuber (LSTM backbone) | Rumelhart, D. E.; Elman, J. L. |
| Type≠ | Weakly supervised sequence model | Sequential neural network |
| Oorspronkelijke bron≠ | 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 ↗ |
| Aliassen | WS-LSTM, noisy-label LSTM, distant-supervision LSTM, data-programming LSTM | RNN, Elman network, Jordan network, simple recurrent network |
| Verwant≠ | 6 | 3 |
| Samenvatting≠ | 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. |
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