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| Schwache rekurrente neuronale Netze× | Long Short-Term Memory (LSTM)× | |
|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2009–2016 | 1997 |
| Urheber≠ | Broadly attributed to the weak supervision / distant supervision research community (Mintz et al., 2009; Ratner et al., 2016) | Hochreiter, S. & Schmidhuber, J. |
| Typ≠ | Supervised learning under noisy or incomplete labels | Recurrent neural network with gated memory cells |
| Wegweisende Quelle≠ | 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 ↗ | Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. DOI ↗ |
| Aliasnamen | WS-RNN, distantly supervised RNN, noise-tolerant RNN, weakly supervised sequence model | LSTM, LSTM network, LSTM-RNN, long short-term memory RNN |
| Verwandt≠ | 5 | 4 |
| Zusammenfassung≠ | 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. | Long Short-Term Memory (LSTM) is a gated recurrent neural network architecture introduced by Hochreiter and Schmidhuber in 1997. It was designed to learn dependencies across long sequences by using dedicated memory cells and three learned gates — forget, input, and output — that control what information is retained, updated, or passed forward at each time step. |
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