Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| RNNi iliyofunzwa kwa udhaifu× | Mtandao wa Nyuro Unaojirudia× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Kina | Ujifunzaji wa Kina |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2009–2016 | 1986–1990 |
| Mwanzilishi≠ | Broadly attributed to the weak supervision / distant supervision research community (Mintz et al., 2009; Ratner et al., 2016) | Rumelhart, D. E.; Elman, J. L. |
| Aina≠ | Supervised learning under noisy or incomplete labels | Sequential neural network |
| Chanzo asilia≠ | 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 ↗ |
| Majina mbadala | WS-RNN, distantly supervised RNN, noise-tolerant RNN, weakly supervised sequence model | RNN, Elman network, Jordan network, simple recurrent network |
| Zinazohusiana≠ | 5 | 3 |
| Muhtasari≠ | 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. | 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. |
| ScholarGateSeti ya data ↗ |
|
|