विधियों की तुलना करें

	कमजोर पर्यवेक्षित आवर्ती तंत्रिका नेटवर्क	कमजोर पर्यवेक्षित एलएसटीएम
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2009–2016	2016–2018
प्रवर्तक	Broadly attributed to the weak supervision / distant supervision research community (Mintz et al., 2009; Ratner et al., 2016)	Ratner et al. (data programming framework); Hochreiter & Schmidhuber (LSTM backbone)
प्रकार	Supervised learning under noisy or incomplete labels	Weakly supervised sequence model
मौलिक स्रोत	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.	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.
उपनाम	WS-RNN, distantly supervised RNN, noise-tolerant RNN, weakly supervised sequence model	WS-LSTM, noisy-label LSTM, distant-supervision LSTM, data-programming LSTM
संबंधित	5	6
सारांश	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.	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.