Σύγκριση μεθόδων

	Ασθενώς Επιβλεπόμενο GRU	Επαναλαμβανόμενη Μονάδα με Πύλες (GRU)
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2014–2016	2014
Δημιουργός	Chung et al. (GRU); Ratner et al. (weak supervision framework)	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y.
Τύπος	Weakly supervised sequence model	Recurrent neural network with gating
Θεμελιώδης πηγή	Ratner, A. J., De Sa, C. M., Wu, S., Selsam, D., & Re, C. (2016). Data Programming: Creating Large Training Sets, Quickly. Advances in Neural Information Processing Systems (NeurIPS), 29.	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In Proceedings of EMNLP 2014, pp. 1724–1734.
Εναλλακτικές ονομασίες	WS-GRU, GRU with weak supervision, weakly labeled GRU, noisy-label GRU	GRU, GRU network, gated RNN, GRU cell
Συναφείς	6	3
Σύνοψη	Weakly Supervised GRU trains a Gated Recurrent Unit network on sequences labeled by imperfect, heuristic, or programmatic sources rather than costly hand-annotated ground truth. It combines the GRU's efficiency at capturing temporal dependencies with weak-supervision techniques that aggregate noisy labels, enabling practical sequence modeling when large fully labeled datasets are unavailable.	The Gated Recurrent Unit (GRU), introduced by Cho et al. in 2014, is a streamlined recurrent neural network that uses two learned gates — an update gate and a reset gate — to selectively retain or discard information across time steps, enabling effective sequence modelling with fewer parameters than LSTM.