Compara mètodes

	Mecanisme d'atenció	XGBoost
Camp	Aprenentatge profund	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	2015	2016
Autor original	Bahdanau, D.; Luong, M.T.	Chen, T. & Guestrin, C.
Tipus	Neural attention layer (encoder-decoder)	Ensemble (gradient-boosted decision trees)
Font seminal	Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Àlies	Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention	XGBoost, extreme gradient boosting, scalable tree boosting
Relacionats	5	5
Resum	The attention mechanism, introduced by Bahdanau, Cho and Bengio in 2015 and refined by Luong, Pham and Manning the same year, lets a sequence decoder dynamically learn which of the encoder's outputs to focus on at each step. Before the Transformer, it substantially improved machine-translation quality by freeing models from compressing an entire input into a single fixed vector.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.