Comparer des méthodes

	Transformeur (traitement du langage naturel)	XGBoost
Domaine	Apprentissage profond	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2017	2016
Auteur d'origine	Vaswani, A. et al.	Chen, T. & Guestrin, C.
Type	Attention-based deep neural network	Ensemble (gradient-boosted decision trees)
Source fondatrice	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Alias	Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP	XGBoost, extreme gradient boosting, scalable tree boosting
Apparentées	4	5
Résumé	The Transformer is an attention-based deep learning model, introduced by Vaswani and colleagues in 2017, that performs text classification, named-entity recognition, and language modelling by letting every token in a sequence attend directly to every other token. It replaced earlier recurrent designs with a self-attention mechanism that processes whole sequences in parallel.	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.