Uporedite metode

	Model sekvenca-na-sekvencu	Mehanizam pažnje	BERT fine-tuning	Slučajna šuma
Oblast	Duboko učenje	Duboko učenje	Duboko učenje	Mašinsko učenje
Porodica	Machine learning	Machine learning	Machine learning	Machine learning
Godina nastanka	2014	2015	2019	2001
Tvorac	Sutskever, I.; Cho, K.	Bahdanau, D.; Luong, M.T.	Devlin, J. et al.	Breiman, L.
Tip	Encoder-decoder neural network (deep learning)	Neural attention layer (encoder-decoder)	Transfer learning (fine-tuning a pre-trained transformer)	Ensemble (bagging of decision trees)
Temeljni izvor	Sutskever, I., Vinyals, O. & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. NeurIPS.	Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR.	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Drugi nazivi	Dizi-Dizi Modeli (Seq2Seq — Encoder-Decoder), encoder-decoder model, seq2seq, sequence to sequence learning	Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention	BERT İnce Ayar (Fine-Tuning), BERT ince ayar, fine-tuning BERT, transfer learning with BERT	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Srodne	5	5	5	4
Sažetak	The sequence-to-sequence (Seq2Seq) model, introduced by Sutskever, Vinyals and Le and by Cho and colleagues in 2014, is an encoder-decoder neural network that maps a variable-length input sequence to a variable-length output sequence. It is the foundation of machine translation, text summarization, dialogue systems and code generation.	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.	BERT fine-tuning, building on the BERT model introduced by Devlin and colleagues in 2019, re-trains a pre-trained BERT model on a small labelled dataset for a target task such as classification, named-entity recognition, or question answering. Through transfer learning it reaches high performance even with relatively little task-specific data.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.