方法对比

	长格式Transformer / BigBird	随机森林
领域	深度学习	机器学习
方法族	Machine learning	Machine learning
起源年份	2020	2001
提出者	Beltagy, Peters & Cohan (Longformer); Zaheer et al. (BigBird)	Breiman, L.
类型	Sparse-attention Transformer for long sequences	Ensemble (bagging of decision trees)
开创性文献	Beltagy, I., Peters, M. E. & Cohan, A. (2020). Longformer: The Long-Document Transformer. arXiv.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
别名	Uzun Dizi Transformer (Longformer / BigBird), uzun dizi transformer, long-document transformer, sparse-attention transformer	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
相关	4	4
摘要	Long-sequence Transformers such as Longformer (Beltagy, Peters & Cohan, 2020) and BigBird (Zaheer et al., 2020) replace the standard Transformer's O(n²) attention with sparse attention patterns that scale linearly, O(n), with sequence length. This lets a single model attend over thousands of tokens — full documents, legal texts, or genomic sequences — that would not fit a conventional Transformer.	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.