方法对比
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| 神经架构搜索× | 长格式Transformer / BigBird× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2017 | 2020 |
| 提出者≠ | Zoph, B. & Le, Q.V. | Beltagy, Peters & Cohan (Longformer); Zaheer et al. (BigBird) |
| 类型≠ | Automated architecture optimization (deep learning) | Sparse-attention Transformer for long sequences |
| 开创性文献≠ | Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR. link ↗ | Beltagy, I., Peters, M. E. & Cohan, A. (2020). Longformer: The Long-Document Transformer. arXiv. link ↗ |
| 别名 | Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search | Uzun Dizi Transformer (Longformer / BigBird), uzun dizi transformer, long-document transformer, sparse-attention transformer |
| 相关≠ | 5 | 4 |
| 摘要≠ | Neural Architecture Search (NAS), introduced by Zoph and Le in 2017, automatically optimizes architectural decisions such as a network's depth, width, and connection structure instead of hand-designing them. Leading methods in the field include DARTS, ENAS, and Once-for-All. | 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. |
| ScholarGate数据集 ↗ |
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