方法对比
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| 注意力机制× | 多头自注意力机制× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2015 | 2017 |
| 提出者≠ | Bahdanau, D.; Luong, M.T. | Vaswani, A. et al. |
| 类型≠ | Neural attention layer (encoder-decoder) | Attention mechanism (Transformer core) |
| 开创性文献≠ | Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR. link ↗ | Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS. link ↗ |
| 别名≠ | Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention | Öz-Dikkat ve Çok Başlı Dikkat (Multi-Head Self-Attention), öz-dikkat, multi-head attention, scaled dot-product attention |
| 相关 | 5 | 5 |
| 摘要≠ | 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. | Multi-head self-attention, introduced by Vaswani and colleagues in 2017, is the mechanism that lets every position in a sequence compute its relationship to all other positions in parallel. It is the core of the Transformer architecture and the foundation underneath BERT, GPT, and T5. |
| ScholarGate数据集 ↗ |
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