Attention is all you need.¶
Vaswani, A., Shazeer, Parmar, Uszkoreit, Jones, Gomez, & Kaiser. (2017). Attention is all you need. Advances in Neural Information Processing Systems 30 (NeurIPS 2017).
Cited by¶
3 citations across 3 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Attention
- Machine learning & AI (Vaswani et al. 2017, transformer architectures): attention mechanisms in sequence-to-sequence models, self-attention, multi-head attention, cross-attention, scaled dot-product attention as differentiable allocation.
This sourceIntroduces the Transformer with multi-head attention as the sole sequence-mixing mechanism.
- Machine learning & AI (Vaswani et al. 2017, transformer architectures): attention mechanisms in sequence-to-sequence models, self-attention, multi-head attention, cross-attention, scaled dot-product attention as differentiable allocation.
- Attentional Capacity
- The intervention family transfers from the human-factors literature with structural fidelity: filter upstream (RAG), automate per-task draw (caching), route across modality-analogs (mixture-of-experts), design the inference duty cycle to manage exhaustion (context windowing).
This sourceIntroduces the Transformer with multi-head attention as the sole sequence-mixing mechanism; attention heads constitute a bounded per-layer per-token selection budget
- The intervention family transfers from the human-factors literature with structural fidelity: filter upstream (RAG), automate per-task draw (caching), route across modality-analogs (mixture-of-experts), design the inference duty cycle to manage exhaustion (context windowing).
- Paradigmatic vs. Syntagmatic Relations
- The transformer architecture (Vaswani et al. 2017
This sourceIntroduces the Transformer architecture with multi-head attention as the sole sequence-mixing mechanism; attention heads constitute a bounded per-layer per-token selection budget — a non-biological instance of the attentional-capacity pattern.
- The transformer architecture (Vaswani et al. 2017
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