Attention
Multi-Query Attention
Introduced by Shazeer in Fast Transformer Decoding: One Write-Head is All You NeedMulti-head attention consists of multiple attention layers (heads) in parallel with different linear transformations on the queries, keys, values and outputs. Multi-query attention is identical except that the different heads share a single set of keys and values.
Source: Fast Transformer Decoding: One Write-Head is All You Need
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Language Modelling | 6 | 18.18% |
| Answer Generation | 1 | 3.03% |
| Document Classification | 1 | 3.03% |
| Image Classification | 1 | 3.03% |
| Auto Debugging | 1 | 3.03% |
| Code Generation | 1 | 3.03% |
| Common Sense Reasoning | 1 | 3.03% |
| Coreference Resolution | 1 | 3.03% |
| Cross-Lingual Question Answering | 1 | 3.03% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
