Regularization
Attention Dropout
Attention Dropout is a type of dropout used in attention-based architectures, where elements are randomly dropped out of the softmax in the attention equation. For example, for scaled-dot product attention, we would drop elements from the first term:
$$ {\text{Attention}}(Q, K, V) = \text{softmax}\left(\frac{QK^{T}}{\sqrt{d_k}}\right)V $$
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Language Modelling | 77 | 9.53% |
| Retrieval | 77 | 9.53% |
| Question Answering | 42 | 5.20% |
| Large Language Model | 39 | 4.83% |
| Sentence | 29 | 3.59% |
| In-Context Learning | 21 | 2.60% |
| Text Generation | 18 | 2.23% |
| Information Retrieval | 18 | 2.23% |
| Code Generation | 14 | 1.73% |
Dropout
