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:
$$ {\text{Attention}}(Q, K, V) = \text{softmax}\left(\frac{QK^{T}}{\sqrt{d_k}}\right)V $$
Paper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Language Modelling | 85 | 10.75% |
Retrieval | 64 | 8.09% |
Question Answering | 50 | 6.32% |
Large Language Model | 42 | 5.31% |
Sentence | 31 | 3.92% |
Text Generation | 23 | 2.91% |
In-Context Learning | 20 | 2.53% |
Information Retrieval | 16 | 2.02% |
Text Classification | 15 | 1.90% |