Low-Rank Factorization-based Multi-head Attention Mechanism, or LAMA, is a type of attention module that uses low-rank factorization to reduce computational complexity. It uses low-rank bilinear pooling to construct a structured sentence representation that attends to multiple aspects of a sentence.
Source: Low Rank Factorization for Compact Multi-Head Self-AttentionPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
---|---|---|
Language Modelling | 11 | 20.75% |
Image Inpainting | 4 | 7.55% |
In-Context Learning | 3 | 5.66% |
Question Answering | 3 | 5.66% |
Text Classification | 3 | 5.66% |
Meta-Learning | 2 | 3.77% |
Retrieval | 2 | 3.77% |
Sentiment Analysis | 2 | 3.77% |
Quantization | 1 | 1.89% |