Linear Attention
Linear Attention is a class of attention mechanisms designed to approximate the standard self-attention operation in linear time complexity relative to sequence length, bypassing the quadratic memory scaling limits of standard Transformers.
Frequently Asked Questions
Why does standard self-attention scale quadratically?▼
Because standard attention calculates similarity scores between every token and every other token in the sequence (an N x N matrix).
What are examples of models utilizing linear complexity layouts?▼
State Space Models (like Mamba) or Linear Transformer variants that rewrite the attention matrix multiplication order.
Quick Facts
- CategoryNeural Architectures
- Key ApplicationInfinite context length LLMs, long-sequence DNA analysis, and fast state-space models.
Coverage Trend12 Weeks
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