Softmax Linear Attention aims for efficient global competition
Softmax Linear Attention proposes applying softmax at the head level to restore global competition in linear attention while retaining the goal of linear complexity. The approach can be compared with standard softmax normalization.
The paper presents Softmax Linear Attention (SLA), which applies softmax at the head level to restore global competition in linear attention. The proposal aims to retain linear complexity while improving the selection of relevant information.
For engineers evaluating attention variants, SLA offers an approach to compare with standard softmax normalization. If you use AI to study or apply the material, avoid sending internal or personal data without authorization and follow your organization’s data-protection policy.