ReLU Approximation Recipe for Softmax Transformers
A new paper on arXiv provides a systematic recipe for translating ReLU approximation results to the softmax attention mechanism, covering common approximation targets such as multiplication, reciprocal computation, and min/max primitives. The method yields target-specific, economic resource bounds beyond universal approximation statements, offering new analytical tools for analyzing softmax transformer models. The paper is categorized under Computer Science > Machine Learning and was submitted on April 25, 2026.
Key facts
- The paper provides a systematic recipe for translating ReLU approximation results to softmax attention.
- It covers common approximation targets including multiplication, reciprocal computation, and min/max primitives.
- The method yields target-specific, economic resource bounds beyond universal approximation statements.
- The results offer new analytical tools for analyzing softmax transformer models.
- The paper is categorized under Computer Science > Machine Learning.
- The submission date is April 25, 2026.
- The paper is available on arXiv with ID 2604.24878.
- The abstract describes the recipe as systematic and covering many common approximation targets.
Entities
Institutions
- arXiv