Geometry Meets Attention: Interpretable Transformers via SVD Inspiration
Self-attention is a cornerstone of modern deep learning, yet its dense dot-product formulation offers limited interpretability and lacks explicit structural constraints. We propose SVD-inspired Attention (SVDA), a novel self-attention mechanism that introduces normalized query/key projections and a...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11072340/ |
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