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Efficient Low-Rank and Sparse Attention Approximation (ELSAA) methodology for training Transformers with longer contexts while preserving both sharp token-level interactions and broad contextual mixing.
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| name | elsaa-efficient-low-rank-sparse-attention |
| version | 1.0.0 |
| description | Efficient Low-Rank and Sparse Attention Approximation (ELSAA) methodology for training Transformers with longer contexts while preserving both sharp token-level interactions and broad contextual mixing. |
| author | Mahdi Heidari, Mohammad Mahdi Rahimi, Jaekyun Moon |
| license | MIT |
| arxiv_id | 2607.20214v1 |
| tags | ["transformer","attention","efficiency","long-context","low-rank","sparse"] |
ELSAA (Efficient Low-Rank and Sparse Attention Approximation) addresses the quadratic $N\times N$ attention score matrix bottleneck in Transformers by approximating the attention score operator itself rather than decomposing learned projection matrices.
ELSAA, efficient attention, low-rank attention, sparse attention, long-context transformers, quadratic bottleneck
