5 citations · 5 across the 5 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
MirrorLA: Reflecting Feature Map for Vision Linear Attention
Weikang Meng, Liangyu Huo, Yadan Luo +3
Linear attention significantly reduces the computational complexity of Transformers from quadratic to linear, yet it consistently lags behind softmax-based attention in performance…
cs.LG2026
STILL: Selecting Tokens for Intra-Layer Hybrid Attention to Linearize LLMs
Weikang Meng, Liangyu Huo, Yadan Luo +4
Linearizing pretrained large language models (LLMs) primarily relies on intra-layer hybrid attention mechanisms to alleviate the quadratic complexity of standard softmax attention.…
cs.LG2025
NormDirection: Restoring the Missing Query Norm in Vision Linear Attention
Weikang Meng, Yadan Luo, Liangyu Huo +4
Linear attention mitigates the quadratic complexity of softmax attention but suffers from a critical loss of expressiveness. We identify two primary causes: (1) The normalization o…