[KongchangAI]
ConceptSliding Window Attention / 滑动窗口注意力

SWA

A sparse attention variant in which each token attends only to neighboring tokens within a fixed preceding window. It reduces the computational complexity of attention from quadratic to approximately linear, supporting very long contexts and lowering inference cost.

Timeline (last 90 days)

Oct 13

The SWA (Sliding Window Attention) bounded replay feature introduced in DeepSeek models has been fully implemented by vLLM, and official data shows TTFT drops by about 30%.

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Oct 13

SWA (Sliding Window Attention) is a sparse attention variant that lets each token attend only to its neighbors within a fixed preceding window, reducing complexity to approximately linear.

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Oct 12

The five design dimensions of SADL include: a baseline loss function combining Dice and top-K losses, strong data augmentation, reweighting by lesion volume, Stochastic Weight Averaging (SWA), and the pairing of cohort-balanced sampling with a residual encoder backbone.

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Oct 12

SWA (Stochastic Weight Averaging) averages multiple checkpoints sampled at a high learning rate during the late stage of training, guiding the model to converge to a flatter loss basin and thereby improving generalization.

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