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Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\"om Method

NeurIPS 2021 · Architectures and efficiency › Attention and transformers · approach: Attention head and block redesign

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace softmax with Gaussian kernel (ablation)Self-Attention · LRA averageaccuracy (%): 57.37 -> 58.56Helped
replace softmax with Gaussian kernel (ablation)Self-Attention · LRA Pathfinderaccuracy (%): 65.26 -> 70.73Helped
replace softmax with Gaussian kernel (ablation)Self-Attention · LRA Textaccuracy (%): 61.95 -> 60.22Hurt
replace softmax with Gaussian kernel (ablation)Self-Attention · LRA running time on Textrunning time (h): 4.30 -> 3.91No clear effect
add Nystrom approximation to Kernelized Attention (Skyformer) (ablation)Kernelized Attention · LRA Textrunning time (h): 3.91 -> 1.02Helped
add Nystrom approximation to Kernelized Attention (Skyformer) (ablation)Kernelized Attention · LRA averageaccuracy (%): 58.56 -> 59.39Helped
add Nystrom approximation to Kernelized Attention (Skyformer) (ablation)Kernelized Attention · LRA Imageaccuracy (%): 41.29 -> 40.77Hurt
use Skyformer instead of self-attentionSelf-Attention · LRA Textaccuracy (%): 61.95 -> 64.70Helped
use Skyformer instead of self-attentionSelf-Attention · LRA Retrievalaccuracy (%): 80.69 -> 82.06Helped
use Skyformer instead of self-attentionSelf-Attention · LRA Textpeak memory (GB): 10.37 -> 1.59Helped
directly apply Nystrom to softmax self-attention (ablation)Self-Attention · LRA trainingtraining stabilityHurt
use Skyformer instead of NystromformerNystromformer · LRA averageaccuracy (%): 58.93 -> 59.39Helped

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