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Theory, Analysis, and Best Practices for Sigmoid Self-Attention
This paper analyzes sigmoid attention in transformers, proving its universality and improved regularity, while introducing FLASHSIGMOID for efficient implementation, matching softmax performance across various domains.
https://arxiv.org/abs//2409.04431
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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By Igor Melnyk
5
33 ratings
This paper analyzes sigmoid attention in transformers, proving its universality and improved regularity, while introducing FLASHSIGMOID for efficient implementation, matching softmax performance across various domains.
https://arxiv.org/abs//2409.04431
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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