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This paper proposes modifications to the transformer architecture that improve its performance on long-range tasks, narrowing the gap with specialized layers. The modifications incorporate an inductive bias towards smoothness and locality, improving results without additional computation or parameters.
https://arxiv.org/abs//2311.16620
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
By Igor Melnyk5
33 ratings
This paper proposes modifications to the transformer architecture that improve its performance on long-range tasks, narrowing the gap with specialized layers. The modifications incorporate an inductive bias towards smoothness and locality, improving results without additional computation or parameters.
https://arxiv.org/abs//2311.16620
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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