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In-Context Learning Functions with Varying Number of Minima
This paper explores the interplay between In-Context Learning (ICL) and the properties of functions it approximates. The study finds that increasing the number of minima in the functions degrades ICL performance, but ICL still outperforms a 2-layer Neural Network model and learns faster.
https://arxiv.org/abs//2311.12538
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 explores the interplay between In-Context Learning (ICL) and the properties of functions it approximates. The study finds that increasing the number of minima in the functions degrades ICL performance, but ICL still outperforms a 2-layer Neural Network model and learns faster.
https://arxiv.org/abs//2311.12538
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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