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The AdEMAMix Optimizer: Better, Faster, Older
This paper critiques single EMA usage in momentum optimizers, proposing AdEMAMix, which combines two EMAs for improved gradient relevance, faster convergence, and reduced model forgetting in training.
https://arxiv.org/abs//2409.03137
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 critiques single EMA usage in momentum optimizers, proposing AdEMAMix, which combines two EMAs for improved gradient relevance, faster convergence, and reduced model forgetting in training.
https://arxiv.org/abs//2409.03137
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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