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[short] Approximating Two-Layer Feedforward Networks for Efficient Transformers
The paper introduces a general framework for approximating two-layer neural networks using sparse Mixtures of Experts (MoEs) and product-key memories (PKMs). The proposed methods improve both MoEs and PKMs, showing that they are competitive with dense models while being more resource efficient.
https://arxiv.org/abs//2310.10837
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
The paper introduces a general framework for approximating two-layer neural networks using sparse Mixtures of Experts (MoEs) and product-key memories (PKMs). The proposed methods improve both MoEs and PKMs, showing that they are competitive with dense models while being more resource efficient.
https://arxiv.org/abs//2310.10837
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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