Sign up to save your podcasts
Or
Share Approximating Two-Layer Feedforward Networks for Efficient Transformers
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
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
View all episodes
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
More shows like Arxiv Papers
View all
Exchanges
970 Listeners
Odd Lots
1,967 Listeners
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
436 Listeners
The Daily
111,948 Listeners
All-In with Chamath, Jason, Sacks & Friedberg
10,182 Listeners
Hard Fork
5,530 Listeners
UnHerd with Freddie Sayers
195 Listeners
Unsupervised Learning with Jacob Effron
52 Listeners
Latent Space: The AI Engineer Podcast
101 Listeners
BG2Pod with Brad Gerstner and Bill Gurley
491 Listeners