
Sign up to save your podcasts
Or


The paper proposes combining State Space Models (SSMs) with Mixture of Experts (MoE) to unlock the scaling potential of SSMs. The resulting model, MoE-Mamba, outperforms both Mamba and Transformer-MoE in terms of performance and training steps.
https://arxiv.org/abs//2401.04081
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
The paper proposes combining State Space Models (SSMs) with Mixture of Experts (MoE) to unlock the scaling potential of SSMs. The resulting model, MoE-Mamba, outperforms both Mamba and Transformer-MoE in terms of performance and training steps.
https://arxiv.org/abs//2401.04081
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

970 Listeners

1,967 Listeners

436 Listeners

111,948 Listeners

10,182 Listeners

5,530 Listeners

195 Listeners

52 Listeners

101 Listeners

491 Listeners