Summary of An Analysis Of Linear Complexity Attention Substitutes with Best-rq, by Ryan Whetten et al.
An Analysis of Linear Complexity Attention Substitutes with BEST-RQ
by Ryan Whetten, Titouan Parcollet, Adel Moumen, Marco Dinarelli, Yannick Estève
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the effectiveness of replacing multi-head self-attention (MHSA) with linear alternatives in a Self-Supervised Learning (SSL) setting, focusing on speech processing. Recent state-of-the-art methods like HyperMixing, Fastformer, SummaryMixing, and Mamba are evaluated for their impact on computational resources and performance on the SSL MP3S benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows that these linear alternatives maintain competitive performance to MHSA while reducing VRAM consumption by 20-60% and increasing speed by 7-65%. This could lead to more efficient speech processing applications, making it a valuable contribution to the field of SSL in this domain. |
Keywords
» Artificial intelligence » Self attention » Self supervised
