Summary of Landmark Alternating Diffusion, by Sing-yuan Yeh et al.
Landmark Alternating Diffusion
by Sing-Yuan Yeh, Hau-Tieng Wu, Ronen Talmon, Mao-Pei Tsui
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistics Theory (math.ST); Data Analysis, Statistics and Probability (physics.data-an); Machine Learning (stat.ML)
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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 In this paper, researchers introduce Landmark Alternating Diffusion (LAD), an improved version of the widely used Alternating Diffusion (AD) sensor fusion algorithm. The authors aim to reduce AD’s computational burden while maintaining its performance by incorporating ideas from Robust and Scalable Embedding via Landmark Diffusion (ROSELAND). They provide theoretical analyses of LAD under the manifold setup and demonstrate its application in automatic sleep stage annotation using electroencephalogram channels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LAD is a new way to combine data from multiple sources. It’s faster than the old method, Alternating Diffusion (AD), which is often used for sensor fusion. The researchers took ideas from ROSELAND and made them work with AD. They showed that LAD can be used for a specific task: identifying sleep stages using brain wave recordings. |
Keywords
» Artificial intelligence » Diffusion » Embedding
