Summary of Temporally Multi-scale Sparse Self-attention For Physical Activity Data Imputation, by Hui Wei et al.
Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data Imputation
by Hui Wei, Maxwell A. Xu, Colin Samplawski, James M. Rehg, Santosh Kumar, Benjamin M. Marlin
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes a novel approach to imputing missing step count data from wearable sensors, using a large-scale dataset with over 5 million hourly observations. The authors develop a domain-knowledge-informed sparse self-attention model that captures the temporal multi-scale nature of step-count data. They evaluate their model’s performance against baselines and conduct ablation studies to verify design choices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special sensors on people’s clothes to collect health data in real-life situations. Sometimes, this data can be missing because of various reasons. The researchers are trying to find a way to fill in these gaps. They created a big dataset with over 5 million hours’ worth of step count information and developed a new model that can predict missing values. The model is special because it takes into account the patterns and rhythms of people’s daily activities. |
Keywords
* Artificial intelligence * Self attention
