Summary of Sleepnet: Attention-enhanced Robust Sleep Prediction Using Dynamic Social Networks, by Maryam Khalid et al.
SleepNet: Attention-Enhanced Robust Sleep Prediction using Dynamic Social Networks
by Maryam Khalid, Elizabeth B. Klerman, Andrew W. Mchill, Andrew J. K. Phillips, Akane Sano
First submitted to arxiv on: 20 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI); Signal Processing (eess.SP)
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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 This paper proposes a system called SleepNet, which uses graph networks and integrates physiological and phone data from ubiquitous mobile and wearable devices to predict next-day sleep labels. The system leverages social contagion in sleep behavior by incorporating attention mechanisms to overcome limitations of large-scale graphs containing connections irrelevant to sleep behavior. Experimental evaluation highlights the improvement provided by incorporating social networks in the model, demonstrating the stability of SleepNet against perturbations in input data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to predict how well you’ll sleep tomorrow using information from your phone and wearable devices. It uses special connections between people’s sleep behaviors to make a better prediction. This can help us understand why some people have trouble sleeping and what we can do to improve our sleep. The system works by looking at lots of data, including what time people go to bed, how long they sleep, and even the weather outside. It then uses this information to make a prediction about how well you’ll sleep tomorrow. |
Keywords
* Artificial intelligence * Attention
