2024-07-24-wei24a.md

title	abstract	year	volume	publisher	series	software	layout	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	genre	issued	pdf	extras
Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data Imputation	Wearable sensors enable health researchers to continuously collect data pertaining to the physiological state of individuals in real-world settings. However, such data can be subject to extensive missingness due to a complex combination of factors. In this work, we study the problem of imputation of missing step count data, one of the most ubiquitous forms of wearable sensor data. We construct a novel and large scale data set consisting of a training set with over 3 million hourly step count observations and a test set with over 2.5 million hourly step count observations. We propose a domain knowledge-informed sparse self-attention model for this task that captures the temporal multi-scale nature of step-count data. We assess the performance of the model relative to baselines and conduct ablation studies to verify our specific model designs.	2024	248	PMLR	Proceedings of Machine Learning Research	https://github.com/reml-lab/allofus-imputation	inproceedings	2640-3498	wei24a	0	Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data Imputation	137	154	137-154	137	false	Wei, Hui and Xu, Maxwell A and Samplawski, Colin and Rehg, James Matthew and Kumar, Santosh and Marlin, Benjamin	given family Hui Wei given family Maxwell A Xu given family Colin Samplawski given family James Matthew Rehg given family Santosh Kumar given family Benjamin Marlin	2024-07-24		Proceedings of the fifth Conference on Health, Inference, and Learning	inproceedings	date-parts 2024 7 24	https://raw.githubusercontent.com/mlresearch/v248/main/assets/wei24a/wei24a.pdf