90 citations · 141 across the 11 of their papers we have counts for
10 papers · 1 filter
Domain Adaptation Under Behavioral and Temporal Shifts for Natural Time Series Mobile Activity Recognition
Garrett Wilson, Janardhan Rao Doppa, Diane J. Cook
Increasingly, human behavior is captured on mobile devices, leading to an increased interest in automated human activity recognition. However, existing datasets typically consist o…
HydraGAN A Multi-head, Multi-objective Approach to Synthetic Data Generation
Chance N DeSmet, Diane J Cook
Synthetic data generation overcomes limitations of real-world machine learning. Traditional methods are valuable for augmenting costly datasets but only optimize one criterion: rea…
Mimic: An adaptive algorithm for multivariate time series classification
Yuhui Wang, Diane J. Cook
Time series data are valuable but are often inscrutable. Gaining trust in time series classifiers for finance, healthcare, and other critical applications may rely on creating inte…
CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial Learning
Garrett Wilson, Janardhan Rao Doppa, Diane J. Cook
Unsupervised domain adaptation (UDA) provides a strategy for improving machine learning performance in data-rich (target) domains where ground truth labels are inaccessible but can…
Transfer Learning for Activity Recognition in Mobile Health
Yuchao Ma, Andrew T. Campbell, Diane J. Cook +6
While activity recognition from inertial sensors holds potential for mobile health, differences in sensing platforms and user movement patterns cause performance degradation. Aimin…
Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data
Garrett Wilson, Janardhan Rao Doppa, Diane J. Cook
Domain adaptation (DA) offers a valuable means to reuse data and models for new problem domains. However, robust techniques have not yet been considered for time series data with v…