G
Garrett Wilson
Researcher at Washington State University
Publications - 10
Citations - 744
Garrett Wilson is an academic researcher from Washington State University. The author has contributed to research in topics: Deep learning & Supervised learning. The author has an hindex of 5, co-authored 9 publications receiving 298 citations.
Papers
More filters
Journal ArticleDOI
A Survey of Unsupervised Deep Domain Adaptation
Garrett Wilson,Diane J. Cook +1 more
TL;DR: A survey will compare single-source and typically homogeneous unsupervised deep domain adaptation approaches, combining the powerful, hierarchical representations from deep learning with domain adaptation to reduce reliance on potentially costly target data labels.
Journal ArticleDOI
Robot-Enabled Support of Daily Activities in Smart Home Environments.
Garrett Wilson,Christopher Pereyda,Nisha Raghunath,Gabriel Victor de la Cruz,Shivam Goel,Sepehr Nesaei,Bryan Minor,Maureen Schmitter-Edgecombe,Matthew D. Taylor,Diane J. Cook +9 more
TL;DR: An integration of robots into smart environments to provide more interactive support of individuals with functional limitations is described and the components of the RAS system are described and its use in a smart home testbed is demonstrated.
Posted Content
A Survey of Unsupervised Deep Domain Adaptation
Garrett Wilson,Diane J. Cook +1 more
TL;DR: Many single-source and typically homogeneous unsupervised deep domain adaptation approaches have been developed, combining the powerful, hierarchical representations from deep learning with domain adaptation to reduce reliance on potentially-costly target data labels.
Posted Content
Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data
TL;DR: A novel Convolutional deep Domain Adaptation model for Time Series data (CoDATS) that significantly improves accuracy and training time over state-of-the-art DA strategies on real-world sensor data benchmarks and a novel Domain adaptation with Weak Supervision (DA-WS) method by utilizing weak supervision in the form of target-domain label distributions, which may be easier to collect than additional data labels.
Proceedings ArticleDOI
Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data
TL;DR: In this article, the authors proposed a domain adaptation model for time series data with varying amounts of data availability, which improves accuracy and training time over state-of-the-art DA strategies on realworld sensor data benchmarks.