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pro vyhledávání: '"Wilson, Garrett"'
Increasingly, human behavior is captured on mobile devices, leading to an increased interest in automated human activity recognition. However, existing datasets typically consist of scripted movements. Our long-term goal is to perform mobile activity
Externí odkaz:
http://arxiv.org/abs/2207.04367
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 be found in related (source) domains. In cases where meta-domain info
Externí odkaz:
http://arxiv.org/abs/2109.14778
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 varying amounts of data availability. In this paper, we make three main
Externí odkaz:
http://arxiv.org/abs/2005.10996
Publikováno v:
The Phi Delta Kappan, 2022 Apr 01. 103(7), 47-50.
Externí odkaz:
https://www.jstor.org/stable/27158565
Autor:
Wilson, Garrett, Cook, Diane J.
Often domain adaptation is performed using a discriminator (domain classifier) to learn domain-invariant feature representations so that a classifier trained on labeled source data will generalize well to unlabeled target data. A line of research ste
Externí odkaz:
http://arxiv.org/abs/1907.07802
Autor:
Wilson, Garrett, Cook, Diane J.
Deep learning has produced state-of-the-art results for a variety of tasks. While such approaches for supervised learning have performed well, they assume that training and testing data are drawn from the same distribution, which may not always be th
Externí odkaz:
http://arxiv.org/abs/1812.02849
Autor:
Wilson, Garrett, Pereyda, Christopher, Raghunath, Nisha, de la Cruz, Gabriel, Goel, Shivam, Nesaei, Sepehr, Minor, Bryan, Schmitter-Edgecombe, Maureen, Taylor, Matthew E., Cook, Diane J.
Publikováno v:
In Cognitive Systems Research May 2019 54:258-272
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