Z
Zhixiang Xu
Researcher at Washington University in St. Louis
Publications - 14
Citations - 1789
Zhixiang Xu is an academic researcher from Washington University in St. Louis. The author has contributed to research in topics: Feature extraction & Feature selection. The author has an hindex of 12, co-authored 14 publications receiving 1670 citations. Previous affiliations of Zhixiang Xu include University of Washington.
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Marginalized Denoising Autoencoders for Domain Adaptation
TL;DR: This paper proposed marginalized SDA (mSDA) that addresses two crucial limitations of stacked denoising autoencoders: high computational cost and lack of scalability to high-dimensional features.
Proceedings Article
Marginalized Denoising Autoencoders for Domain Adaptation
TL;DR: The approach of mSDA marginalizes noise and thus does not require stochastic gradient descent or other optimization algorithms to learn parameters--in fact, they are computed in closed-form, significantly speeds up SDAs by two orders of magnitude.
Proceedings ArticleDOI
Gradient boosted feature selection
TL;DR: Gradient Boosted Feature Selection (GBFS) as mentioned in this paper is a feature selection algorithm that is based on a modification of gradient boosted trees and is shown to match or outperform other state of the art feature selection algorithms.
Proceedings Article
The Greedy Miser: Learning under Test-time Budgets
TL;DR: An algorithm is proposed, the Greedy Miser, that incorporates the feature extraction cost during training to explicitly minimize the cpu-time during testing and is significantly more cost-effective and scales to larger data sets.
Posted Content
The Greedy Miser: Learning under Test-time Budgets
TL;DR: Greedy Miser as discussed by the authors incorporates the feature extraction cost during training to explicitly minimize the cpu-time during testing, which is a straightforward extension of stage-wise regression and is equally suitable for regression or multi-class classification.