Minimum variance semi-supervised boosting for multi-label classification

Zhao, Chenyang; Zhai, Shaodan

doi:10.1109/globalsip.2015.7418417

Cited by 2 publications

(1 citation statement)

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“…Our method draws inspiration from transductive experimental design, which chooses the most informative points (experiments) to measure by seeking data points that are both hard to predict and informative for the unexplored test data [40]. Similar prediction uncertainty approaches have been explored in semi-supervised classification models, such as minimum entropy and minimum variance regularization, which can now also be understood in the posterior regularization framework [7,41].…”

Section: Incorporating Spatial Informationmentioning

confidence: 99%

Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance

Jean,

Xie,

Ermon

2018

Preprint

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Large amounts of labeled data are typically required to train deep learning models. For many real-world problems, however, acquiring additional data can be expensive or even impossible. We present semi-supervised deep kernel learning (SSDKL), a semi-supervised regression model based on minimizing predictive variance in the posterior regularization framework. SSDKL combines the hierarchical representation learning of neural networks with the probabilistic modeling capabilities of Gaussian processes. By leveraging unlabeled data, we show improvements on a diverse set of real-world regression tasks over supervised deep kernel learning and semi-supervised methods such as VAT and mean teacher adapted for regression. * denotes equal contribution Preprint. Work in progress.

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Section: Incorporating Spatial Informationmentioning

confidence: 99%