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Deep Learning with Label Noise: A Hierarchical Approach
Our hierarchical approach improves upon regular deep neural networks in learning with label noise.
Mental State Classification Using Multi-graph Features
We consider the problem of extracting features from passive, multi-channel electroencephalogram (EEG) devices for downstream inference tasks related to high-level mental states such as stress and cognitive load.
Inducing a hierarchy for multi-class classification problems
In applications where categorical labels follow a natural hierarchy, classification methods that exploit the label structure often outperform those that do not.
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