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Extreme Multi-Label Classification

Extreme Multi-Label Classification is a supervised learning problem where an instance may be associated with multiple labels. The two main problems are the unbalanced labels in the dataset and the amount of different labels.

Papers

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Showing 31–40 of 75 papers

TitleStatusHype
Subset Labeled LDA for Large-Scale Multi-Label Classification—0
TailMix: Overcoming the Label Sparsity for Extreme Multi-label Classification—0
Tensor Composition Net for Visual Relationship Prediction—0
The Emerging Trends of Multi-Label Learning—0
Unbiased Loss Functions for Extreme Classification With Missing Labels—0
Unbiased Loss Functions for Multilabel Classification with Missing Labels—0
Uncertainty in Extreme Multi-label Classification—0
UniDEC : Unified Dual Encoder and Classifier Training for Extreme Multi-Label Classification—0
Efficient Text Encoders for Labor Market Analysis—0
Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification—0
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