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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 51–60 of 75 papers

TitleStatusHype
Content Explorer: Recommending Novel Entities for a Document Writer—0
Efficient Text Encoders for Labor Market Analysis—0
Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification—0
Exploring space efficiency in a tree-based linear model for extreme multi-label classification—0
Extreme Classification for Answer Type Prediction in Question Answering—0
Extreme Multi-label Classification from Aggregated Labels—0
Extreme Multi-Label Classification with Label Masking for Product Attribute Value Extraction—0
Extreme Multi-label Learning for Semantic Matching in Product Search—0
Extreme Multi-Label Skill Extraction Training using Large Language Models—0
Fine-grained Generalization Analysis of Vector-valued Learning—0
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