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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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TitleStatusHype
Semantic Operators: A Declarative Model for Rich, AI-based Data ProcessingCode5
In-Context Learning for Extreme Multi-Label ClassificationCode3
Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification FrameworkCode1
Cluster-Guided Label Generation in Extreme Multi-Label ClassificationCode1
ECLARE: Extreme Classification with Label Graph CorrelationsCode1
Propensity-scored Probabilistic Label TreesCode1
ELIAS: End-to-End Learning to Index and Search in Large Output SpacesCode1
DECAF: Deep Extreme Classification with Label FeaturesCode1
Probabilistic Label Trees for Extreme Multi-label ClassificationCode1
Augmenting Training Data for Massive Semantic Matching Models in Low-Traffic E-commerce Stores—0
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