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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 21–30 of 75 papers

TitleStatusHype
Dense Retrieval as Indirect Supervision for Large-space Decision MakingCode0
Dual-Encoders for Extreme Multi-Label ClassificationCode0
Extreme Multi-Label Skill Extraction Training using Large Language Models—0
MDACE: MIMIC Documents Annotated with Code EvidenceCode0
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood AggregationCode0
Extreme Classification for Answer Type Prediction in Question Answering—0
Adopting the Multi-answer Questioning Task with an Auxiliary Metric for Extreme Multi-label Text Classification Utilizing the Label Hierarchy—0
Review of Extreme Multilabel Classification—0
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label ClassificationCode0
Uncertainty in Extreme Multi-label Classification—0
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