SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 11–20 of 75 papers

TitleStatusHype
In-Context Learning for Extreme Multi-Label ClassificationCode3
ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label ClassificationCode0
Generalized test utilities for long-tail performance in extreme multi-label classificationCode0
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
Show:102550
← PrevPage 2 of 8Next →

No leaderboard results yet.