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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
Efficient Text Encoders for Labor Market Analysis—0
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
Extreme Multi-Label Classification with Label Masking for Product Attribute Value Extraction—0
HGCN4MeSH: Hybrid Graph Convolution Network for MeSH Indexing—0
Extreme Multi-Label Skill Extraction Training using Large Language Models—0
Fine-grained Generalization Analysis of Vector-valued Learning—0
From Lazy to Prolific: Tackling Missing Labels in Open Vocabulary Extreme Classification by Positive-Unlabeled Sequence Learning—0
Fully Scalable Gaussian Processes using Subspace Inducing Inputs—0
Block-wise Partitioning for Extreme Multi-label Classification—0
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