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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 11–20 of 75 papers

TitleStatusHype
Retrieval-augmented Encoders for Extreme Multi-label Text Classification—0
Prototypical Extreme Multi-label Classification with a Dynamic Margin Loss—0
Exploring space efficiency in a tree-based linear model for extreme multi-label classification—0
GraphEx: A Graph-based Extraction Method for Advertiser Keyphrase Recommendation—0
From Lazy to Prolific: Tackling Missing Labels in Open Vocabulary Extreme Classification by Positive-Unlabeled Sequence Learning—0
Multi-label Learning with Random Circular VectorsCode0
UniDEC : Unified Dual Encoder and Classifier Training for Extreme Multi-Label Classification—0
Learning label-label correlations in Extreme Multi-label Classification via Label Features—0
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
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