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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

Showing 6170 of 75 papers

TitleStatusHype
Learning a Compressed Sensing Measurement Matrix via Gradient UnrollingCode0
Adversarial Extreme Multi-label ClassificationCode0
Stratified Sampling for Extreme Multi-Label DataCode0
Priberam at MESINESP Multi-label Classification of Medical Texts TaskCode0
A no-regret generalization of hierarchical softmax to extreme multi-label classificationCode0
Bonsai -- Diverse and Shallow Trees for Extreme Multi-label ClassificationCode0
MDACE: MIMIC Documents Annotated with Code EvidenceCode0
Multi-label Learning with Random Circular VectorsCode0
Taming Pretrained Transformers for Extreme Multi-label Text ClassificationCode0
Generalized test utilities for long-tail performance in extreme multi-label classificationCode0
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