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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 51–75 of 75 papers

TitleStatusHype
Label Disentanglement in Partition-based Extreme Multilabel Classification—0
Learning from eXtreme Bandit Feedback—0
Learning label-label correlations in Extreme Multi-label Classification via Label Features—0
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model—0
DiSMEC - Distributed Sparse Machines for Extreme Multi-label ClassificationCode0
Dense Retrieval as Indirect Supervision for Large-space Decision MakingCode0
Dual-Encoders for Extreme Multi-Label ClassificationCode0
Revisiting the Vector Space Model: Sparse Weighted Nearest-Neighbor Method for Extreme Multi-Label ClassificationCode0
Top-k eXtreme Contextual Bandits with Arm HierarchyCode0
Deep Extreme Multi-label LearningCode0
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
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood PredictionCode0
HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text CategorizationCode0
Adaptive Elastic Training for Sparse Deep Learning on Heterogeneous Multi-GPU ServersCode0
ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label ClassificationCode0
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label ClassificationCode0
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