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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 26–50 of 75 papers

TitleStatusHype
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
Review of Extreme Multilabel Classification—0
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label ClassificationCode0
Uncertainty in Extreme Multi-label Classification—0
On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification—0
Augmenting Training Data for Massive Semantic Matching Models in Low-Traffic E-commerce Stores—0
Investigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification—0
Open Vocabulary Extreme Classification Using Generative Models—0
Extreme Multi-Label Classification with Label Masking for Product Attribute Value Extraction—0
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood PredictionCode0
Adaptive Elastic Training for Sparse Deep Learning on Heterogeneous Multi-GPU ServersCode0
On Riemannian Approach for Constrained Optimization Model in Extreme Classification Problems—0
TailMix: Overcoming the Label Sparsity for Extreme Multi-label Classification—0
Speeding-up One-vs-All Training for Extreme Classification via Smart Initialization—0
Unbiased Loss Functions for Multilabel Classification with Missing Labels—0
Label Disentanglement in Partition-based Extreme Multilabel Classification—0
Extreme Multi-label Learning for Semantic Matching in Product SearchCode0
Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification—0
Priberam at MESINESP Multi-label Classification of Medical Texts TaskCode0
Fine-grained Generalization Analysis of Vector-valued Learning—0
Stratified Sampling for Extreme Multi-Label DataCode0
Top-k eXtreme Contextual Bandits with Arm HierarchyCode0
Multi-label Ranking: Mining Multi-label and Label Ranking Data—0
Tensor Composition Net for Visual Relationship Prediction—0
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