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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
DiSMEC - Distributed Sparse Machines for Extreme Multi-label ClassificationCode0
Generalized test utilities for long-tail performance in extreme multi-label classificationCode0
Priberam at MESINESP Multi-label Classification of Medical Texts TaskCode0
Dual-Encoders for Extreme Multi-Label ClassificationCode0
HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text CategorizationCode0
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation—0
Prototypical Extreme Multi-label Classification with a Dynamic Margin Loss—0
Ranking-Based Autoencoder for Extreme Multi-label Classification—0
Retrieval-augmented Encoders for Extreme Multi-label Text Classification—0
Review of Extreme Multilabel Classification—0
SeCSeq: Semantic Coding for Sequence-to-Sequence based Extreme Multi-label Classification—0
Sparse Local Embeddings for Extreme Multi-label Classification—0
Speeding-up One-vs-All Training for Extreme Classification via Smart Initialization—0
Subset Labeled LDA for Large-Scale Multi-Label Classification—0
TailMix: Overcoming the Label Sparsity for Extreme Multi-label Classification—0
Tensor Composition Net for Visual Relationship Prediction—0
The Emerging Trends of Multi-Label Learning—0
Unbiased Loss Functions for Extreme Classification With Missing Labels—0
Unbiased Loss Functions for Multilabel Classification with Missing Labels—0
Uncertainty in Extreme Multi-label Classification—0
UniDEC : Unified Dual Encoder and Classifier Training for Extreme Multi-Label Classification—0
Adopting the Multi-answer Questioning Task with an Auxiliary Metric for Extreme Multi-label Text Classification Utilizing the Label Hierarchy—0
On-the-fly Global Embeddings Using Random Projections for Extreme Multi-label Classification—0
Augmenting Training Data for Massive Semantic Matching Models in Low-Traffic E-commerce Stores—0
Block-wise Partitioning for Extreme Multi-label Classification—0
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