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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 31–40 of 75 papers

TitleStatusHype
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
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