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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
The Emerging Trends of Multi-Label Learning—0
Learning from eXtreme Bandit Feedback—0
On Data Augmentation for Extreme Multi-label Classification—0
HGCN4MeSH: Hybrid Graph Convolution Network for MeSH Indexing—0
Unbiased Loss Functions for Extreme Classification With Missing Labels—0
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model—0
Extreme Multi-label Classification from Aggregated Labels—0
On-the-fly Global Embeddings Using Random Projections for Extreme Multi-label Classification—0
Taming Pretrained Transformers for Extreme Multi-label Text ClassificationCode0
Bonsai -- Diverse and Shallow Trees for Extreme Multi-label ClassificationCode0
Ranking-Based Autoencoder for Extreme Multi-label Classification—0
SeCSeq: Semantic Coding for Sequence-to-Sequence based Extreme Multi-label Classification—0
Block-wise Partitioning for Extreme Multi-label Classification—0
A no-regret generalization of hierarchical softmax to extreme multi-label classificationCode0
Content Explorer: Recommending Novel Entities for a Document Writer—0
HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text CategorizationCode0
Fully Scalable Gaussian Processes using Subspace Inducing Inputs—0
Learning a Compressed Sensing Measurement Matrix via Gradient UnrollingCode0
Adversarial Extreme Multi-label ClassificationCode0
Revisiting the Vector Space Model: Sparse Weighted Nearest-Neighbor Method for Extreme Multi-Label ClassificationCode0
Subset Labeled LDA for Large-Scale Multi-Label Classification—0
Deep Extreme Multi-label LearningCode0
DiSMEC - Distributed Sparse Machines for Extreme Multi-label ClassificationCode0
Sparse Local Embeddings for Extreme Multi-label Classification—0
Locally Non-linear Embeddings for Extreme Multi-label Learning—0
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