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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 1–50 of 75 papers

TitleStatusHype
Semantic Operators: A Declarative Model for Rich, AI-based Data ProcessingCode5
In-Context Learning for Extreme Multi-Label ClassificationCode3
Probabilistic Label Trees for Extreme Multi-label ClassificationCode1
DECAF: Deep Extreme Classification with Label FeaturesCode1
ELIAS: End-to-End Learning to Index and Search in Large Output SpacesCode1
ECLARE: Extreme Classification with Label Graph CorrelationsCode1
Propensity-scored Probabilistic Label TreesCode1
Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification FrameworkCode1
Cluster-Guided Label Generation in Extreme Multi-Label ClassificationCode1
Prototypical Extreme Multi-label Classification with a Dynamic Margin Loss—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
Content Explorer: Recommending Novel Entities for a Document Writer—0
Locally Non-linear Embeddings for Extreme Multi-label Learning—0
Multi-label Ranking: Mining Multi-label and Label Ranking Data—0
On Data Augmentation for Extreme Multi-label Classification—0
On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification—0
On Riemannian Approach for Constrained Optimization Model in Extreme Classification Problems—0
Open Vocabulary Extreme Classification Using Generative Models—0
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation—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
Efficient Text Encoders for Labor Market Analysis—0
Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification—0
Exploring space efficiency in a tree-based linear model for extreme multi-label classification—0
Extreme Classification for Answer Type Prediction in Question Answering—0
Extreme Multi-label Classification from Aggregated Labels—0
Extreme Multi-Label Classification with Label Masking for Product Attribute Value Extraction—0
Extreme Multi-label Learning for Semantic Matching in Product Search—0
Extreme Multi-Label Skill Extraction Training using Large Language Models—0
Fine-grained Generalization Analysis of Vector-valued Learning—0
From Lazy to Prolific: Tackling Missing Labels in Open Vocabulary Extreme Classification by Positive-Unlabeled Sequence Learning—0
Fully Scalable Gaussian Processes using Subspace Inducing Inputs—0
GraphEx: A Graph-based Extraction Method for Advertiser Keyphrase Recommendation—0
HGCN4MeSH: Hybrid Graph Convolution Network for MeSH Indexing—0
Investigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification—0
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