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Extreme Multi-Label Classification
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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.
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Retrieval-augmented Encoders for Extreme Multi-label Text Classification
Prototypical Extreme Multi-label Classification with a Dynamic Margin Loss
Exploring space efficiency in a tree-based linear model for extreme multi-label classification
GraphEx: A Graph-based Extraction Method for Advertiser Keyphrase Recommendation
From Lazy to Prolific: Tackling Missing Labels in Open Vocabulary Extreme Classification by Positive-Unlabeled Sequence Learning
Semantic Operators: A Declarative Model for Rich, AI-based Data Processing
Multi-label Learning with Random Circular Vectors
UniDEC : Unified Dual Encoder and Classifier Training for Extreme Multi-Label Classification
Learning label-label correlations in Extreme Multi-label Classification via Label Features
In-Context Learning for Extreme Multi-Label Classification
ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification
Generalized test utilities for long-tail performance in extreme multi-label classification
Dense Retrieval as Indirect Supervision for Large-space Decision Making
Dual-Encoders for Extreme Multi-Label Classification
Extreme Multi-Label Skill Extraction Training using Large Language Models
MDACE: MIMIC Documents Annotated with Code Evidence
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation
Extreme Classification for Answer Type Prediction in Question Answering
Adopting the Multi-answer Questioning Task with an Auxiliary Metric for Extreme Multi-label Text Classification Utilizing the Label Hierarchy
Cluster-Guided Label Generation in Extreme Multi-Label Classification
Review of Extreme Multilabel Classification
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification
Uncertainty in Extreme Multi-label Classification
ELIAS: End-to-End Learning to Index and Search in Large Output Spaces
On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification
Open Vocabulary Extreme Classification Using Generative Models
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
Propensity-scored Probabilistic Label Trees
Adaptive Elastic Training for Sparse Deep Learning on Heterogeneous Multi-GPU Servers
On Riemannian Approach for Constrained Optimization Model in Extreme Classification Problems
Speeding-up One-vs-All Training for Extreme Classification via Smart Initialization
Unbiased Loss Functions for Multilabel Classification with Missing Labels
DECAF: Deep Extreme Classification with Label Features
ECLARE: Extreme Classification with Label Graph Correlations
Label Disentanglement in Partition-based Extreme Multilabel Classification
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