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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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Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification
Priberam at MESINESP Multi-label Classification of Medical Texts Task
Fine-grained Generalization Analysis of Vector-valued Learning
Stratified Sampling for Extreme Multi-Label Data
Top-$k$ eXtreme Contextual Bandits with Arm Hierarchy
Multi-label Ranking: Mining Multi-label and Label Ranking Data
Tensor Composition Net for Visual Relationship Prediction
The Emerging Trends of Multi-Label Learning
Learning from eXtreme Bandit Feedback
Probabilistic Label Trees for Extreme Multi-label Classification
On Data Augmentation for Extreme Multi-label Classification
Unbiased Loss Functions for Extreme Classification With Missing Labels
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model
Extreme Multi-label Classification from Aggregated Labels
On-the-fly Global Embeddings Using Random Projections for Extreme Multi-label Classification
Taming Pretrained Transformers for Extreme Multi-label Text Classification
Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification
Ranking-Based Autoencoder for Extreme Multi-label Classification
Block-wise Partitioning for Extreme Multi-label Classification
A no-regret generalization of hierarchical softmax to extreme multi-label classification
Fully Scalable Gaussian Processes using Subspace Inducing Inputs
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
Adversarial Extreme Multi-label Classification
Revisiting the Vector Space Model: Sparse Weighted Nearest-Neighbor Method for Extreme Multi-Label Classification
Subset Labeled LDA for Large-Scale Multi-Label Classification
Deep Extreme Multi-label Learning
DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification
Locally Non-linear Embeddings for Extreme Multi-label Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.