HyperLabel: Multi-Label Classification via Hypergraph-Based Label Correlation Modeling

arXiv:2609.32276 · cs.LG · Submitted 2026-09-26 · Read on arXiv

cs.LG

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/iZHpy/Multi-label_hypergraph

Terminology

• Hypergraph — 65× in this paper · explained in 2 episodes
• Embeddings — 17× in this paper · explained in 2 episodes
• Transformer — 15× in this paper · explained in 2 episodes
• Contrastive Learning — 8× in this paper · explained in 5 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• F1 Score — 5× in this paper · explained in 3 episodes
• Aggregation — 4× in this paper · explained in 1 episode
• Alignment — 4× in this paper · explained in 1 episode
• Interpretability — 4× in this paper · explained in 15 episodes
• Latent Space — 4× in this paper · explained in 7 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Attention Mechanism — 3× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• Latent Representation — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Ablation study — 2× in this paper · explained in 1 episode
• Hamming Accuracy — 2× in this paper
• Latent Representations — 2× in this paper · explained in 3 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Cross-Attention Mechanism — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Multi-hop Reasoning — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• Auto-Encoding Variational Bayes

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks