Learning Structured Representations with Hyperbolic Embeddings
–Neural Information Processing Systems
Most real-world datasets consist of a natural hierarchy between classes or an inherent label structure that is either already available or can be constructed cheaply. However, most existing representation learning methods ignore this hierarchy, treating labels as permutation invariant.
Neural Information Processing Systems
Oct-10-2025, 12:18:14 GMT
- Country:
- North America
- United States
- Michigan (0.04)
- Illinois > Champaign County
- Urbana (0.04)
- Canada > Ontario
- Toronto (0.04)
- United States
- Europe
- Germany (0.04)
- United Kingdom > England
- Cambridgeshire > Cambridge (0.04)
- Asia > Japan
- Kyūshū & Okinawa > Okinawa (0.04)
- North America
- Genre:
- Research Report > Experimental Study (1.00)
- Industry:
- Information Technology (0.92)
- Government (0.67)
- Technology:
- Information Technology
- Data Science > Data Mining (1.00)
- Artificial Intelligence
- Vision (1.00)
- Representation & Reasoning (1.00)
- Natural Language (1.00)
- Cognitive Science (1.00)
- Machine Learning
- Statistical Learning (1.00)
- Neural Networks > Deep Learning (1.00)
- Information Technology