Link Prediction On Wordnet
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | Accuracy | Paper Title | Repository |
|---|---|---|---|
| Poincare Embeddings (dim=10) | 68.3 | Poincaré Embeddings for Learning Hierarchical Representations | - |
| Hyperbolic Entailment Cones | 94.4 | Hyperbolic Entailment Cones for Learning Hierarchical Embeddings | - |
| Hyper-SAGNN-W | - | Hyper-SAGNN: a self-attention based graph neural network for hypergraphs | - |
| Hyper-SAGNN-E | - | Hyper-SAGNN: a self-attention based graph neural network for hypergraphs | - |
| Poincare Embeddings (dim=20) | 74.3 | Poincaré Embeddings for Learning Hierarchical Representations | - |
| Poincare Embeddings (dim=50) | 77.0 | Poincaré Embeddings for Learning Hierarchical Representations | - |
| Poincare Embeddings (dim=100) | 77.4 | Poincaré Embeddings for Learning Hierarchical Representations | - |
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