Link Prediction On Wordnet
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
| Paper Title | ||
|---|---|---|
| Hyperbolic Entailment Cones | 94.4 | Hyperbolic Entailment Cones for Learning Hierarchical Embeddings |
| Poincare Embeddings (dim=100) | 77.4 | Poincaré Embeddings for Learning Hierarchical Representations |
| Poincare Embeddings (dim=50) | 77.0 | Poincaré Embeddings for Learning Hierarchical Representations |
| Poincare Embeddings (dim=20) | 74.3 | Poincaré Embeddings for Learning Hierarchical Representations |
| Poincare Embeddings (dim=10) | 68.3 | Poincaré Embeddings for Learning Hierarchical Representations |
| 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 |
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