SOTAVerified

Relational Reasoning

The goal of Relational Reasoning is to figure out the relationships among different entities, such as image pixels, words or sentences, human skeletons or interactive moving agents.

Source: Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network

Papers

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Showing 201–210 of 483 papers

TitleStatusHype
Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection—0
Extrapolatable Relational Reasoning With Comparators in Low-Dimensional Manifolds—0
FreeQ-Graph: Free-form Querying with Semantic Consistent Scene Graph for 3D Scene Understanding—0
Analysis of Nuanced Stances and Sentiment Towards Entities of US Politicians through the Lens of Moral Foundation Theory—0
Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation—0
Deep Learning for Ontology Reasoning—0
A Semantic Matching Energy Function for Learning with Multi-relational Data—0
Introducing DRAIL -- a Step Towards Declarative Deep Relational Learning—0
Knowledge Graph Embedding via Graph Attenuated Attention Networks—0
A Boosting Approach to Learning Graph Representations—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified