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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–160 of 483 papers

TitleStatusHype
Knowledge Graph Completion via Complex Tensor FactorizationCode0
Explaining Local, Global, And Higher-Order Interactions In Deep LearningCode0
Identifying Morality Frames in Political Tweets using Relational LearningCode0
Cognitive Knowledge Graph Reasoning for One-shot Relational LearningCode0
In-Context Analogical Reasoning with Pre-Trained Language ModelsCode0
Inferring Scientific Cross-Document Coreference and Hierarchy with Definition-Augmented Relational ReasoningCode0
Language-Conditioned Graph Networks for Relational ReasoningCode0
Logic Tensor Networks for Semantic Image InterpretationCode0
Graph Neural Networks with Generated Parameters for Relation ExtractionCode0
Graph-Based Global Reasoning NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified