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 71–80 of 483 papers

TitleStatusHype
EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question AnsweringCode1
Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation—0
zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language ModelsCode1
When can transformers reason with abstract symbols?Code0
Large Language Models can Learn RulesCode1
Associative TransformerCode0
A Novel Neural-symbolic System under Statistical Relational Learning—0
Quantifying and Attributing the Hallucination of Large Language Models via Association Analysis—0
Redundancy-Free Self-Supervised Relational Learning for Graph ClusteringCode1
Reconstructing Groups of People with Hypergraph Relational ReasoningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified