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 141–150 of 483 papers

TitleStatusHype
Position: Topological Deep Learning is the New Frontier for Relational Learning—0
Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation—0
LLMs for Relational Reasoning: How Far are We?—0
MLAD: A Unified Model for Multi-system Log Anomaly Detection—0
Higher-order Relational Reasoning for Pedestrian Trajectory Prediction—0
Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation—0
When can transformers reason with abstract symbols?Code0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified