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 111–120 of 483 papers

TitleStatusHype
Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields—0
Two pathways to resolve relational inconsistencies—0
FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational LearningCode0
SOLD: Slot Object-Centric Latent Dynamics Models for Relational Manipulation Learning from Pixels—0
Privately Learning from Graphs with Applications in Fine-tuning Large Language ModelsCode0
Temporal Relational Reasoning of Large Language Models for Detecting Stock Portfolio Crashes—0
Shifting the Human-AI Relationship: Toward a Dynamic Relational Learning-Partner Model—0
Inferring Scientific Cross-Document Coreference and Hierarchy with Definition-Augmented Relational ReasoningCode0
Hypergraph-based Motion Generation with Multi-modal Interaction Relational Reasoning—0
Spatiotemporal Covariance Neural NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified