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 451–460 of 483 papers

TitleStatusHype
Optimal quadratic binding for relational reasoning in vector symbolic neural architecturesCode0
ORCHARD: A Benchmark For Measuring Systematic Generalization of Multi-Hierarchical ReasoningCode0
Interaction Relational Network for Mutual Action RecognitionCode0
PARN: Position-Aware Relation Networks for Few-Shot LearningCode0
An Insect-Inspired Randomly, Weighted Neural Network with Random Fourier Features For Neuro-Symbolic Relational LearningCode0
Inferring Scientific Cross-Document Coreference and Hierarchy with Definition-Augmented Relational ReasoningCode0
In-Context Analogical Reasoning with Pre-Trained Language ModelsCode0
Disentangling and Integrating Relational and Sensory Information in Transformer ArchitecturesCode0
Robust Face Tracking using Multiple Appearance Models and Graph Relational LearningCode0
Coresets for Relational Data and The ApplicationsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified