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 241–250 of 483 papers

TitleStatusHype
SAG-VAE: End-to-end Joint Inference of Data Representations and Feature Relations—0
Sampling Community Structure—0
SARN: Relational Reasoning through Sequential Attention—0
Scalable Statistical Relational Learning for NLP—0
Schema Independent Relational Learning—0
SCR-Graph: Spatial-Causal Relationships based Graph Reasoning Network for Human Action Prediction—0
Search Space Properties for Learning a Class of Constraint-based Grammars—0
Self-supervised Multi-actor Social Activity Understanding in Streaming Videos—0
Set Interdependence Transformer: Set-to-Sequence Neural Networks for Permutation Learning and Structure Prediction—0
Shifting the Human-AI Relationship: Toward a Dynamic Relational Learning-Partner Model—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified