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 171–180 of 483 papers

TitleStatusHype
Geometric Relational Embeddings: A Survey—0
Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective—0
Constellation: Learning relational abstractions over objects for compositional imagination—0
Graph-to-Vision: Multi-graph Understanding and Reasoning using Vision-Language Models—0
Genesis: Towards the Automation of Systems Biology Research—0
Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text—0
Agent-Centric Representations for Multi-Agent Reinforcement Learning—0
Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning—0
Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion—0
Hyperlink Regression via Bregman Divergence—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified