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 321–330 of 483 papers

TitleStatusHype
A logic-based relational learning approach to relation extraction: The OntoILPER system—0
A Modern Take on Visual Relationship Reasoning for Grasp Planning—0
A More Compact Object Detector Head Network with Feature Enhancement and Relational Reasoning—0
Analogical and Relational Reasoning with Spiking Neural Networks—0
Analysis of Nuanced Stances and Sentiment Towards Entities of US Politicians through the Lens of Moral Foundation Theory—0
A Multi-Task Perspective for Link Prediction with New Relation Types and Nodes—0
A Novel Neural-symbolic System under Statistical Relational Learning—0
Application of Statistical Relational Learning to Hybrid Recommendation Systems—0
A Preliminary Approach for Learning Relational Policies for the Management of Critically Ill Children—0
A Probit Tensor Factorization Model For Relational Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified