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
Joint Information Extraction and Reasoning: A Scalable Statistical Relational Learning Approach—0
Numeric Input Relations for Relational Learning with Applications to Community Structure Analysis—0
Stochastic And-Or Grammars: A Unified Framework and Logic Perspective—0
SkILL - a Stochastic Inductive Logic Learner—0
Content+Context=Classification: Examining the Roles of Social Interactions and Linguist Content in Twitter User Classification—0
Contrastive Feature Induction for Efficient Structure Learning of Conditional Random Fields—0
Recursive Neural Networks Can Learn Logical Semantics—0
kLogNLP: Graph Kernel--based Relational Learning of Natural Language—0
Locally Boosted Graph Aggregation for Community Detection—0
Understanding the Complexity of Lifted Inference and Asymmetric Weighted Model Counting—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified