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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 341–350 of 483 papers

TitleStatusHype
Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation—0
Benchmarking Systematic Relational Reasoning with Large Language and Reasoning Models—0
Boosting gets full Attention for Relational Learning—0
Boosting Neural Language Inference via Cascaded Interactive Reasoning—0
A Statistical Relational Approach to Learning Distance-based GCNs—0
Broadcasting Convolutional Network for Visual Relational Reasoning—0
Causal Relational Learning—0
Chain of Reasoning for Visual Question Answering—0
Character-based recurrent neural networks for morphological relational reasoning—0
Classification by Attention: Scene Graph Classification with Prior Knowledge—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified