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 201–210 of 483 papers

TitleStatusHype
Learning Reasoning Patterns for Relational Triple Extraction with Mutual Generation of Text and Graph—0
Optimal quadratic binding for relational reasoning in vector symbolic neural architecturesCode0
Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction—0
Relational Reasoning Over Spatial-Temporal Graphs for Video Summarization—0
MGRR-Net: Multi-level Graph Relational Reasoning Network for Facial Action Units Detection—0
MoReL: Multi-omics Relational Learning—0
Important Object Identification with Semi-Supervised Learning for Autonomous Driving—0
Probabilities of the Third Type: Statistical Relational Learning and Reasoning with Relative Frequencies—0
Learning to Coarsen Graphs with Graph Neural Networks—0
Does Entity Abstraction Help Generative Transformers Reason?—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified