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 371–380 of 483 papers

TitleStatusHype
Compositional Language Understanding with Text-based Relational ReasoningCode0
Multi-layer Relation Networks—0
Effective Learning of Probabilistic Models for Clinical Predictions from Longitudinal Data—0
Dilated DenseNets for Relational Reasoning—0
SARN: Relational Reasoning through Sequential Attention—0
Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network—0
The Visual QA Devil in the Details: The Impact of Early Fusion and Batch Norm on CLEVR—0
Mapping Natural Language Commands to Web ElementsCode0
One-Shot Relational Learning for Knowledge GraphsCode0
LinkNBed: Multi-Graph Representation Learning with Entity Linkage—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified