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 51–60 of 483 papers

TitleStatusHype
Neural Logic MachinesCode1
Beyond Graph Neural Networks with Lifted Relational Neural NetworksCode1
Few-shot Relational Reasoning via Connection Subgraph PretrainingCode1
Pix2Code: Learning to Compose Neural Visual Concepts as ProgramsCode1
3D Interaction Geometric Pre-training for Molecular Relational LearningCode1
Fusing Context Into Knowledge Graph for Commonsense Question AnsweringCode1
Generative Adversarial Zero-Shot Relational Learning for Knowledge GraphsCode1
Generative 3D Part Assembly via Dynamic Graph LearningCode1
Redundancy-Free Self-Supervised Relational Learning for Graph ClusteringCode1
Heterogeneous Graph Contrastive Learning for RecommendationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified