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 251–260 of 483 papers

TitleStatusHype
Regularized Orthogonal Tensor Decompositions for Multi-Relational Learning—0
Relational Algorithms for k-means Clustering—0
Relational-Grid-World: A Novel Relational Reasoning Environment and An Agent Model for Relational Information Extraction—0
Relational Learning Analysis of Social Politics using Knowledge Graph Embedding—0
Relational Learning and Feature Extraction by Querying over Heterogeneous Information Networks—0
Relational Learning between Multiple Pulmonary Nodules via Deep Set Attention Transformers—0
Relational Learning for Skill Preconditions—0
Relational Learning in Pre-Trained Models: A Theory from Hypergraph Recovery Perspective—0
Relational Learning with Variational Bayes—0
Relational Mimic for Visual Adversarial Imitation Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified