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 461–470 of 483 papers

TitleStatusHype
Learning Relational Representations with Auto-encoding Logic Programs—0
Relational Reasoning Networks—0
Learning Structured Embeddings of Knowledge Graphs with Adversarial Learning Framework—0
Learning to Coarsen Graphs with Graph Neural Networks—0
Learning to ``Read Between the Lines'' using Bayesian Logic Programs—0
Learning to Solve NLP Tasks in an Incremental Number of Languages—0
Lifted Inference with Linear Order Axiom—0
Lifted Relational Neural Networks—0
Lifted Symmetry Detection and Breaking for MAP Inference—0
LinkNBed: Multi-Graph Representation Learning with Entity Linkage—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified