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 291–300 of 483 papers

TitleStatusHype
Evaluating Logical Generalization in Graph Neural NetworksCode1
Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning—0
Better Set Representations For Relational ReasoningCode1
Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective—0
Symbolic Learning and Reasoning with Noisy Data for Probabilistic Anchoring—0
Struct-MMSB: Mixed Membership Stochastic Blockmodels with Interpretable Structured Priors—0
Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network—0
Self-Attentive Associative MemoryCode1
A Survey on Knowledge Graphs: Representation, Acquisition and ApplicationsCode2
Evaluating the Progress of Deep Learning for Visual Relational Concepts—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified