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 301–310 of 483 papers

TitleStatusHype
Learning Over Dirty Data Without Cleaning—0
EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning—0
Solving Raven's Progressive Matrices with Multi-Layer Relation Networks—0
Incorporating Relational Background Knowledge into Reinforcement Learning via Differentiable Inductive Logic Programming—0
Deep Sets for Generalization in RL—0
Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning—0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified