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 281–290 of 483 papers

TitleStatusHype
Graph-based Kinship Reasoning Network—0
Learning Structured Embeddings of Knowledge Graphs with Adversarial Learning Framework—0
Relational Learning between Multiple Pulmonary Nodules via Deep Set Attention Transformers—0
Causal Relational Learning—0
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
Deep Relational Reasoning Graph Network for Arbitrary Shape Text DetectionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified