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 361–370 of 483 papers

TitleStatusHype
Composition of Sentence Embeddings:Lessons from Statistical Relational Learning—0
Learning Relational Representations with Auto-encoding Logic Programs—0
Multi-Label Network Classification via Weighted Personalized Factorizations—0
MUREL: Multimodal Relational Reasoning for Visual Question AnsweringCode0
Variational Quantum Circuit Model for Knowledge Graphs Embedding—0
Graph Neural Networks with Generated Parameters for Relation ExtractionCode0
Interpretable preference learning: a game theoretic framework for large margin on-line feature and rule learningCode0
Spatial Knowledge Distillation to aid Visual Reasoning—0
Feed-Forward Neural Networks Need Inductive Bias to Learn Equality Relations—0
Chain of Reasoning for Visual Question Answering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified