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
Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation—0
Multi-choice Relational Reasoning for Machine Reading Comprehension—0
Multi-Label Network Classification via Weighted Personalized Factorizations—0
Multi-layer Relation Networks—0
Multiple protein feature prediction with statistical relational learning—0
Multi-Relational Learning at Scale with ADMM—0
Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss—0
Multi-Scale Progressive Attention Network for Video Question Answering—0
Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network—0
Multi-task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs—0
Show:102550
← PrevPage 37 of 49Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified