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 401–410 of 483 papers

TitleStatusHype
Recurrent Relational NetworksCode0
Tensor Decompositions for Modeling Inverse Dynamics—0
Mandolin: A Knowledge Discovery Framework for the Web of DataCode0
Deep Semantic Abstractions of Everyday Human Activities: On Commonsense Representations of Human Interactions—0
Usable & Scalable Learning Over Relational Data With Automatic Language Bias—0
On Inductive Abilities of Latent Factor Models for Relational LearningCode0
Character-based recurrent neural networks for morphological relational reasoning—0
Multi-task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs—0
KeLP at SemEval-2017 Task 3: Learning Pairwise Patterns in Community Question Answering—0
Relational Learning and Feature Extraction by Querying over Heterogeneous Information Networks—0
Show:102550
← PrevPage 41 of 49Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified