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 411–420 of 483 papers

TitleStatusHype
RelTextRank: An Open Source Framework for Building Relational Syntactic-Semantic Text Pair Representations—0
Robust Face Tracking using Multiple Appearance Models and Graph Relational LearningCode0
RelNet: End-to-End Modeling of Entities & Relations—0
A simple neural network module for relational reasoningCode0
Deep Learning for Ontology Reasoning—0
Logic Tensor Networks for Semantic Image InterpretationCode0
Induction of Interpretable Possibilistic Logic Theories from Relational Data—0
Online learnability of Statistical Relational Learning in anomaly detection—0
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge GraphsCode0
Demystifying Relational Latent Representations—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified