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 326–350 of 483 papers

TitleStatusHype
Relationships from Entity StreamCode0
Meta Relational Learning for Few-Shot Link Prediction in Knowledge GraphsCode0
The Impact of Semantic Linguistic Features in Relation Extraction: A Logical Relational Learning Approach—0
CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from TextCode1
Hyperlink Regression via Bregman Divergence—0
Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling—0
Relation Network for Multi-label Aerial Image ClassificationCode0
End-to-end Recurrent Multi-Object Tracking and Trajectory Prediction with Relational Reasoning—0
Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems—0
Cognitive Knowledge Graph Reasoning for One-shot Relational LearningCode0
WikiDataSets: Standardized sub-graphs from WikidataCode0
Relational Reasoning using Prior Knowledge for Visual Captioning—0
Adaptive Convolution for Multi-Relational Learning—0
Composition of Sentence Embeddings: Lessons from Statistical Relational Learning—0
Structural Relational Reasoning of Point Clouds—0
Neural Markov Logic Networks—0
MDE: Multiple Distance Embeddings for Link Prediction in Knowledge GraphsCode0
An Explicitly Relational Neural Network ArchitectureCode0
Neural-Symbolic Argumentation Mining: an Argument in Favor of Deep Learning and Reasoning—0
GMNN: Graph Markov Neural NetworksCode0
Learning Generative Models across Incomparable Spaces—0
The relational processing limits of classic and contemporary neural network models of language processingCode0
Language-Conditioned Graph Networks for Relational ReasoningCode0
Deep reinforcement learning with relational inductive biases—0
Improving Composition of Sentence Embeddings through the Lens of Statistical Relational Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified