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 301–350 of 483 papers

TitleStatusHype
Estimating Aggregate Properties In Relational Networks With Unobserved Data—0
Domain-Liftability of Relational Marginal Polytopes—0
A logic-based relational learning approach to relation extraction: The OntoILPER system—0
A Preliminary Approach for Learning Relational Policies for the Management of Critically Ill Children—0
Generative Adversarial Zero-Shot Relational Learning for Knowledge GraphsCode1
Facial Action Unit Detection via Adaptive Attention and Relation—0
User Profiling Using Hinge-loss Markov Random Fields—0
Improving End-to-End Object Tracking Using Relational Reasoning—0
Knowledge Graph Embedding via Graph Attenuated Attention Networks—0
Relational Mimic for Visual Adversarial Imitation Learning—0
Towards Omni-Supervised Face Alignment for Large Scale Unlabeled Videos—0
Quantum Embedding of Knowledge for ReasoningCode1
SAG-VAE: End-to-end Joint Inference of Data Representations and Feature Relations—0
Transfer Learning in Visual and Relational Reasoning—0
SCR-Graph: Spatial-Causal Relationships based Graph Reasoning Network for Human Action Prediction—0
Inductive Relation Prediction by Subgraph ReasoningCode1
Attention on Abstract Visual Reasoning—0
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense ReasoningCode0
Interaction Relational Network for Mutual Action RecognitionCode0
TransGCN:Coupling Transformation Assumptions with Graph Convolutional Networks for Link Prediction—0
Compensating Supervision Incompleteness with Prior Knowledge in Semantic Image InterpretationCode1
Relational Learning for Joint Head and Human DetectionCode0
Deep Message Passing on Sets—0
RuDaS: Synthetic Datasets for Rule Learning and Evaluation ToolsCode0
PARN: Position-Aware Relation Networks for Few-Shot LearningCode0
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