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

Showing 101–125 of 483 papers

TitleStatusHype
One-Shot Relational Learning for Knowledge GraphsCode0
Mandolin: A Knowledge Discovery Framework for the Web of DataCode0
Benchmarking and Understanding Compositional Relational Reasoning of LLMsCode0
Mapping Natural Language Commands to Web ElementsCode0
MDE: Multiple Distance Embeddings for Link Prediction in Knowledge GraphsCode0
Modeling Content and Context with Deep Relational LearningCode0
Lifted Inference beyond First-Order LogicCode0
MUREL: Multimodal Relational Reasoning for Visual Question AnsweringCode0
An Insect-Inspired Randomly, Weighted Neural Network with Random Fourier Features For Neuro-Symbolic Relational LearningCode0
Object-Oriented Dynamics Learning through Multi-Level AbstractionCode0
Leveraging Relational Information for Learning Weakly Disentangled RepresentationsCode0
LightPath: Lightweight and Scalable Path Representation LearningCode0
Disentangling and Integrating Relational and Sensory Information in Transformer ArchitecturesCode0
Breakpoint Transformers for Modeling and Tracking Intermediate BeliefsCode0
Bridging Generative and Discriminative Learning: Few-Shot Relation Extraction via Two-Stage Knowledge-Guided Pre-trainingCode0
Distributed Associative Memory Network with Memory Refreshing LossCode0
Logic Tensor Networks for Semantic Image InterpretationCode0
Meta Relational Learning for Few-Shot Link Prediction in Knowledge GraphsCode0
On Inductive Abilities of Latent Factor Models for Relational LearningCode0
Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation TypesCode0
Coresets for Relational Data and The ApplicationsCode0
Automatic Generation of Contrast Sets from Scene Graphs: Probing the Compositional Consistency of GQACode0
Large Class Separation is not what you need for Relational Reasoning-based OOD DetectionCode0
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge GraphsCode0
Knowledge Graph Completion via Complex Tensor FactorizationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified