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 451–475 of 483 papers

TitleStatusHype
Optimal quadratic binding for relational reasoning in vector symbolic neural architecturesCode0
ORCHARD: A Benchmark For Measuring Systematic Generalization of Multi-Hierarchical ReasoningCode0
Interaction Relational Network for Mutual Action RecognitionCode0
PARN: Position-Aware Relation Networks for Few-Shot LearningCode0
An Insect-Inspired Randomly, Weighted Neural Network with Random Fourier Features For Neuro-Symbolic Relational LearningCode0
Inferring Scientific Cross-Document Coreference and Hierarchy with Definition-Augmented Relational ReasoningCode0
In-Context Analogical Reasoning with Pre-Trained Language ModelsCode0
Disentangling and Integrating Relational and Sensory Information in Transformer ArchitecturesCode0
Robust Face Tracking using Multiple Appearance Models and Graph Relational LearningCode0
Coresets for Relational Data and The ApplicationsCode0
RuDaS: Synthetic Datasets for Rule Learning and Evaluation ToolsCode0
Principled and Efficient Motif Finding for Structure Learning of Lifted Graphical ModelsCode0
Privately Learning from Graphs with Applications in Fine-tuning Large Language ModelsCode0
Probing Cross-Modal Representations in Multi-Step Relational ReasoningCode0
Compositional Language Understanding with Text-based Relational ReasoningCode0
Propagation on Multi-relational Graphs for Node RegressionCode0
Propositionalization and Embeddings: Two Sides of the Same CoinCode0
Identifying Morality Frames in Political Tweets using Relational LearningCode0
An Explicitly Relational Neural Network ArchitectureCode0
Holographic Embeddings of Knowledge GraphsCode0
R5: Rule Discovery with Reinforced and Recurrent Relational ReasoningCode0
Adversarial Sets for Regularising Neural Link PredictorsCode0
Associative TransformerCode0
Scalable Label Propagation for Multi-relational Learning on the Tensor Product of GraphsCode0
Complementary Structure-Learning Neural Networks for Relational ReasoningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified