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 151175 of 483 papers

TitleStatusHype
Lifted Inference beyond First-Order LogicCode0
Mandolin: A Knowledge Discovery Framework for the Web of DataCode0
MUREL: Multimodal Relational Reasoning for Visual Question AnsweringCode0
Column Networks for Collective ClassificationCode0
Learning the meanings of function words from grounded language using a visual question answering modelCode0
Explaining Local, Global, And Higher-Order Interactions In Deep LearningCode0
A simple neural network module for relational reasoningCode0
Knowledge Graph Completion via Complex Tensor FactorizationCode0
Language-Conditioned Graph Networks for Relational ReasoningCode0
Cognitive Knowledge Graph Reasoning for One-shot Relational LearningCode0
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge GraphsCode0
Object-Oriented Dynamics Learning through Multi-Level AbstractionCode0
Large Class Separation is not what you need for Relational Reasoning-based OOD DetectionCode0
Graph-Based Global Reasoning NetworksCode0
Differentially Private Relational Learning with Entity-level Privacy GuaranteesCode0
Graph Based Relational Features for Collective ClassificationCode0
Inferring Scientific Cross-Document Coreference and Hierarchy with Definition-Augmented Relational ReasoningCode0
Interaction Relational Network for Mutual Action RecognitionCode0
Holographic Embeddings of Knowledge GraphsCode0
Identifying Morality Frames in Political Tweets using Relational LearningCode0
Generalization of CNNs on Relational Reasoning with Bar ChartsCode0
In-Context Analogical Reasoning with Pre-Trained Language ModelsCode0
Graph Neural Networks with Generated Parameters for Relation ExtractionCode0
Privately Learning from Graphs with Applications in Fine-tuning Large Language ModelsCode0
Interpretable preference learning: a game theoretic framework for large margin on-line feature and rule learningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99Unverified