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 351–375 of 483 papers

TitleStatusHype
Metric-guided Distillation: Distilling Knowledge from the Metric to Ranker and Retriever for Generative Commonsense Reasoning—0
MGRR-Net: Multi-level Graph Relational Reasoning Network for Facial Action Units Detection—0
Mix and Reason: Reasoning over Semantic Topology with Data Mixing for Domain Generalization—0
Mixture-of-Graphs: Zero-shot Relational Learning for Knowledge Graph by Fusing Ontology and Textual Experts—0
MLAD: A Unified Model for Multi-system Log Anomaly Detection—0
Modelling Compositionality and Structure Dependence in Natural Language—0
Modular Graph Attention Network for Complex Visual Relational Reasoning—0
Modularity Matters: Learning Invariant Relational Reasoning Tasks—0
ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models—0
MoReL: Multi-omics Relational Learning—0
Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation—0
Multi-choice Relational Reasoning for Machine Reading Comprehension—0
Multi-Label Network Classification via Weighted Personalized Factorizations—0
Multi-layer Relation Networks—0
Multiple protein feature prediction with statistical relational learning—0
Multi-Relational Learning at Scale with ADMM—0
Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss—0
Multi-Scale Progressive Attention Network for Video Question Answering—0
Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network—0
Multi-task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs—0
Neural Markov Logic Networks—0
Neural-Symbolic Argumentation Mining: an Argument in Favor of Deep Learning and Reasoning—0
Neural-Symbolic Relational Reasoning on Graph Models: Effective Link Inference and Computation from Knowledge Bases—0
Numeric Input Relations for Relational Learning with Applications to Community Structure Analysis—0
Object-Centric Representation Learning for Video Question Answering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified