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 326–350 of 483 papers

TitleStatusHype
A Multi-Task Perspective for Link Prediction with New Relation Types and Nodes—0
A Novel Neural-symbolic System under Statistical Relational Learning—0
Application of Statistical Relational Learning to Hybrid Recommendation Systems—0
A Preliminary Approach for Learning Relational Policies for the Management of Critically Ill Children—0
A Probit Tensor Factorization Model For Relational Learning—0
Arbitrarily Applicable Same/Opposite Relational Responding with NARS—0
A Relational-learning Perspective to Multi-label Chest X-ray Classification—0
A Relation-Augmented Fully Convolutional Network for Semantic Segmentation in Aerial Scenes—0
A Semantic Matching Energy Function for Learning with Multi-relational Data—0
A Statistical Relational Learning Approach to Identifying Evidence Based Medicine Categories—0
A Study of Compositional Generalization in Neural Models—0
A Three-Way Model for Collective Learning on Multi-Relational Data—0
Attention on Abstract Visual Reasoning—0
A Universal Model for Cross Modality Mapping by Relational Reasoning—0
Usable & Scalable Learning Over Relational Data With Automatic Language Bias—0
Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation—0
Benchmarking Systematic Relational Reasoning with Large Language and Reasoning Models—0
Boosting gets full Attention for Relational Learning—0
Boosting Neural Language Inference via Cascaded Interactive Reasoning—0
A Statistical Relational Approach to Learning Distance-based GCNs—0
Broadcasting Convolutional Network for Visual Relational Reasoning—0
Causal Relational Learning—0
Chain of Reasoning for Visual Question Answering—0
Character-based recurrent neural networks for morphological relational reasoning—0
Classification by Attention: Scene Graph Classification with Prior Knowledge—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified