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 276–300 of 483 papers

TitleStatusHype
Neural-Symbolic Relational Reasoning on Graph Models: Effective Link Inference and Computation from Knowledge Bases—0
Construction and Elicitation of a Black Box Model in the Game of Bridge—0
Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question AnsweringCode1
Machine Number Sense: A Dataset of Visual Arithmetic Problems for Abstract and Relational ReasoningCode1
A Complete Characterization of Projectivity for Statistical Relational Models—0
Graph-based Kinship Reasoning Network—0
Learning Structured Embeddings of Knowledge Graphs with Adversarial Learning Framework—0
Relational Learning between Multiple Pulmonary Nodules via Deep Set Attention Transformers—0
Causal Relational Learning—0
Learning Over Dirty Data Without Cleaning—0
EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning—0
Solving Raven's Progressive Matrices with Multi-Layer Relation Networks—0
Incorporating Relational Background Knowledge into Reinforcement Learning via Differentiable Inductive Logic Programming—0
Deep Sets for Generalization in RL—0
Deep Relational Reasoning Graph Network for Arbitrary Shape Text DetectionCode1
Evaluating Logical Generalization in Graph Neural NetworksCode1
Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning—0
Better Set Representations For Relational ReasoningCode1
Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective—0
Symbolic Learning and Reasoning with Noisy Data for Probabilistic Anchoring—0
Struct-MMSB: Mixed Membership Stochastic Blockmodels with Interpretable Structured Priors—0
Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network—0
Self-Attentive Associative MemoryCode1
A Survey on Knowledge Graphs: Representation, Acquisition and ApplicationsCode2
Evaluating the Progress of Deep Learning for Visual Relational Concepts—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified