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
Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection—0
Knowledge Graph Embedding via Graph Attenuated Attention Networks—0
Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification—0
Learning Generative Models across Incomparable Spaces—0
Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction—0
Learning Over Dirty Data Without Cleaning—0
Learning Probabilistic Logic Programs in Continuous Domains—0
Learning Probabilistic Temporal Safety Properties from Examples in Relational Domains—0
Learning Reasoning Patterns for Relational Triple Extraction with Mutual Generation of Text and Graph—0
Learning Relational Features with Backward Random Walks—0
Learning Relational Representations with Auto-encoding Logic Programs—0
Relational Reasoning Networks—0
Learning Structured Embeddings of Knowledge Graphs with Adversarial Learning Framework—0
Learning to Coarsen Graphs with Graph Neural Networks—0
Learning to ``Read Between the Lines'' using Bayesian Logic Programs—0
Learning to Solve NLP Tasks in an Incremental Number of Languages—0
Lifted Inference with Linear Order Axiom—0
Lifted Relational Neural Networks—0
Lifted Symmetry Detection and Breaking for MAP Inference—0
LinkNBed: Multi-Graph Representation Learning with Entity Linkage—0
LLMs for Relational Reasoning: How Far are We?—0
Locally Boosted Graph Aggregation for Community Detection—0
Local Region Perception and Relationship Learning Combined with Feature Fusion for Facial Action Unit Detection—0
Logistic Tensor Factorization for Multi-Relational Data—0
Look Around for Anomalies: Weakly-Supervised Anomaly Detection via Context-Motion Relational Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified