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

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Showing 231–240 of 483 papers

TitleStatusHype
Deep Learning for Ontology Reasoning—0
Deep Inductive Logic Reasoning for Multi-Hop Reading Comprehension—0
Deep Generative Models for Ultra-High Granularity Particle Physics Detector Simulation: A Voyage From Emulation to Extrapolation—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
Inductive Logic Boosting—0
Induction of Interpretable Possibilistic Logic Theories from Relational Data—0
Decoupling Mixture-of-Graphs: Unseen Relational Learning for Knowledge Graph Completion by Fusing Ontology and Textual Experts—0
Analysis of Nuanced Stances and Sentiment Towards Entities of US Politicians through the Lens of Moral Foundation Theory—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified