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 221–230 of 483 papers

TitleStatusHype
Deep reinforcement learning with relational inductive biases—0
Benchmarking Systematic Relational Reasoning with Large Language and Reasoning Models—0
A Concept-Centered Hypertext Approach to Case-Based Retrieval—0
Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation—0
Deep Learning for Ontology Reasoning—0
Learning Generative Models across Incomparable Spaces—0
Deep Inductive Logic Reasoning for Multi-Hop Reading Comprehension—0
Learning Over Dirty Data Without Cleaning—0
Deep Generative Models for Ultra-High Granularity Particle Physics Detector Simulation: A Voyage From Emulation to Extrapolation—0
Inductive Logic Boosting—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified