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 211–220 of 483 papers

TitleStatusHype
Introducing DRAIL -- a Step Towards Declarative Deep Relational Learning—0
Interpretable Reinforcement Learning With Neural Symbolic Logic—0
Joint Modeling of Visual Objects and Relations for Scene Graph Generation—0
KeLP at SemEval-2017 Task 3: Learning Pairwise Patterns in Community Question Answering—0
Deep reinforcement learning with relational inductive biases—0
kLogNLP: Graph Kernel--based Relational Learning of Natural Language—0
Benchmarking Systematic Relational Reasoning with Large Language and Reasoning Models—0
Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection—0
A Concept-Centered Hypertext Approach to Case-Based Retrieval—0
Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified