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 371–380 of 483 papers

TitleStatusHype
Neural Markov Logic Networks—0
Neural-Symbolic Argumentation Mining: an Argument in Favor of Deep Learning and Reasoning—0
Neural-Symbolic Relational Reasoning on Graph Models: Effective Link Inference and Computation from Knowledge Bases—0
Numeric Input Relations for Relational Learning with Applications to Community Structure Analysis—0
Object-Centric Representation Learning for Video Question Answering—0
Object-Oriented Model Learning through Multi-Level Abstraction—0
On Lifting the Gibbs Sampling Algorithm—0
Online learnability of Statistical Relational Learning in anomaly detection—0
On the Semantic Relationship between Probabilistic Soft Logic and Markov Logic—0
Path-of-Thoughts: Extracting and Following Paths for Robust Relational Reasoning with Large Language Models—0
Show:102550
← PrevPage 38 of 49Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified