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 151–160 of 483 papers

TitleStatusHype
Concept Representation Learning with Contrastive Self-Supervised Learning—0
A Study of Compositional Generalization in Neural Models—0
A logic-based relational learning approach to relation extraction: The OntoILPER system—0
Computing Marginal Distributions over Continuous Markov Networks for Statistical Relational Learning—0
A Statistical Relational Learning Approach to Identifying Evidence Based Medicine Categories—0
Composition of Sentence Embeddings:Lessons from Statistical Relational Learning—0
Composition of Sentence Embeddings: Lessons from Statistical Relational Learning—0
A Learning Algorithm for Relational Logistic Regression: Preliminary Results—0
Abstraction and Relational learning—0
Deep Message Passing on Sets—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified