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 381–390 of 483 papers

TitleStatusHype
Learning Probabilistic Logic Programs in Continuous Domains—0
A Comparative Study of Distributional and Symbolic Paradigms for Relational LearningCode0
Modularity Matters: Learning Invariant Relational Reasoning Tasks—0
Relational recurrent neural networksCode0
Relational Deep Reinforcement LearningCode0
Relational inductive biases, deep learning, and graph networksCode1
Tensorize, Factorize and Regularize: Robust Visual Relationship Learning—0
Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module—0
VC-Dimension Based Generalization Bounds for Relational Learning—0
Semi-Supervised Online Structure Learning for Composite Event RecognitionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified