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 431–440 of 483 papers

TitleStatusHype
Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text—0
Application of Statistical Relational Learning to Hybrid Recommendation Systems—0
On the Semantic Relationship between Probabilistic Soft Logic and Markov Logic—0
A Learning Algorithm for Relational Logistic Regression: Preliminary Results—0
Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation—0
ConvKN at SemEval-2016 Task 3: Answer and Question Selection for Question Answering on Arabic and English Fora—0
Scalable Statistical Relational Learning for NLP—0
Cross-Graph Learning of Multi-Relational Associations—0
Multi-Relational Learning at Scale with ADMM—0
Regularized Orthogonal Tensor Decompositions for Multi-Relational Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified