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 281–290 of 483 papers

TitleStatusHype
Recurrent Relational Memory Network for Unsupervised Image Captioning—0
A Study of Compositional Generalization in Neural Models—0
Extrapolatable Relational Reasoning With Comparators in Low-Dimensional Manifolds—0
Symbolic Logic meets Machine Learning: A Brief Survey in Infinite Domains—0
Relational reasoning and generalization using non-symbolic neural networksCode0
Explaining Local, Global, And Higher-Order Interactions In Deep LearningCode0
Propositionalization and Embeddings: Two Sides of the Same CoinCode0
Relational Learning Analysis of Social Politics using Knowledge Graph Embedding—0
Randomly Weighted, Untrained Neural Tensor Networks Achieve Greater Relational Expressiveness—0
RelEx: A Model-Agnostic Relational Model Explainer—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified