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 271–280 of 483 papers

TitleStatusHype
Structural Relational Reasoning of Point Clouds—0
Structural Representations for Learning Relations between Pairs of Texts—0
Subverting machines, fluctuating identities: Re-learning human categorization—0
Swift Markov Logic for Probabilistic Reasoning on Knowledge Graphs—0
Symbolic Learning and Reasoning with Noisy Data for Probabilistic Anchoring—0
Symbolic Logic meets Machine Learning: A Brief Survey in Infinite Domains—0
Temporal Relational Reasoning of Large Language Models for Detecting Stock Portfolio Crashes—0
Tensor Decompositions for Modeling Inverse Dynamics—0
Tensorize, Factorize and Regularize: Robust Visual Relationship Learning—0
Text-Guided Coarse-to-Fine Fusion Network for Robust Remote Sensing Visual Question Answering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified