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 251–275 of 483 papers

TitleStatusHype
Skeleton-based Relational Reasoning for Group Activity Analysis—0
SkILL - a Stochastic Inductive Logic Learner—0
Slice Normalized Dynamic Markov Logic Networks—0
Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network—0
SOLD: Slot Object-Centric Latent Dynamics Models for Relational Manipulation Learning from Pixels—0
Solving Raven's Progressive Matrices with Multi-Layer Relation Networks—0
Sparse Relational Reasoning with Object-Centric Representations—0
Spatial Knowledge Distillation to aid Visual Reasoning—0
Spatially Invariant Unsupervised 3D Object-Centric Learning and Scene Decomposition—0
Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction—0
Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking—0
Facial Action Unit Detection via Adaptive Attention and Relation—0
SQL for SRL: Structure Learning Inside a Database System—0
Deep Message Passing on Sets—0
Probabilities of the Third Type: Statistical Relational Learning and Reasoning with Relative Frequencies—0
Statistical relational learning and neuro-symbolic AI: what does first-order logic offer?—0
Stochastic And-Or Grammars: A Unified Framework and Logic Perspective—0
Stochastic Gradient Descent for Relational Logistic Regression via Partial Network Crawls—0
StrokeNet: Stroke Assisted and Hierarchical Graph Reasoning Networks—0
Struct-MMSB: Mixed Membership Stochastic Blockmodels with Interpretable Structured Priors—0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified