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 301–325 of 483 papers

TitleStatusHype
Learning Over Dirty Data Without Cleaning—0
EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning—0
Solving Raven's Progressive Matrices with Multi-Layer Relation Networks—0
Incorporating Relational Background Knowledge into Reinforcement Learning via Differentiable Inductive Logic Programming—0
Deep Sets for Generalization in RL—0
Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning—0
Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective—0
Symbolic Learning and Reasoning with Noisy Data for Probabilistic Anchoring—0
Struct-MMSB: Mixed Membership Stochastic Blockmodels with Interpretable Structured Priors—0
Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network—0
Evaluating the Progress of Deep Learning for Visual Relational Concepts—0
Estimating Aggregate Properties In Relational Networks With Unobserved Data—0
Domain-Liftability of Relational Marginal Polytopes—0
A logic-based relational learning approach to relation extraction: The OntoILPER system—0
A Preliminary Approach for Learning Relational Policies for the Management of Critically Ill Children—0
Facial Action Unit Detection via Adaptive Attention and Relation—0
User Profiling Using Hinge-loss Markov Random Fields—0
Improving End-to-End Object Tracking Using Relational Reasoning—0
Knowledge Graph Embedding via Graph Attenuated Attention Networks—0
Relational Mimic for Visual Adversarial Imitation Learning—0
Towards Omni-Supervised Face Alignment for Large Scale Unlabeled Videos—0
SAG-VAE: End-to-end Joint Inference of Data Representations and Feature Relations—0
Transfer Learning in Visual and Relational Reasoning—0
SCR-Graph: Spatial-Causal Relationships based Graph Reasoning Network for Human Action Prediction—0
Attention on Abstract Visual Reasoning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified