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 426–450 of 483 papers

TitleStatusHype
HR-RCNN: Hierarchical Relational Reasoning for Object Detection—0
Hybrid SRL with Optimization Modulo Theories—0
Hyperbolic Manifold Regression—0
Hypergraph-based Motion Generation with Multi-modal Interaction Relational Reasoning—0
Hyperlink Regression via Bregman Divergence—0
Imagine, Reason and Write: Visual Storytelling with Graph Knowledge and Relational Reasoning—0
Important Object Identification with Semi-Supervised Learning for Autonomous Driving—0
Improving Composition of Sentence Embeddings through the Lens of Statistical Relational Learning—0
Improving End-to-End Object Tracking Using Relational Reasoning—0
Improving Generalization for Abstract Reasoning Tasks Using Disentangled Feature Representations—0
Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling—0
Improving Scene Graph Classification by Exploiting Knowledge from Texts—0
Incorporating Relational Background Knowledge into Reinforcement Learning via Differentiable Inductive Logic Programming—0
Induction of Interpretable Possibilistic Logic Theories from Relational Data—0
Inductive Logic Boosting—0
Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation—0
Interpretable Reinforcement Learning With Neural Symbolic Logic—0
Introducing DRAIL -- a Step Towards Declarative Deep Relational Learning—0
Two pathways to resolve relational inconsistencies—0
Joint Information Extraction and Reasoning: A Scalable Statistical Relational Learning Approach—0
Jointly Extracting Explicit and Implicit Relational Triples with Reasoning Pattern Enhanced Binary Pointer Network—0
Joint Modeling of Visual Objects and Relations for Scene Graph Generation—0
KeLP at SemEval-2017 Task 3: Learning Pairwise Patterns in Community Question Answering—0
kLog: A Language for Logical and Relational Learning with Kernels—0
kLogNLP: Graph Kernel--based Relational Learning of Natural Language—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified