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 351–400 of 483 papers

TitleStatusHype
Object-Oriented Model Learning through Multi-Level Abstraction—0
Neural Logic MachinesCode1
Object-Oriented Dynamics Learning through Multi-Level AbstractionCode0
A Relation-Augmented Fully Convolutional Network for Semantic Segmentation in Aerial Scenes—0
Relational Reasoning Network (RRN) for Anatomical Landmarking—0
Composition of Sentence Embeddings:Lessons from Statistical Relational Learning—0
Learning Relational Representations with Auto-encoding Logic Programs—0
Fast Graph Representation Learning with PyTorch GeometricCode1
Multi-Label Network Classification via Weighted Personalized Factorizations—0
MUREL: Multimodal Relational Reasoning for Visual Question AnsweringCode0
Variational Quantum Circuit Model for Knowledge Graphs Embedding—0
Graph Neural Networks with Generated Parameters for Relation ExtractionCode0
Interpretable preference learning: a game theoretic framework for large margin on-line feature and rule learningCode0
Spatial Knowledge Distillation to aid Visual Reasoning—0
Feed-Forward Neural Networks Need Inductive Bias to Learn Equality Relations—0
Chain of Reasoning for Visual Question Answering—0
Graph-Based Global Reasoning NetworksCode0
Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects—0
A Concept-Centered Hypertext Approach to Case-Based Retrieval—0
Improving Generalization for Abstract Reasoning Tasks Using Disentangled Feature Representations—0
Compositional Language Understanding with Text-based Relational ReasoningCode0
Multi-layer Relation Networks—0
Effective Learning of Probabilistic Models for Clinical Predictions from Longitudinal Data—0
Dilated DenseNets for Relational Reasoning—0
SARN: Relational Reasoning through Sequential Attention—0
Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network—0
The Visual QA Devil in the Details: The Impact of Early Fusion and Batch Norm on CLEVR—0
Mapping Natural Language Commands to Web ElementsCode0
One-Shot Relational Learning for Knowledge GraphsCode0
LinkNBed: Multi-Graph Representation Learning with Entity Linkage—0
Learning Probabilistic Logic Programs in Continuous Domains—0
A Comparative Study of Distributional and Symbolic Paradigms for Relational LearningCode0
Modularity Matters: Learning Invariant Relational Reasoning Tasks—0
Relational recurrent neural networksCode0
Relational Deep Reinforcement LearningCode0
Relational inductive biases, deep learning, and graph networksCode1
Tensorize, Factorize and Regularize: Robust Visual Relationship Learning—0
Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module—0
VC-Dimension Based Generalization Bounds for Relational Learning—0
Semi-Supervised Online Structure Learning for Composite Event RecognitionCode1
Scalable Label Propagation for Multi-relational Learning on the Tensor Product of GraphsCode0
TransRev: Modeling Reviews as Translations from Users to Items—0
Finding ReMO (Related Memory Object): A Simple Neural Architecture for Text based Reasoning—0
Active Learning of Strict Partial Orders: A Case Study on Concept Prerequisite Relations—0
Recurrent Relational Networks for complex relational reasoningCode0
RelNN: A Deep Neural Model for Relational LearningCode0
Broadcasting Convolutional Network for Visual Relational Reasoning—0
Temporal Relational Reasoning in VideosCode0
Recurrent Relational NetworksCode0
Tensor Decompositions for Modeling Inverse Dynamics—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified