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

Showing 176200 of 483 papers

TitleStatusHype
Interpretable preference learning: a game theoretic framework for large margin on-line feature and rule learningCode0
Large Class Separation is not what you need for Relational Reasoning-based OOD DetectionCode0
On Inductive Abilities of Latent Factor Models for Relational LearningCode0
Efficient Inference and Learning in a Large Knowledge Base: Reasoning with Extracted Information using a Locally Groundable First-Order Probabilistic Logic0
Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects0
Character-based recurrent neural networks for morphological relational reasoning0
Effective Learning of Probabilistic Models for Clinical Predictions from Longitudinal Data0
Chain of Reasoning for Visual Question Answering0
Application of Statistical Relational Learning to Hybrid Recommendation Systems0
Causal Relational Learning0
Domain-Liftability of Relational Marginal Polytopes0
Broadcasting Convolutional Network for Visual Relational Reasoning0
Active Learning of Strict Partial Orders: A Case Study on Concept Prerequisite Relations0
Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation0
Does Entity Abstraction Help Generative Transformers Reason?0
A Statistical Relational Approach to Learning Distance-based GCNs0
Distributed Associative Memory Network with Association Reinforcing Loss0
A Novel Neural-symbolic System under Statistical Relational Learning0
Distilling Structured Knowledge for Text-Based Relational Reasoning0
Discriminative Gaifman Models0
Dilated DenseNets for Relational Reasoning0
Boosting Neural Language Inference via Cascaded Interactive Reasoning0
A Multi-Task Perspective for Link Prediction with New Relation Types and Nodes0
A Critical Review of Inductive Logic Programming Techniques for Explainable AI0
Differentiable Parsing and Visual Grounding of Natural Language Instructions for Object Placement0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99Unverified