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 151–175 of 483 papers

TitleStatusHype
Concept Representation Learning with Contrastive Self-Supervised Learning—0
A Study of Compositional Generalization in Neural Models—0
Imagine, Reason and Write: Visual Storytelling with Graph Knowledge and Relational Reasoning—0
Deep Message Passing on Sets—0
A Statistical Relational Learning Approach to Identifying Evidence Based Medicine Categories—0
Hypergraph-based Motion Generation with Multi-modal Interaction Relational Reasoning—0
Graph-based Facial Affect Analysis: A Review—0
Composition of Sentence Embeddings:Lessons from Statistical Relational Learning—0
Composition of Sentence Embeddings: Lessons from Statistical Relational Learning—0
A Learning Algorithm for Relational Logistic Regression: Preliminary Results—0
Abstraction and Relational learning—0
Hyperlink Regression via Bregman Divergence—0
Important Object Identification with Semi-Supervised Learning for Autonomous Driving—0
Graph-based Kinship Reasoning Network—0
Graph-Based Knowledge Integration for Question Answering over Dialogue—0
Computing Marginal Distributions over Continuous Markov Networks for Statistical Relational Learning—0
Improving Generalization for Abstract Reasoning Tasks Using Disentangled Feature Representations—0
Graph Collaborative Reasoning—0
Geometric Relational Embeddings: A Survey—0
Genesis: Towards the Automation of Systems Biology Research—0
Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text—0
Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective—0
Constellation: Learning relational abstractions over objects for compositional imagination—0
Graph-to-Vision: Multi-graph Understanding and Reasoning using Vision-Language Models—0
Agent-Centric Representations for Multi-Agent Reinforcement Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified