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 251–275 of 483 papers

TitleStatusHype
Regularized Orthogonal Tensor Decompositions for Multi-Relational Learning—0
Relational Algorithms for k-means Clustering—0
Relational-Grid-World: A Novel Relational Reasoning Environment and An Agent Model for Relational Information Extraction—0
Relational Learning Analysis of Social Politics using Knowledge Graph Embedding—0
Relational Learning and Feature Extraction by Querying over Heterogeneous Information Networks—0
Relational Learning between Multiple Pulmonary Nodules via Deep Set Attention Transformers—0
Relational Learning for Skill Preconditions—0
Relational Learning in Pre-Trained Models: A Theory from Hypergraph Recovery Perspective—0
Relational Learning with Variational Bayes—0
Relational Mimic for Visual Adversarial Imitation Learning—0
Relational Neural Markov Random Fields—0
Relational Reasoning Network (RRN) for Anatomical Landmarking—0
Behavior-Inspired Neural Networks for Relational Inference—0
Relational Reasoning Over Spatial-Temporal Graphs for Video Summarization—0
Relational Reasoning using Prior Knowledge for Visual Captioning—0
Relational Reasoning via Set Transformers: Provable Efficiency and Applications to MARL—0
Relational Similarity Machines—0
Relation Matters: Foreground-aware Graph-based Relational Reasoning for Domain Adaptive Object Detection—0
RelEx: A Model-Agnostic Relational Model Explainer—0
RelNet: End-to-End Modeling of Entities & Relations—0
RelTextRank: An Open Source Framework for Building Relational Syntactic-Semantic Text Pair Representations—0
Representational Alignment with Chemical Induced Fit for Molecular Relational Learning—0
Retrieval-based Knowledge Augmented Vision Language Pre-training—0
ReVoLT: Relational Reasoning and Voronoi Local Graph Planning for Target-driven Navigation—0
SAG-VAE: End-to-end Joint Inference of Data Representations and Feature Relations—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified