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

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Showing 101–150 of 483 papers

TitleStatusHype
Deep reinforcement learning with relational inductive biases—0
Benchmarking Systematic Relational Reasoning with Large Language and Reasoning Models—0
A Concept-Centered Hypertext Approach to Case-Based Retrieval—0
Graph Convolutional Networks from the Perspective of Sheaves and the Neural Tangent Kernel—0
Deep Learning for Ontology Reasoning—0
Deep Sets for Generalization in RL—0
Demystifying Relational Latent Representations—0
Desk Organization: Effect of Multimodal Inputs on Spatial Relational Learning—0
DEVICE: DEpth and VIsual ConcEpts Aware Transformer for TextCaps—0
Differentiable Parsing and Visual Grounding of Natural Language Instructions for Object Placement—0
Deep Inductive Logic Reasoning for Multi-Hop Reading Comprehension—0
Analysis of Nuanced Stances and Sentiment Towards Entities of US Politicians through the Lens of Moral Foundation Theory—0
Boosting Neural Language Inference via Cascaded Interactive Reasoning—0
Distilling Structured Knowledge for Text-Based Relational Reasoning—0
Distributed Associative Memory Network with Association Reinforcing Loss—0
A Novel Neural-symbolic System under Statistical Relational Learning—0
Does Entity Abstraction Help Generative Transformers Reason?—0
Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation—0
Domain-Liftability of Relational Marginal Polytopes—0
Broadcasting Convolutional Network for Visual Relational Reasoning—0
Deep Generative Models for Ultra-High Granularity Particle Physics Detector Simulation: A Voyage From Emulation to Extrapolation—0
Decoupling Mixture-of-Graphs: Unseen Relational Learning for Knowledge Graph Completion by Fusing Ontology and Textual Experts—0
A Complete Characterization of Projectivity for Statistical Relational Models—0
Effective Learning of Probabilistic Models for Clinical Predictions from Longitudinal Data—0
Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects—0
Efficient Inference and Learning in a Large Knowledge Base: Reasoning with Extracted Information using a Locally Groundable First-Order Probabilistic Logic—0
Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation—0
Cross-Graph Learning of Multi-Relational Associations—0
Cross Chest Graph for Disease Diagnosis with Structural Relational Reasoning—0
Usable & Scalable Learning Over Relational Data With Automatic Language Bias—0
Analogical and Relational Reasoning with Spiking Neural Networks—0
Graph Collaborative Reasoning—0
Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning—0
A More Compact Object Detector Head Network with Feature Enhancement and Relational Reasoning—0
A Modern Take on Visual Relationship Reasoning for Grasp Planning—0
ConvKN at SemEval-2016 Task 3: Answer and Question Selection for Question Answering on Arabic and English Fora—0
Contrastive Feature Induction for Efficient Structure Learning of Conditional Random Fields—0
A Universal Model for Cross Modality Mapping by Relational Reasoning—0
Graph-based Facial Affect Analysis: A Review—0
Continual Reasoning: Non-Monotonic Reasoning in Neurosymbolic AI using Continual Learning—0
Context Aware Group Activity Recognition—0
Attention on Abstract Visual Reasoning—0
Content+Context=Classification: Examining the Roles of Social Interactions and Linguist Content in Twitter User Classification—0
Construction and Elicitation of a Black Box Model in the Game of Bridge—0
A Three-Way Model for Collective Learning on Multi-Relational Data—0
Graph-based Kinship Reasoning Network—0
Constellation: Learning relational abstractions over objects for compositional imagination—0
Conditional Hybrid GAN for Sequence Generation—0
Concept Representation Learning with Contrastive Self-Supervised Learning—0
A Study of Compositional Generalization in Neural Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified