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 76–100 of 483 papers

TitleStatusHype
Associative TransformerCode0
A Novel Neural-symbolic System under Statistical Relational Learning—0
Quantifying and Attributing the Hallucination of Large Language Models via Association Analysis—0
Redundancy-Free Self-Supervised Relational Learning for Graph ClusteringCode1
Reconstructing Groups of People with Hypergraph Relational ReasoningCode1
Lifted Inference beyond First-Order LogicCode0
RLIPv2: Fast Scaling of Relational Language-Image Pre-trainingCode1
Learning the meanings of function words from grounded language using a visual question answering modelCode0
CommonsenseVIS: Visualizing and Understanding Commonsense Reasoning Capabilities of Natural Language Models—0
LightPath: Lightweight and Scalable Path Representation LearningCode0
Anticipating Technical Expertise and Capability Evolution in Research Communities using Dynamic Graph TransformersCode0
Large Class Separation is not what you need for Relational Reasoning-based OOD DetectionCode0
A Multi-Task Perspective for Link Prediction with New Relation Types and Nodes—0
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of ThoughtCode2
Statistical relational learning and neuro-symbolic AI: what does first-order logic offer?—0
Shift-Robust Molecular Relational Learning with Causal SubstructureCode1
In-Context Analogical Reasoning with Pre-Trained Language ModelsCode0
Modularized Zero-shot VQA with Pre-trained ModelsCode0
Visual Causal Scene Refinement for Video Question AnsweringCode3
Continual Reasoning: Non-Monotonic Reasoning in Neurosymbolic AI using Continual Learning—0
Conditional Graph Information Bottleneck for Molecular Relational LearningCode1
Cluster Flow: how a hierarchical clustering layer make allows deep-NNs more resilient to hacking, more human-like and easily implements relational reasoning—0
Retrieval-based Knowledge Augmented Vision Language Pre-training—0
Geometric Relational Embeddings: A Survey—0
Relational Context Learning for Human-Object Interaction DetectionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified