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 276–300 of 483 papers

TitleStatusHype
Symbolic Logic meets Machine Learning: A Brief Survey in Infinite Domains—0
Temporal Relational Reasoning of Large Language Models for Detecting Stock Portfolio Crashes—0
Tensor Decompositions for Modeling Inverse Dynamics—0
Tensorize, Factorize and Regularize: Robust Visual Relationship Learning—0
Text-Guided Coarse-to-Fine Fusion Network for Robust Remote Sensing Visual Question Answering—0
T-GAP: Learning to Walk across Time for Temporal Knowledge Graph Completion—0
The CTU Prague Relational Learning Repository—0
The Curious Case of Stacking Boosted Relational Dependency Networks—0
The Impact of Semantic Linguistic Features in Relation Extraction: A Logical Relational Learning Approach—0
Learning positional encodings in transformers depends on initialization—0
The Visual QA Devil in the Details: The Impact of Early Fusion and Batch Norm on CLEVR—0
Enhancing Large Language Models with Domain-Specific Knowledge: The Case in Topological Materials—0
Towards Abstract Relational Learning in Human Robot Interaction—0
Towards Human-Like Machine Comprehension: Few-Shot Relational Learning in Visually-Rich Documents—0
Towards Omni-Supervised Face Alignment for Large Scale Unlabeled Videos—0
Transfer Learning in Visual and Relational Reasoning—0
TransGCN:Coupling Transformation Assumptions with Graph Convolutional Networks for Link Prediction—0
TransRev: Modeling Reviews as Translations from Users to Items—0
TRIP: Temporal Residual Learning with Image Noise Prior for Image-to-Video Diffusion Models—0
Understanding the Complexity of Lifted Inference and Asymmetric Weighted Model Counting—0
Unified Graph Structured Models for Video Understanding—0
User Profiling Using Hinge-loss Markov Random Fields—0
Variational Deep Logic Network for Joint Inference of Entities and Relations—0
Variational Quantum Circuit Model for Knowledge Graphs Embedding—0
VC-Dimension Based Generalization Bounds for Relational Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified