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 201–225 of 483 papers

TitleStatusHype
Differentiable Parsing and Visual Grounding of Natural Language Instructions for Object Placement—0
Knowledge Graph Embedding via Graph Attenuated Attention Networks—0
DEVICE: DEpth and VIsual ConcEpts Aware Transformer for TextCaps—0
Desk Organization: Effect of Multimodal Inputs on Spatial Relational Learning—0
kLog: A Language for Logical and Relational Learning with Kernels—0
Demystifying Relational Latent Representations—0
Boosting gets full Attention for Relational Learning—0
Joint Modeling of Visual Objects and Relations for Scene Graph Generation—0
Deep Sets for Generalization in RL—0
Jointly Extracting Explicit and Implicit Relational Triples with Reasoning Pattern Enhanced Binary Pointer Network—0
Joint Information Extraction and Reasoning: A Scalable Statistical Relational Learning Approach—0
Deep Semantic Abstractions of Everyday Human Activities: On Commonsense Representations of Human Interactions—0
Two pathways to resolve relational inconsistencies—0
KeLP at SemEval-2017 Task 3: Learning Pairwise Patterns in Community Question Answering—0
Introducing DRAIL -- a Step Towards Declarative Deep Relational Learning—0
kLogNLP: Graph Kernel--based Relational Learning of Natural Language—0
Interpretable Reinforcement Learning With Neural Symbolic Logic—0
Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection—0
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
Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation—0
Deep Learning for Ontology Reasoning—0
Deep Inductive Logic Reasoning for Multi-Hop Reading Comprehension—0
Deep Generative Models for Ultra-High Granularity Particle Physics Detector Simulation: A Voyage From Emulation to Extrapolation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified