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 201–210 of 483 papers

TitleStatusHype
DEVICE: DEpth and VIsual ConcEpts Aware Transformer for TextCaps—0
Desk Organization: Effect of Multimodal Inputs on Spatial Relational Learning—0
Demystifying Relational Latent Representations—0
Boosting gets full Attention for Relational Learning—0
Deep Sets for Generalization in RL—0
Deep Semantic Abstractions of Everyday Human Activities: On Commonsense Representations of Human Interactions—0
Introducing DRAIL -- a Step Towards Declarative Deep Relational Learning—0
Interpretable Reinforcement Learning With Neural Symbolic Logic—0
Deep reinforcement learning with relational inductive biases—0
Benchmarking Systematic Relational Reasoning with Large Language and Reasoning Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified