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 341–350 of 483 papers

TitleStatusHype
Neural Markov Logic Networks—0
MDE: Multiple Distance Embeddings for Link Prediction in Knowledge GraphsCode0
An Explicitly Relational Neural Network ArchitectureCode0
Neural-Symbolic Argumentation Mining: an Argument in Favor of Deep Learning and Reasoning—0
GMNN: Graph Markov Neural NetworksCode0
Learning Generative Models across Incomparable Spaces—0
The relational processing limits of classic and contemporary neural network models of language processingCode0
Language-Conditioned Graph Networks for Relational ReasoningCode0
Deep reinforcement learning with relational inductive biases—0
Improving Composition of Sentence Embeddings through the Lens of Statistical Relational Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified