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 351–360 of 483 papers

TitleStatusHype
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
Improving Composition of Sentence Embeddings through the Lens of Statistical Relational Learning—0
Deep reinforcement learning with relational inductive biases—0
Object-Oriented Model Learning through Multi-Level Abstraction—0
Object-Oriented Dynamics Learning through Multi-Level AbstractionCode0
A Relation-Augmented Fully Convolutional Network for Semantic Segmentation in Aerial Scenes—0
Relational Reasoning Network (RRN) for Anatomical Landmarking—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified