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
Cluster Flow: how a hierarchical clustering layer make allows deep-NNs more resilient to hacking, more human-like and easily implements relational reasoning—0
Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation—0
Collaborative Adversarial Learning for RelationalLearning on Multiple Bipartite Graphs—0
Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems—0
CommonsenseVIS: Visualizing and Understanding Commonsense Reasoning Capabilities of Natural Language Models—0
Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text—0
Composition of Sentence Embeddings: Lessons from Statistical Relational Learning—0
Composition of Sentence Embeddings:Lessons from Statistical Relational Learning—0
Computing Marginal Distributions over Continuous Markov Networks for Statistical Relational Learning—0
Concept Representation Learning with Contrastive Self-Supervised Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified