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 451–483 of 483 papers

TitleStatusHype
Joint Information Extraction and Reasoning: A Scalable Statistical Relational Learning Approach—0
Numeric Input Relations for Relational Learning with Applications to Community Structure Analysis—0
Stochastic And-Or Grammars: A Unified Framework and Logic Perspective—0
SkILL - a Stochastic Inductive Logic Learner—0
Content+Context=Classification: Examining the Roles of Social Interactions and Linguist Content in Twitter User Classification—0
Contrastive Feature Induction for Efficient Structure Learning of Conditional Random Fields—0
Recursive Neural Networks Can Learn Logical Semantics—0
kLogNLP: Graph Kernel--based Relational Learning of Natural Language—0
Locally Boosted Graph Aggregation for Community Detection—0
Understanding the Complexity of Lifted Inference and Asymmetric Weighted Model Counting—0
How to construct a multi-lingual domain ontology—0
Efficient Inference and Learning in a Large Knowledge Base: Reasoning with Extracted Information using a Locally Groundable First-Order Probabilistic Logic—0
Inductive Logic Boosting—0
Hybrid SRL with Optimization Modulo Theories—0
A Boosting Approach to Learning Graph Representations—0
Post-Proceedings of the First International Workshop on Learning and Nonmonotonic Reasoning—0
Ensemble Relational Learning based on Selective Propositionalization—0
Logistic Tensor Factorization for Multi-Relational Data—0
Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss—0
A Semantic Matching Energy Function for Learning with Multi-relational Data—0
Slice Normalized Dynamic Markov Logic Networks—0
On Lifting the Gibbs Sampling Algorithm—0
Search Space Properties for Learning a Class of Constraint-based Grammars—0
A Statistical Relational Learning Approach to Identifying Evidence Based Medicine Categories—0
Reading The Web with Learned Syntactic-Semantic Inference Rules—0
Learning to ``Read Between the Lines'' using Bayesian Logic Programs—0
Adding Distributional Semantics to Knowledge Base Entities through Web-scale Entity Linking—0
kLog: A Language for Logical and Relational Learning with Kernels—0
A Three-Way Model for Collective Learning on Multi-Relational Data—0
Efficient Relational Learning with Hidden Variable Detection—0
Computing Marginal Distributions over Continuous Markov Networks for Statistical Relational Learning—0
Ranking relations using analogies in biological and information networks—0
Abstraction and Relational learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified