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 401–450 of 483 papers

TitleStatusHype
Recurrent Relational NetworksCode0
Tensor Decompositions for Modeling Inverse Dynamics—0
Mandolin: A Knowledge Discovery Framework for the Web of DataCode0
Deep Semantic Abstractions of Everyday Human Activities: On Commonsense Representations of Human Interactions—0
Usable & Scalable Learning Over Relational Data With Automatic Language Bias—0
On Inductive Abilities of Latent Factor Models for Relational LearningCode0
Character-based recurrent neural networks for morphological relational reasoning—0
Multi-task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs—0
KeLP at SemEval-2017 Task 3: Learning Pairwise Patterns in Community Question Answering—0
Relational Learning and Feature Extraction by Querying over Heterogeneous Information Networks—0
Adversarial Sets for Regularising Neural Link PredictorsCode0
Stochastic Gradient Descent for Relational Logistic Regression via Partial Network Crawls—0
RelTextRank: An Open Source Framework for Building Relational Syntactic-Semantic Text Pair Representations—0
Robust Face Tracking using Multiple Appearance Models and Graph Relational LearningCode0
RelNet: End-to-End Modeling of Entities & Relations—0
A simple neural network module for relational reasoningCode0
Deep Learning for Ontology Reasoning—0
Logic Tensor Networks for Semantic Image InterpretationCode0
Induction of Interpretable Possibilistic Logic Theories from Relational Data—0
Online learnability of Statistical Relational Learning in anomaly detection—0
Demystifying Relational Latent Representations—0
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge GraphsCode0
Knowledge Graph Completion via Complex Tensor FactorizationCode0
Graph Based Relational Features for Collective ClassificationCode0
Introducing DRAIL -- a Step Towards Declarative Deep Relational Learning—0
Weakly Supervised Tweet Stance Classification by Relational Bootstrapping—0
Discriminative Gaifman Models—0
Multiple protein feature prediction with statistical relational learning—0
Column Networks for Collective ClassificationCode0
Relational Similarity Machines—0
Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text—0
Application of Statistical Relational Learning to Hybrid Recommendation Systems—0
On the Semantic Relationship between Probabilistic Soft Logic and Markov Logic—0
A Learning Algorithm for Relational Logistic Regression: Preliminary Results—0
Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation—0
ConvKN at SemEval-2016 Task 3: Answer and Question Selection for Question Answering on Arabic and English Fora—0
Scalable Statistical Relational Learning for NLP—0
Cross-Graph Learning of Multi-Relational Associations—0
Multi-Relational Learning at Scale with ADMM—0
Regularized Orthogonal Tensor Decompositions for Multi-Relational Learning—0
Lifted Symmetry Detection and Breaking for MAP Inference—0
The CTU Prague Relational Learning Repository—0
Holographic Embeddings of Knowledge GraphsCode0
Lifted Relational Neural Networks—0
Schema Independent Relational Learning—0
FactorBase: SQL for Learning A Multi-Relational Graphical Model—0
SQL for SRL: Structure Learning Inside a Database System—0
Structural Representations for Learning Relations between Pairs of Texts—0
Learning Relational Features with Backward Random Walks—0
Matrix and Tensor Factorization Methods for Natural Language Processing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified