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Found 9 papers, 1 papers with code
Promoting Research Collaboration with Open Data Driven Team Recommendation in Response to Call for Proposals
Building teams and promoting collaboration are two very common business activities.
Knowledge-based Refinement of Scientific Publication Knowledge Graphs
We consider the problem of identifying authorship by posing it as a knowledge graph construction and refinement.
ToupleGDD: A Fine-Designed Solution of Influence Maximization by Deep Reinforcement Learning
Aiming at selecting a small subset of nodes with maximum influence on networks, the Influence Maximization (IM) problem has been extensively studied.
Explainable Models via Compression of Tree Ensembles
Ensemble models (bagging and gradient-boosting) of relational decision trees have proved to be one of the most effective learning methods in the area of probabilistic logic models (PLMs).
Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach
Predicting and discovering drug-drug interactions (DDIs) using machine learning has been studied extensively.
A Statistical Relational Approach to Learning Distance-based GCNs
We consider the problem of learning distance-based Graph Convolutional Networks (GCNs) for relational data.
The Curious Case of Stacking Boosted Relational Dependency Networks
Reducing bias while learning and inference is an important requirement to achieve generalizable and better performing models.
Non-Parametric Learning of Gaifman Models
We consider the problem of structure learning for Gaifman models and learn relational features that can be used to derive feature representations from a knowledge base.
Beyond Textual Data: Predicting Drug-Drug Interactions from Molecular Structure Images using Siamese Neural Networks
Predicting and discovering drug-drug interactions (DDIs) is an important problem and has been studied extensively both from medical and machine learning point of view.
