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Found 13 papers, 1 papers with code
Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach
Predicting and discovering drug-drug interactions (DDIs) using machine learning has been studied extensively.
Multi-Robot Routing with Time Windows: A Column Generation Approach
We formulate the problem as a weighted set packing problem where the elements in consideration are items on the warehouse floor that can be picked up and delivered within specified time windows.
Integer Programming for Multi-Robot Planning: A Column Generation Approach
We consider the problem of coordinating a fleet of robots in a warehouse so as to maximize the reward achieved within a time limit while respecting problem and robot specific constraints.
Knowledge Graph Alignment using String Edit Distance
In this work, we propose a novel knowledge graph alignment technique based upon string edit distance that exploits the type information between entities and can find similarity between relations of any arity
Non-Parametric Learning of Lifted Restricted Boltzmann Machines
We consider the problem of discriminatively learning restricted Boltzmann machines in the presence of relational data.
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.
Neural Networks for Relational Data
While deep networks have been enormously successful over the last decade, they rely on flat-feature vector representations, which makes them unsuitable for richly structured domains such as those arising in applications like social network analysis.
Knowledge-augmented Column Networks: Guiding Deep Learning with Advice
Recently, deep models have had considerable success in several tasks, especially with low-level representations.
Human-Guided Column Networks: Augmenting Deep Learning with Advice
While extremely successful in several applications, especially with low-level representations; sparse, noisy samples and structured domains (with multiple objects and interactions) are some of the open challenges in most deep models.
Human-Guided Learning of Column Networks: Augmenting Deep Learning with Advice
Recently, deep models have been successfully applied in several applications, especially with low-level representations.
Structure Learning for Relational Logistic Regression: An Ensemble Approach
We consider the problem of learning Relational Logistic Regression (RLR).
Advice Refinement in Knowledge-Based SVMs
Knowledge-based support vector machines (KBSVMs) incorporate advice from domain experts, which can improve generalization significantly.
