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Found 6 papers, 1 papers with code
Interactive Ontology Matching with Cost-Efficient Learning
The creation of high-quality ontologies is crucial for data integration and knowledge-based reasoning, specifically in the context of the rising data economy.
ProcK: Machine Learning for Knowledge-Intensive Processes
We present a novel methodology to build powerful predictive process models.
The Combinatorial Multi-Bandit Problem and its Application to Energy Management
For our energy management application we propose a range of algorithms that combine exploration principles for multi-arm bandits with mathematical programming.
Large-Scale Cargo Distribution
This study focuses on the design and development of methods for generating cargo distribution plans for large-scale logistics networks.
A Study of the Learning Progress in Neural Architecture Search Techniques
As a surprising result, we find that the learning curves are completely flat, i. e., there is no observable progress of the controller in terms of the performance of its generated architectures.
On the Performance of Differential Evolution for Hyperparameter Tuning
This empirical study involves a range of different machine learning algorithms and datasets with various characteristics to compare the performance of Differential Evolution with Sequential Model-based Algorithm Configuration (SMAC), a reference Bayesian Optimization approach.
