Recommender Systems with Evolutionary Algorithms: Many­-Objective Optimization for Large­-Scale Music Recommendation: A demonstration showing the reliability of serving users recommendations with trade-­off for large music collections, by leveraging diverse Recommender Systems and Evolutionary Algorithms

Using Recommender Systems with Evolutionary Algorithms is an extremely niche domain. It holds the key to enabling new user interaction designs, where users can effectively configure their experience with a Recommender System. This thesis answers important questions about the scientific aspects of its application to large-scale data through a...

Evaluating and Improving Large-Scale Machine Learning Frameworks

Given the increasing popularity of Machine Learning, and the ever increasing need to solve larger and more complex learning challenges, it is unsurprising that numerous distributed learning strategies have been brought forward in recent years, along with many large scale Machine Learning frameworks. It is however unclear how well these...

Tensor Network Kalman Filter for Large-Scale MIMO Systems: With Application to Adaptive Optics

For large-scale system with tens of thousands of states and outputs the computation in the conventional Kalman filter becomes time-consuming such that Kalman filtering in large-scale real-time application is practically infeasible. A possible mathematical framework to lift the curse of dimensionality is to lift the problem in higher dimensions...

Quantifying the effect of woody vegetation on the wave loads on a dike using remote sensing: Large scale physical model tests

It is already acknowledged that Nature-based Solutions can be used to attenuate waves, however it is still uncertain to what extent the vegetation can contribute to decreasing the flood risk. So far mainly small-scale tests have been performed to quantify wave attenuating properties of vegetation. To quantify the effect of more extreme wave...