About the project
Within deep neural networks, simplifying and pruning highly over-parameterized models have shown to improve accuracy, and results in sparse networks that also are more computationally efficient and sometimes easier to interpret. This is very important for next generation machine learning algorithms. In this project we aim at exploring Bayesian approaches to sparsification/pruning for neural networks. Focus will be on prior specifications, statistical properties and efficient algorithms. Exploration of the pruning approach in other machine/statistical learning settings will also be of interest. While sparse models usually are obtained by going from large to small, also procedures going from small to large will be considered.
Project team
Professor Geir Olve Storvik, University of Oslo, Department of Mathematics
Professor Fred Godtliebsen, UiT The Arctic University of Norway, Department of Mathematics and Statistics
Postdoctoral Fellow Leiv Rønneberg, University of Oslo, Department of Mathematics
PhD Fellow August Arnstad, University of Oslo, Department of Mathematics