Completed Master's theses

Here we list some of the Master's theses that have been completed with a supervisor from the Machine Learning group. Theses that are available in the Bergen Open Research Archive are are available through the provided links.

2023

• Willem Theodorus Schooltink. Topological Regularization of Support Vector Machines.
• Alvar Hønsi. Gaussian Likelihoods in Bayesian Neural Networks.
• Sigurd Roll Solberg. Vectorizing Distributed Homology with Deep Set of Set Networks.
• Audun Ljone Henriksen. Learning Acquisition Functions for Cost-aware Bayesian Optimization.
• Simen Høyvik. Document Ranking for Systematic Reviews in Medicine.
• Anastasiia Vlasenko. Multi-List Recommendations for Personalizing Streaming Content.
• Morten Blørstad. Improving Stability of Tree-Based Models.
• Peter Løkhammer Liessem. Object Tracking Approach for Catch Estimation on Trawl Surveys.
• Julia Cat-Vy Nguyen. Vessel recognition in ultrasound images using machine learning techniques.
• Eivind Anton Sætre Skarstein. Building a finite state automaton for physical processes using queries and counterexamples on long short-term memory models.
• Sven Alrik Solemdal. Automatic Detection of the Arterial Input Function in DCE-MRI.
• Emir Zamwa. Generative Adversarial Networks for Annotating Images of Otoliths.
• Anders Imenes. Combining Query Rewriting and Knowledge Graph Embeddings for Complex Query Answering.
• Erik Hystad. Online learning through Reinforcement learning in a high-fidelity physics simulator.
• Anders Stigen Mikkelsen. Deep Reinforcement Learning Self-Play In Trading Financial Market.
• Thorarinn Sigurvin Gunnarsson. Metrics exploration in the context of ensemble weather forecasts using deep learning.
• Hans Martin Aannestad. Transfer Learning Remaining Machine Life by Deep Convolutional Neural Networks.

2022

• Johanna Jøsang. Rule mining on extended knowledge graphs.
• John Isak Fjellvang Villanger. Communication in Turn Based Multiplayer Games Using Deep Reinforcement Learning.
• Christian Mehl Wergeland. Exploring Ways of Creating an AI Drawing Assistant.
• Knut Thormod Aarnes Holager. Selecting Maximally Informative Frequency Subsets for Acoustic Surveys.
• Erlend Fonnes. Automatic blurring of specific faces in video.
• Hans Martin Theigler Johansen. Making a Drawing-Assistant System using Deep Learning.
• Adrian Tvilde Evensen. Binary domain classification for Norwegian language in task-oriented dialogue systems.
• Mathias Larsson Madslien. Deep Learning Methods for Automated Classification of Fish Behavior.
• Halvor Helland. MetZoom: A CNN/LSTM hybrid based model for water reservoir inflow prediction.
• Bård Ersland. Memory-based control for quadrupedal locomotion - a sim-to-real study.
• Brage Alvsvåg. Improving fish detection using efficient neural networks.
• Fromsa Hera. Density estimation of single-cell mass cytometry with generative models.

2021

• Jonas Folkvord Triki. Analysis of Word Embeddings: A Clustering and Topological Approach.
• Tord Sture Stangeland. Seismic Event Classification using Machine Learning.

2019

• Kristian Rosland. Predicting Loss of Inference Accuracy in Bounded Tree-Width Bayesian Networks.