Deep convolutional networks are able to learn representation of images, scoring well in tasks such as image classification and object detection. During model training, these networks have the ability to process different input sizes without requiring changes to their architecture. In this project, we would like to investigate the effects that changing input sizes has on these kinds of models. We …
Supervisors: Pınar Tözün, Ties Robroek
Semester: Fall 2024
Tags: data attribution, deep learning, machine learning, resource efficiency

Today’s foundation models are trained on vast amounts of data. The quality and size of this data has a huge impact on the accuracy of these models. Selecting the right amount and variety of data for a given task, however, is a resource-intensive process. In this project, we would like to investigate various state-of-the-art data selection mechanisms from a hardware requirements and …
Supervisors: Pınar Tözün, Ties Robroek
Semester: Fall 2024
Tags: data selection, deep learning, machine learning, resource efficiency

A medical Visual Question Answering (VQA) system can provide meaningful references for both doctors and patients during the treatment process. Different from normal images, a learning setting with medical images is more challenging due limited amounts of data, class-imbalance and the presence of label noise for diagnosis tasks. Moreover, little attention is paid to how the images and meta-data is …
Supervisors: Amelia Jiménez-Sánchez
Semester: Fall 2023
Tags: medical imaging, deep learning, machine learning, transfer learning, meta-learning

Deep neural networks have been revolutionary in computer vision and publicly available image datasets played an important role in this success. Due to their size, neural networks require vast amounts of data for training. Yet when it comes to medical settings dataset sizes are very limited due to the cost of data annotation, privacy concerns, differences in imaging techniques, and others. In such …
Supervisors: Dovile Juodelyte
Semester: Fall 2023
Tags: transfer learning, deep learning, medical imaging

The goal of the project is to explore the accuracy of electricity production predictions based on historical data and weather predictions. This may be tackled as a sequence prediction problem using recurrent neural networks The long term goal is to incorporate wind turbines in the reserve market for electricity.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Forecasting, Machine Learning, Deep Learning