Mathematics of Double Descent

Recently, there has been an increase in literature about the Double Descent phenomenon for heavily over-parameterized models. Double Descent refers to the shape of the test risk curve, which can show a second descent in the over-parameterized regime, resulting in the remarkable combination of both low training and low test risk. However, much is...

To Overfit, or Not to Overfit: Improving the Performance of Deep Learning-Based SCA

Profiling side-channel analysis allows evaluators to estimate the worst-case security of a target. When security evaluations relax the assumptions about the adversary’s knowledge, profiling models may easily be sub-optimal due to the inability to extract the most informative points of interest from the side-channel measurements. When used for...

Improving machine learning based side-channel analysis: can dropout be dropped out?

Analysing physical leakages (e.g. power consumption and electromagnetic radiation) of cryptographic devices can be used by adversaries to extract secret keys. Over the last couple of years, researchers have shown that machine learning has potential for this process. Machine learning models need to be fine-tuned to enhance key extraction...

Preventing overfitting in Mixed Integer Optimization based classification tree construction

The global use of azole antifungals as treatment against infections caused by <i>Candida</i> has led to an increase in azole resistance. The primal goal of this BSc thesis is to improve existing Mixed Integer Optimization models to classify azole resistance of <i>C. glabrata</i> more accurately by preventing overfitting. Moreover, these...

Tuning of Optical Beamforming Networks: A Deep Learning Approach

Optical beamforming networks (OBFNs), which consist of many small and flat antennas, called phased array antennas (PAAs), can be tuned such that the signal beam from the airplanes can be steered towards a satellite. This was proposed as a alternative to the mechanically steered antenna, which has many disadvantages. The problem of tuning a large...