Sequential Monte Carlo method for training Neural Networks on non-stationary time series

In this thesis, we study the sequential Monte Carlo method for training neural networks in the context of time series forecasting. Sequential Monte Carlo can be particularly useful in problems in which the data is sequential, noisy and non-stationary. We compare this algorithm against a gradient-based method known as stochastic gradient descent ...

Nerve fiber tracing in bright-field images of human skin using deep learning

The goal of this thesis is to find an automated method that can trace all nerve fibers in bright-field images of skin tissue. This is an important step towards the automated quantification of intra-epidermal nerve fiber density, an important biomarker in the diagnosis of small-fiber neuropathy.<br/>Deep learning is a popular new field of...