Predicting voluntary employee turnover using core employee data

Voluntary employee turnover is the process of an employee voluntarily choosing to resign from a company. High voluntary turnover has been shown to have negative effect on both organisational and financial performance of companies. Therefore, if companies were to know which individuals would leave their company in the coming months, this would...

Activating frequencies: Exploring non-linearities in the Fourier domain

Convolutional Neural Networks (CNNs) are achieving state of the art performance in computer vision. One downside of CNNs is their computational complexity. One way to make CNNs more computational efficient is by implementing their convolutions in the frequency domain, using Fast Fourier Transforms (FFTs). This has as a consequence that most...

Active Learning for Overlay Prediction in Semi-conductor Manufacturing

In the manufacturing of semi-conductor devices there is a constant demand for increasing precision and yield. Measuring and controlling overlay errors is essential in this process, but these measurements are difficult and costly. Predictive models can be used as an addition to measurements, but they required labelled data for training. To...

Quantitative evaluation of Generative Adversarial Networks and improved training techniques

Generative adversarial networks (GANs) are a class of generative models, for which the goal is to learn from training data and then to generate data with similar characteristics. Despite the wide use of GANs, a quantitative evaluation method of their performance is lacking. In the current work, we invented a series of artificial datasets,...

Model-based rare category detection for temporal data

Rare category detection is the task of discovering rare classes in unlabelled and imbalanced datasets. Existing algorithms focus almost exclusively on static data in which instances are assumed to be independent. In this thesis we propose an algorithm that is designed for temporal data. Specifically, we are interested in data with temporal...

Outlier detection in multivariate time series: Exploiting reconstructions from random projections

Rituals of Leaving: Predictive Modelling of Leaving Behaviour in Conversation

In this thesis, leaving behaviour in a small group setting is studied. In the past, group conversations have mostly been studied in a static setting. Inclusion of a temporal dimension would be of use in numerous applications. In order to account for temporal elements, we must understand how group conversations evolve. Leaving and joining a...

Action Selection Policies for Walking Monte Carlo Tree Search

Recent Reinforcement Learning methods have combined function approximation and Monte Carlo Tree Search and are able to learn by self-play up to a very high level in several games such as Go and Hex. One aspect in this combination<br/>that has not had a lot of attention is the action selection policy during self-play, which could influence the...

Time series forecast in non-stationary environment with occurrence of economic bubbles: Bitcoin Price prediction

Being capable to foresee the future of a given financial asset as an investor, may lead to significant economic profits. Therefore, stock market prediction is a field that has been extensively developed by numerous researchers and companies. Recently, however, a new branch of financial assets has emerged, namely cryptocurrencies. As a...

Recognizing and Handling Negations in Machine Learning

In several machine learning problems, a relatively small subproblem is present in which combinations of (negating) objects or structures result in a negation or otherwise other classification compared to when these (negating) objects are not present. To be more specific, a variant of the XOR problem is present in a small amount of objects in...

Automated land use classification: Supervised segmentation of road structures on aerial images using shape regression

Recent advances in Artificial Intelligence and Computer Vision have been showed to be promising for automated land use classification of remotely sensed data. However, current state-of-the-art per-pixel segmentation networks fail to accurately capture geometrical and topological properties on land use segmentation, as these methods have...

Fuzzy Face Clustering For Forensic Investigations

The amount of personal imagery kept on (mobile) devices is increasing by the day. Analysis and organization of these large collections of data are becoming increasingly important in the field of digital forensics, as they can aid in the search for legal evidence. The grouping of faces based on their identity is an important aspect as it provides...

Attention-Aware Age-Agnostic Visual Place Recognition

A cross-domain visual place recognition (VPR) task is proposed in this work, i.e., matching images of the same architectures depicted in different domains. VPR is commonly treated as an image retrieval task, where a query image from an unknown location is matched with relevant instances from geo-tagged gallery database. Different from...

Exploring normalizing flow for anomaly detection

Anomaly detection is a task of interest in many domains. Typical way of tackling this problem is using an unsupervised way. Recently, deep neural network based density estimators such as Normalizing flows have seen a huge interest. The ability of these models to do the exact latent-variable inference and exact log-likelihood calculation with...

Top-Down Networks: A coarse-to-fine reimagination of CNNs

Biological vision adopts a coarse-to-fine information processing pathway, from initial visual detection and binding of salient features of a visual scene, to the enhanced and preferential processing given relevant stimuli. On the contrary, CNNs employ a fine-to-coarse processing, moving from local, edge-detecting filters to more global ones...