In this work, I explore the creation of new methods that optimize simulations of the gravitational N-body problem. Specifically, I take advantage of the recent popularity of Machine Learning methods to find tools that can suit this problem.

The first direct detection of gravitational waves opened the possibility of mapping the Universe via this new and independent messenger.

Promotor: K. Kuijken, Co-Promotor: H. Hoekstra

One of the most important correlation functions in physics, especially in cosmology, is the energy density, which describes how much energy is present at each point in spacetime due to matter fields. A key contribution to the energy density of the primordial universe comes from gravitational waves (GWs),…

We explored the Universe using weak gravitational lensing, a phenomenon that occurs when light from distant galaxies is bent by the gravitational fields of closer cosmic objects, much like how a lens distorts light.

Of all the mass in our Universe, 80% is thought to consist of a hypothetical and invisible substance called dark matter (DM).

In astronomy, the interpration of observations and measurements plays a crucial role: we rely purely and fundamentally on the information that reaches us as observers. And 80% of all matter in the universe is undetectable directly.

In this thesis we study the landscape of gravitational models which modify GR by introducing an additional scalar degree of freedom (d.o.f.) to source Cosmic Acceleration.

This thesis investigates the contribution of quantum computers to machine learning, a field called Quantum Machine Learning. Quantum Machine Learning promises innovative perspectives and methods for solving complex problems in machine learning, leveraging the unique capabilities of quantum computers…

Promotor: J. N. Kok, Co-promotor: A. J. Knobbe

In this dissertation (titled: Exploring the Synergies between Transfer in Reinforcement Learning and Procedural Content Generation) we explore how the two research fields named in the title, namely Transfer in Reinforcement Learning (TRL) and Procedural Content Generation (PCG) can synergize togethe…

With the dramatic growth of the image data on the web, there is an increasing demand of the algorithms capable of understanding the visual information automatically.

The invention of neural networks marks a critical milestone in the pursuit of true artificial intelligence. Despite their impressive performance on various tasks, these networks face limitations in learning efficiently as they are often trained from scratch.

This dissertation advances automated negotiation by developing agents that can learn and adapt across diverse negotiation settings through three increasingly sophisticated approaches: automated algorithm configuration, portfolio-based strategy selection, and end-to-end reinforcement learning with graph…

This thesis mainly focuses on multimodal understanding and Visual Question Answering (VQA) via deep learning methods. For technical contributions, this thesis first focuses on improving multimodal fusion schemes via multi-stage vision-language interactions.

This thesis mainly focuses on cross-modal retrieval and single-modal image retrieval via deep learning methods, i.e. by using deep convolutional neural networks.

Computers zijn in staat om ongelooflijk nauwkeurige voorspellingen te doen op basis van machine learning. Met andere woorden, deze computers kunnen zonder tussenkomst leren als ze eenmaal door mensen zijn voorgeprogrammeerd. Bij LIACS verkennen en verleggen we de grenzen van wat een revolutionaire nieuwe…

For centuries astronomers studied the Universe by collecting light. Nowadays, we are living in times of great technological advancements, which allow us to explore our Universe in a new way - though gravitational wave radiation.

Automated Machine Learning (AutoML) is widely used to automatically build a suitable practical Machine Learning (ML) model for an arbitrary real-world problem, reducing the effort of practitioners in the ML development cycle for real-world applications. Optimization is a key part of a typical AutoML…

Op welke manier kunnen docenten de betrokkenheid van studenten ondersteunen en vergroten in de context van blended learning?

Learning from small data sets in machine learning is a crucial challenge, especially when dealing with data imbalances and anomaly detection. This thesis delves into the challenges and methodologies of learning from small datasets in machine learning, with a particular focus on addressing data imbalances…

Learning about My meaningful Way through life and profession.

Sewer pipes are an essential infrastructure in modern society and their proper operation is important for public health. To keep sewer pipes operational as much as possible, periodical inspections for defects are performed.

In deep reinforcement learning, searching and learning techniques are two important components. They can be used independently and in combination to deal with different problems in AI, and have achieved impressive results in game playing and robotics. These results have inspired research into artificial…

All the data stored and processed by our computers is encoded as sequences of zeros and ones, called bits. Quantum computers offer an alternative to this traditional way of encoding and manipulating information.

In 2018, the number of mobile phone users will reach about 4.9 billion. Assuming an average of 5 photos taken per day using the built-in cameras would result in about 9 trillion photos annually.

Reinforcement learning is a framework that enables agents to learn in a manner similar to humans, i.e. through trial and error. Ideally, we would like to train a generalist agent capable of performing multiple tasks and achieving various goals.

This dissertation is about Bayesian learning from data. How can humans and computers learn from data?

At the largest scales, two ingredients dictate the distribution of matter in the Universe. The first is dark matter, acting as an invisible scaffolding held together by gravitational forces.

Als zorgverlener komt u regelmatig in aanraking met patiënten die aangeven allergisch te zijn voor antibiotica. In deze online module leert u verschillende typen allergieën onderscheiden en dilemma’s uit de dagelijkse praktijk op te lossen.

LLInC ondersteunt innovatief en hoogwaardig onderwijs, binnen de Universiteit Leiden en in samenwerking met onderwijs- en maatschappelijke organisaties.

This thesis investigates how quantum, quantum-inspired, and hybrid quantum-classical computation can enhance key points of the automated machine learning (AutoML) pipeline under the constraints of noisy intermediate-scale quantum (NISQ) devices.

Beeldbewerking heeft met de komst van Deep Learning een revolutie doorgemaakt. Dit heeft, in het bijzonder, opmerkelijke voortgang gegeven in het onderzoeksveld variërend van autonome besturing van voertuigen tot medische diagnostiek.

The focus of this thesis is on the technical methods which help promote the movement towards Trustworthy AI, specifically within the Inspectorate of the Netherlands.

Songbirds as vocal learners learn their songs and song preference from social tutors⁠. Tutor choice for both song and preference learning are important to characterize for understanding individual learning performance and cultural transmission of song⁠.

Datagedreven werken in het onderwijs: het verzamelen, analyseren en interpreteren van gegevens uit onderwijsomgevingen voor verbetering van onderwijs- en leerresultaten.

Tomography is a powerful technique to non-destructively determine the interior structure of an object.Usually, a series of projection images (e.g.\ X-ray images) is acquired from a range of different positions.

Computers zijn in staat om ongelooflijk nauwkeurige voorspellingen te doen op basis van machine learning. Met andere woorden, deze computers kunnen zonder tussenkomst leren als ze eenmaal door mensen zijn voorgeprogrammeerd. Bij LIACS verkennen en verleggen we de grenzen van wat een revolutionaire nieuwe…

This thesis investigates the structural evolution and assembly of galaxies since the first few billions years after the big bang.

Uw persoonlijke en professionele ontwikkeling stopt niet bij het behalen van uw diploma. De faculteit en universiteit bieden u tal van mogelijkheden om na uw studententijd voor een gereduceerd tarief trainingen en workshops te kunnen volgen. Zo kunt u zich blijven ontwikkelen.

Image segmentation is a fundamental task in computer vision, with applications ranging from medical diagnostics to archaeological research.

Explaining treatment response variability between and within patients can support treatment and dosing optimization, to improve treatment of individual patients.

The marine shipping industry is one of the strongest emitters of nitrogen oxides (NOx), a pollutant detrimental to ecology and human health. Over the last 20 years, the pollution produced by power plants, the industry sector, and cars has been decreasing.

Voor zorgprofessionals is het is belangrijk om te weten wanneer u welke antibiotica moet voorschrijven. In deze e-learning leert u hoe de systematiek van behandeling met antibiotica werkt in de eerstelijn.

Bacteriële infecties behoren tot de meest voorkomende doodsoorzaken ter wereld. Als zorgprofessional in Nederland krijgt u waarschijnlijk ook te maken met het behandelen van dit soort infecties. Het is daarom belangrijk om te weten wanneer u welke antibiotica moet voorschrijven. In deze e-learning leert…

Het doel van dit promotieonderzoek was te onderzoeken wat leraren zelf willen leren. De centrale onderzoeksvraag luidde: wat, hoe en waarom willen leraren leren? En hangt dit af van hun jaren leservaring en de school waarin ze werken?

This thesis advances automata learning, a key area in computer science, with applications in software verification, biological analysis, and autonomous technologies. It explores three main themes: first, it introduces a passive learning algorithm for generating compact probabilistic models from positive…

How can we broaden youth participation in STEM Learning

Time-series forecasting through modelling sequences of temporally dependent observations has many industrial and scientific applications. While machine learning models have been widely used to create time-series forecasting models, creating efficient and performant time-series forecasting models is…

Bij het Centre for Professional Learning werken de directeur, programmaleiders, programmacoördinators en het marketing- en communicatieteam samen. Wij streven naar een optimale leerbeleving met hoogwaardig onderwijs voor professionals.