2,104 zoekresultaten voor “beter learning” in de Publieke website
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Computational speedups and learning separations in quantum machine learning
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…
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Massively collaborative machine learning
Promotor: J. N. Kok, Co-promotor: A. J. Knobbe
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Transfer Learning in Deep Reinforcement Learning and Procedural Content Generation
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…
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Deep learning for visual understanding
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.
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Understanding deep meta-learning
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.
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Learning in Automated Negotiation
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…
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Exploring deep learning for multimodal understanding
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.
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Exploring Deep Learning for Intelligent Image Retrieval
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.
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Machine Learning
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…
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Efficient tuning of automated machine learning pipelines
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…
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Betrokkenheid van studenten bij blended learning in het hoger onderwijs
Op welke manier kunnen docenten de betrokkenheid van studenten ondersteunen en vergroten in de context van blended learning?
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Learning from small samples
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…
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Learning My Way
Learning about My meaningful Way through life and profession.
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Machine learning and computer vision for urban drainage inspections
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.
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Searching by Learning: Exploring Artificial General Intelligence on Small Board Games by Deep Reinforcement Learning
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…
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Quantum Methods for Machine Learning and Classical Dynamics
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.
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Exploring Images With Deep Learning for Classification, Retrieval and Synthesis
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.
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Enhancing Autonomy and Efficiency in Goal-Conditioned Reinforcement Learning
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.
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Bayesian learning: challenges, limitations and pragmatics
This dissertation is about Bayesian learning from data. How can humans and computers learn from data?
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E-learning Antibiotica allergie
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.
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Learning Analytics and Data Science
Datagedreven werken in het onderwijs: het verzamelen, analyseren en interpreteren van gegevens uit onderwijsomgevingen voor verbetering van onderwijs- en leerresultaten.
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Leiden Learning & Innovation Centre
LLInC ondersteunt innovatief en hoogwaardig onderwijs, binnen de Universiteit Leiden en in samenwerking met onderwijs- en maatschappelijke organisaties.
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Hybrid Quantum-Classical Metaheuristics for Automated Machine Learning Applications
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.
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Using Deep Learning Models in Image Processing
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.
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Reliable and Fair Machine Learning for Risk Assessment
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.
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Effects of the early social environment on song and preference learning in zebra finches
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.
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Kunstmatige intelligentie en machine learning
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…
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Deep learning for tomographic reconstruction with limited data
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.
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Life long learning
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.
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Deep Learning Solutions for Domain-Specific Image Segmentation
Image segmentation is a fundamental task in computer vision, with applications ranging from medical diagnostics to archaeological research.
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Statistical learning for complex data to enable precision medicine strategies
Explaining treatment response variability between and within patients can support treatment and dosing optimization, to improve treatment of individual patients.
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Machine learning-based NO2 estimation from seagoing ships using TROPOMI/S5P satellite data
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.
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E-learning Antibiotica voorschrijven in de eerstelijn
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.
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E-learning Antibiotica voorschrijven in de tweedelijn
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…
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Professional learning: what teachers want to learn
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?
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Automata learning: from probabilistic to quantum
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…
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Broadening Youth Participation in STEM Learning
How can we broaden youth participation in STEM Learning
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Automated machine learning for dynamic energy management using time-series data
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…
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The crucible of war: Dutch and British military learning processes in and beyond southern Afghanistan
In welke mate hebben de Nederlandse en Britse strijdkrachten geleerd van hun counterinsurgency-operaties in Zuid-Afghanistan tussen 2006 en 2020?
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Over het Centre for Professional Learning
Werken bij een topuniversiteit, met uitstekende carrièremogelijkheden? Ontdek de mogelijkheden bij het Centre for Professional Learning van de Universiteit Leiden.
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Solving the Gravitational N-body Problem with Machine Learning
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.
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Automated Machine Learning for Neural Network Verification
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Medewerkers van het Centre for Professional Learning
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.
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post-translationally modified peptides in Streptomyces with machine learning
The ongoing increase in antimicrobial resistance combined with the low discovery of novel antibiotics is a serious threat to our health care.
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Seeing voices: the role of multimodal cues in vocal learning
Humans and songbirds learn their vocalizations early in life by exposure to the vocalizations of adult conspecifics. Often, better learning outcomes are achieved with live, social, tutors than with audio-only exposure to vocalizations.
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Machine learning for radio galaxy morphology analysis
We explored how to morphologically classify well-resolved jetted radio-loud active galactic nuclei (RLAGN) in the LOw Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS) using machine learning.
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Docenten
De docenten aangesloten bij Inclusief Leiderschap voor Depolarisatie zijn onder andere:
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Multi Modal Representation Learning and Cross-Modal Semantic Matching
Humans perceive the real world through their sensory organs: vision, taste, hearing, smell, and touch. In terms of information, we consider these different modesalso referred to as different channels of information or modals.
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Over het Centre for Professional Learning
Het Centre for Professional Learning (CPL) is sinds 1999 hét expertisecentrum van de Universiteit Leiden in Den Haag voor leren en ontwikkelen in het publieke domein.
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Exploring Open-World Visual Understanding with Deep Learning
We are living in an information era where the amount of image and video data increases exponentially.