909 zoekresultaten voor “machine learning” in de Publieke website
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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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Massively collaborative machine learning
Promotor: J. N. Kok, Co-promotor: A. J. Knobbe
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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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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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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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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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of 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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Data-Driven Machine Learning and Optimization Pipelines for Real- World Applications
Machine Learning is becoming a more and more substantial technology for industry.
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Quantum machine learning: on the design, trainability and noise-robustness of near-term algorithms
This thesis addresses questions on effectively using variational quantum circuits for machine learning tasks.
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Machine learning voorspelt voorkeuren
Cláudio de Sá voorspelt voorkeuren van mensen door gebruik te maken van ranglijsten. Dit doet hij door ‘klassieke’ machine learning-technieken aan te passen. Zijn werk kan onder andere gebruikt worden om de uitslagen van verkiezingen te voorspellen. Promotie op 16 december.
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Development of Machine Learning-Derived Digital Biomarkers for Trial@Home Clinical Trials
Promotie
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PNAS Paperprijs voor quantum machine learning
‘We hopen dat ons artikel de mogelijkheden en voordelen laat zien van het gebruik van kunstmatige intelligentie in de quantumfysica om nieuwe ontdekkingen te doen.’ Vedran Dunjko van het Leiden Institute of Advanced Computer Science droeg bij aan een artikel dat vorig jaar verscheen in PNAS. Het artikel…
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Novel system-inspired model-based quantum machine learning algorithm for prediction and generation of High-Energy Physics data
De huidige en toekomstige quantumcomputers vormen dezelfde uitdaging als de laser in zijn begindagen. In theorie werd voorspeld dat de laser een bron van zeer speciaal, zeer krachtig licht zou zijn. Maar in die tijd waren er geen duidelijke toepassingen voor. Critici van het idee noemden het een probleem…
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Optimally weighted ensembles of surrogate models for sequential parameter optimization
It is a common technique in global optimization with expensive black-box functions to learn a surrogate-model of the response function from past evaluations and use it to decide on the location of future evaluations.
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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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Modelling the interactions of advanced micro- and nanoparticles with novel entities
Novel entities may pose risks to humans and the environment. The small particle size and relatively large surface area of micro- and nanoparticles (MNPs) make them capable of adsorbing other novel entities, leading to the formation of aggregated contamination.
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Alumnus Robert Ietswaart: 'Machine learning leidt tot een revolutie in de medicijnontwikkeling'
Robert Ietswaart doet bij de befaamde Harvard Medical School in Boston onderzoek naar humane genregulatie. Hij ontwikkelde een machine learning-algoritme om beter te kunnen voorspellen of een kandidaatmedicijn bijwerkingen kan gaan vertonen. Ietswaart studeerde wiskunde en natuurkunde in Leiden, en…
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Data-driven donation strategies: understanding and predicting blood donor deferral
The research in this dissertation aims to optimise blood donation processes in the framework of the Dutch national blood bank Sanquin. The primary health risk for blood donors is iron deficiency, which is evaluated based on donors' hemoglobin and ferritin levels.
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Exploring big data approaches in the context of early stage clinical
Als gevolg van de grote technologische vooruitgang in de gezondheidszorg worden in toenemende mate gegevens verzameld tijdens de uitvoering van klinische onderzoeken.
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Network analysis methods for smart inspection in the transport domain
Transport inspectorates are looking for novel methods to identify dangerous behavior, ultimately to reduce risks associated to the movements of people and goods. We explore a data-driven approach to arrive at smart inspections of vehicles.
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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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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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Algorithm selection and configuration for Noisy Intermediate Scale Quantum methods for industrial applications
Quantum hardware comes with a different computing paradigm and new ways to tackle applications. Much effort has to be put into understanding how to leverage this technology to give real-world advantages in areas of interest for industries such as combinatorial optimization or machine learning.
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Numerical exploration of statistical physics
In this thesis, we examine various systems through the lens of several numerical methods.
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Data-Driven Risk Assessment in Infrastructure Networks
Leiden University and the Ministry of Infrastructure and Water Management are involved in a collaboration in the form of a research project titled 'Data-Driven Risk Assessment in Infrastructure Networks'
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Spectral imaging and tomographic reconstruction methods for industrial applications
Radiography is an important technique to inspect objects, with applications in airports and hospitals. X-ray imaging is also essential in industry, for instance in food safety checks for the presence of foreign objects.
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Methods and Tools for Mining Multivariate Time Series
Mining time series is a machine learning subfield that focuses on a particular data structure, where variables are measured over (short or long) periods of time.
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Model-assisted robust optimization for continuous black-box problems
Uncertainty and noise are frequently-encountered obstacles in real-world applications of numerical optimization. The practice of optimization that deals with uncertainties and noise is commonly referred to as robust optimization.
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Data-driven Predictive Maintenance and Time-Series Applications
Predictive maintenance (PdM) is a maintenance policy that uses the past, current, and prognosticated health condition of an asset to predict when timely maintenance should occur.
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Frans RodenburgWiskunde en Natuurwetenschappen
f.j.rodenburg@biology.leidenuniv.nl | +31 71 527 2727
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Robust rules for prediction and description.
In this work, we attempt to answer the question:
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Wouter van LoonFaculteit der Sociale Wetenschappen
w.s.van.loon@fsw.leidenuniv.nl | +31 71 527 2727
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Chen LiWiskunde en Natuurwetenschappen
c.li@cml.leidenuniv.nl | +31 71 527 2727
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Kunstgebitten, machines en stof
Over onorthodoxe uitingen van wetenschap.
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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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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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Learning-based Representations of High-dimensional CAE Models for Automotive Design Optimization
In design optimization problems, engineers typically handcraft design representations based on personal expertise, which leaves a fingerprint of the user experience in the optimization data. Thus, learning this notion of experience as transferrable design features has potential to improve the performance…
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2 PhD Candidates, Reinforcement Learning for Sustainable Energy
Wiskunde en Natuurwetenschappen, Leiden Institute of Advanced Computer Science (LIACS)
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Using cryo-EM methods to uncover structure and function of bacteriophages
Bacteriophages, or phages for short, are the most abundant biological entity in nature. They shape bacterial communities and are a major driving force in bacterial evolution.
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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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Calculated Moves: Generating Air Combat Behaviour
By training with virtual opponents known as computer generated forces (CGFs), trainee fighter pilots can build the experience necessary for air combat operations, at a fraction of the cost of training with real aircraft.
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Filter-based reconstruction methods for tomography
Promotor: K.J. Batenburg
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Automated de novo metabolite identification with mass spectrometry and cheminformatics
Promotor: T. Hankemeier, Co-Promotores: T. Reijmers, L. Coulier
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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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Applied Machine Learning in Neurosurgical Oncology
Promotie
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Novel analytical approaches to characterize particles in biopharmaceuticals
Particles are omnipresent in biopharmaceutical products. In protein-based therapeutics such particles are generally associated with impurities, either derived from the drug product itself (e.g. protein aggregates), or from extrinsic contaminations (e.g. cellulose fibers).
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The holographic glass bead game: from superconductivity to time machines
Promotores: Prof.dr. J. Zaanen, Prof.dr. K.E. Schalm
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Suzan Verberne in de Volkskrant over nieuwe vertaaltechnologieën
Een intelligente vertaalmachine gebaseerd op nieuwe ontwikkelingen in machine learning gaat de concurrentie aan met grote vertaalservices als die van Google. Maar ook Google maakt grote vorderingen sinds het is het is overgestapt op deep learning, aldus informatica-onderzoeker Suzan Verberne in de V…
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Philipp KropfWiskunde en Natuurwetenschappen
p.kropf@cml.leidenuniv.nl | +31 71 527 2727
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I-Fan LinWiskunde en Natuurwetenschappen
i.lin@liacs.leidenuniv.nl | +31 71 527 2727