769 zoekresultaten voor “machine archaeology” in de Publieke website
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Massively collaborative machine learning
Promotor: J. N. Kok, Co-promotor: A. J. Knobbe
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Applied Archaeology
De masterspecialisatie Applied Archaeology bereidt je op een wetenschappelijk niveau voor op een professionele carrière in de archeologie. Deze unieke specialisatie is een samenwerkinging tussen Universiteit Leiden en Hogeschool Saxion.
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World Archaeology
De onderzoekers van het departement ‘World Archaeology’ van de Faculteit Archeologie richten zich in hun onderzoek op verschillende periodes en regio’s: van de oorsprong van de mens tot aan de Middeleeuwen en de moderne tijd, en van Azië tot Zuid-Amerika.
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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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Global Archaeology (MA)
Als je het menselijk verleden bestudeert, kan dit je helpen om de maatschappelijke vraagstukken van tegenwoordig beter te begrijpen en op te lossen.
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Laboratoria Digital Archaeology
De onderzoeksgroep Digital Archaeology beheert twee computerlaboratoria voor verschillende doeleinden: een onderwijslab en een onderzoekslab.
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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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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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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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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-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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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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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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Automated Machine Learning for Neural Network Verification
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space 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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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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The holographic glass bead game: from superconductivity to time machines
Promotores: Prof.dr. J. Zaanen, Prof.dr. K.E. Schalm
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Kunstgebitten, machines en stof
Over onorthodoxe uitingen van wetenschap.
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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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Information-theoretic partition-based models for interpretable machine learning
In this dissertation, we study partition-based models that can be used both for interpretable predictive modeling and for understanding data via interpretable patterns.
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Mens in de machine (najaar 2022)
Eerlijke en creatieve AI
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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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The flux and flow of data: connecting large datasets with machine learning in a drug discovery envirionment
This thesis focuses on data found in the field of computational drug discovery. New insight can be obtained by applying machine learning in various ways and in a variety of domains. Two studies delved into the application of proteochemometrics (PCM), a machine learning technique that can be used to…
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Pharmacokinetics Nonlinear BBB Transport, Inter-species Scaling, and Machine Learning
This thesis focuses on enhancing predictions of central nervous system drug exposure using the LeiCNS-PK3.0, a physiologically based pharmacokinetic model.
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Intent on the Paleolithic: Papers in honour of Prof.dr. Wil Roebroeks
This collection of papers was compiled in celebration of the remarkable academic career of Professor Wil Roebroeks, who has established himself as one of Europe’s leading figures in Palaeolithic archaeology over the past three decades and founded the Human origins research group at Leiden University…
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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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The Little Green Machine
Onderzoekers uit de informatica, wiskunde, meteorologie, materiaalbouw natuur- en sterrenkunde hebben gezamenlijk een oplossing gevonden voor hun grote behoefte aan rekenvermogen. De diverse groep onderzoekers heeft voor de bouw van een revolutionaire supercomputer, die
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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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Theory of mind in language, minds, and machines: a multidisciplinary approach
Humans can see the world through the eyes of other humans and imagine what they know, want, and intend. This competence is known as Theory of Mind.
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How can humans and machines collaborate in a meaningful way in a restrictive environment?
In this project, researchers from computer science, law, psychology, and public administration research in practice how artificial intelligence (AI) can be leveraged to make decision-making in the security domain more effective, while also keeping it safe and accountable.
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Separating quantum and classical computing: rigorous proof and practical application
This thesis probes under what conditions quantum computing presents an advantage over classical computing.
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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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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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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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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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Eduardo Herrera MalatestaFaculteit der Archeologie
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Course: Introduction to the Archaeology of the Book
The Summer School History of the Book, organized by the Allard Pierson Museum, introduces a new course in English: Introduction to the Archaeology of the Book, taught by Prof. Malcolm Walsby (2-6 September 2024).
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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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Demo laat mogelijkheden van machine-leesbare wetgeving zien
Tijdens een ‘vrije sessie bijeenkomst’ van de afdeling Staats- en bestuursrecht op 27 februari presenteerde het RegelRecht-team van het ministerie van Binnenlandse Zaken en Koninkrijksrelaties een demo over machine-leesbare wetgeving.
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Demo laat mogelijkheden van machine-leesbare wetgeving zien
Tijdens een ‘vrije sessie bijeenkomst’ van de afdeling Staats- en bestuursrecht op 27 februari presenteerde het RegelRecht-team van het ministerie van Binnenlandse Zaken en Koninkrijksrelaties een demo over machine-leesbare wetgeving.
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Collaborative Meaning-Making
Humans share meaning through language. Over time, repeated interactions have shaped languages into forms that match our cognitive preferences, making them structured, expressive, easy to learn, and ultimately, meaningful.
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Rurale gemeenschappen in de civitas Cananefatium 50-300 na Christus
Deze dissertatie onderzoekt de rurale gemeenschappen van de Cananefaten in de periode van 50 tot 300 na Christus.
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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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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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Transkribus in het onderwijs: werken aan digitale geletterdheid met historische bronnen
In dit project bundelen onderzoekers en docenten van LUCAS, het ICLON en de UBL de krachten om expertise en ervaring op het gebied van machine learning en AI als open leermateriaal voor het middelbaar onderwijs te publiceren.
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EJLS symposium editorial : is fairness in digital governance a trap?
In dit artikel onderzoeken Barrie Sander en zijn collega's of eerlijkheid in digitaal bestuur onbedoeld structurele ongelijkheden verankert.
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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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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.