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103 search results for “machine learning” in the Public website

• Massively collaborative machine learning Research output

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

• Computational speedups and learning separations in quantum machine learning Research output

  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…

• Reliable and Fair Machine Learning for Risk Assessment Research output

  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.

• Machine learning and computer vision for urban drainage inspections Research output

  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.

• Automated machine learning for dynamic energy management using time-series data Research output

  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…

• of post-translationally modified peptides in Streptomyces with machine learning Research output

  The ongoing increase in antimicrobial resistance combined with the low discovery of novel antibiotics is a serious threat to our health care.

• The flux and flow of data: connecting large datasets with machine learning in a drug discovery envirionment Research output

  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…

• Machine learning-based NO2 estimation from seagoing ships using TROPOMI/S5P satellite data Research output

  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.

• PNAS Paper Prize for quantum machine learning News - 14 March 2019

  ‘We hope our paper highlights the possibilities and benefits of including artificial intelligence in quantum physics to do new discoveries.’ Vedran Dunjko of the Leiden Institute of Advanced Computer Science contributed to a paper that was published in PNAS last year and now received a Cozzarelli Prize…

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  Frans Rodenburg Lecturer

  Science

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  Wouter van Loon Guest

  Faculteit der Sociale Wetenschappen

• Optimally weighted ensembles of surrogate models for sequential parameter optimization Research output

  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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  Simon Marshall PhD candidate

  Science

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  I-Fan Lin PhD candidate

  Science

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  Philipp Kropf PhD candidate

  Science

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  Anna Dawid-Lekowska Assistant professor

  Science

• Modelling the interactions of advanced micro- and nanoparticles with novel entities Research output

  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.

• Data-driven donation strategies: understanding and predicting blood donor deferral Research output

  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.

• Exploring big data approaches in the context of early stage clinical Research output

  Als gevolg van de grote technologische vooruitgang in de gezondheidszorg worden in toenemende mate gegevens verzameld tijdens de uitvoering van klinische onderzoeken.

• Network analysis methods for smart inspection in the transport domain Research output

  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.

• Algorithm selection and configuration for Noisy Intermediate Scale Quantum methods for industrial applications Research output

  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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  Michael Lew Professor Deep Learning

  Science

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  Tom Kouwenhoven PhD candidate

  Science

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  Surendra Balraadjsing PhD candidate

  Science

• Numerical exploration of statistical physics Research output

  In this thesis, we examine various systems through the lens of several numerical methods.

• Data-Driven Risk Assessment in Infrastructure Networks Research project

  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'.

• A document classifier for medicinal chemistry publications trained on the ChEMBL corpus Research output

  Source: J Cheminform, Volume 6, Issue 1 (2014)

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  Gerard van Westen Professor Artificial Intelligence & Medicinal Chemistry

  Science

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  Diego Barbosa Arize Santos Ict developer

  Faculteit der Sociale Wetenschappen

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  Guilherme D'Andrea Curra PhD candidate / self funded

  Faculteit Archeologie

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  Rayyan Toutounji Researcher

  Faculteit der Sociale Wetenschappen

• Model-assisted robust optimization for continuous black-box problems Research output

  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.

• Spectral imaging and tomographic reconstruction methods for industrial applications Research output

  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.

• Methods and Tools for Mining Multivariate Time Series Research output

  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.

• Robust rules for prediction and description. Research output

  In this work, we attempt to answer the question:

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  Fons Verbeek Professor of Computational Bio-imaging

  Science

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  Chen Li Postdoc

  Science

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  Matthijs van Leeuwen Associate professor/Director of Education

  Science

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  Evert van Nieuwenburg Assistant Professor

  Science

• Using cryo-EM methods to uncover structure and function of bacteriophages Research output

  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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  Alex Brandsen Postdoc

  Faculteit Archeologie

• Automated de novo metabolite identification with mass spectrometry and cheminformatics Research output

  Promotor: Prof.dr. T. Hankemeier, Co-Promotores: T. Reijmers, L. Coulier

• Filter-based reconstruction methods for tomography Research output

  Promotor: K.J. Batenburg

• Novel analytical approaches to characterize particles in biopharmaceuticals Research output

  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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  Aske Plaat Professor of Data science

  Science

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  Elise Dusseldorp Professor Methodology and Statistics in Psychology

  Faculteit der Sociale Wetenschappen

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  Marjolein Fokkema Associate professor

  Faculteit der Sociale Wetenschappen

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  Alina Karakanta Assistant professor

  Faculty of Humanities

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  Felix Frohnert PhD candidate

  Science

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  Hongchang Shan PhD candidate / guest

  Science