816 search results for “manycore machine” in the Public website
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Structured Parallel Programming for Monte Carlo Tree Search
The thesis is part of a bigger project, the HEPGAME (High Energy Physics Game). The main objective for HEPGAME is the utilization of AI solutions, particularly by using MCTS for simplification of HEP calculations.
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Massively collaborative machine learning
Promotor: J. N. Kok, Co-promotor: A. J. Knobbe
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Artificial Intelligence & Machine Learning
Computers are capable of making incredibly accurate predictions on the basis of machine learning. In other words, these computers can learn without intervention once they have been pre-programmed by humans. At LIACS, we explore and push the borders of what a revolutionary new generation of algorithms…
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Artificial intelligence and machine learning
Computers are capable of making incredibly accurate predictions on the basis of machine learning. In other words, these computers can learn without intervention once they have been pre-programmed by humans. At LIACS, we explore and push the borders of what a revolutionary new generation of algorithms…
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Predicting alcohol use disorder through machine learning
How to come to valid risk stratification of alcohol use disorder?
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PhD Candidate, Privacy-Preserving Machine Learning
Science, Leiden Institute of Advanced Computer Science (LIACS)
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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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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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Language as a time machine
By studying language you can reconstruct the history of different communities, even when no other historical sources, such as written documents, are available. In the coming years, researchers Willem Adelaar and Marian Klamer will be carrying out this kind of reconstruction in areas of great linguistic…
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Legibility in the Age of Signs and Machines
Legibility in the Age of Signs and Machines offers a compelling reflection on what the notion of legibility entails in a machinic world in which any form of cultural expression – from literary texts, films, artworks and museum exhibits to archives, laws, computer programs and algorithms – necessarily…
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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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Post-doctoral Data Scientist Fairness in machine learning for health
Science, Leiden Institute of Advanced Computer Science (LIACS)
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PhD Candidate, Interpretable causal machine learning for intervention development from wearable sensors data
Science, Leiden Institute of Advanced Computer Science (LIACS)
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On the Power Efficiency, Low latency, and Quality of Service in Network-on-Chip
In multi/many-core System-on-Chips (SoCs), the performance is almost linearly scaling with the number of processing elements.
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Coffee Machines & Personal Mugs
Have you always wanted to use your own coffee mug at the university coffee machines but it was never accepted? We have good news for you!
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Novel system-inspired model-based quantum machine learning algorithm for prediction and generation of High-Energy Physics data
Assistant Professor Vedran Dunjko and his team received a gift from Google to support their quantum research. The research focuses on whether quantum computers can provide new ways of understanding the mysteries of high-energy physics.
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Machine learning predicts preferences
Cláudio de Sá predicted the preferences of people using rankings. He adjusted ‘classical’ machine learning approaches, making them suitable for predicting preferences. His work can be applied in the prediction of election results. PhD defence on 16 December.
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PNAS Paper Prize for quantum machine learning
‘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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Language as a time machine
About 90 per cent of Austronesian and Papuan languages are under threat of soon becoming extinct. Marian Klamer is the only professor in the world who researches both these language groups. She records languages before they disappear and sheds new light on the history of Indonesia. Inaugural lecture…
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Understanding (the value of) machine translation
Leiden University Lecturer Lettie Dorst wins a prestigious Comenius Senior Fellow grant for a project about machine translation and its use in higher education.
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Frans Rodenburg
Science
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Wouter van Loon
Faculteit der Sociale Wetenschappen
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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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Self-learning machines for better understanding of the universe
Bright explosions appear all over the radio and gravitational-wave sky. This dynamic side of the universe which has just been discovered, can be mapped by self-learning machines. The National Science Agenda granted 5 million euro’s to CORTEX, the Center for Optimal, Real-Time Machine Studies of the…
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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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I-Fan Lin
Science
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Simon Marshall
Science
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Fatemeh Mehrafrooz Mayvan
Science
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Machine Learning Improves Cross-border Tax Estimates
Multidisciplinary research has established that VAT-results are in practice six times lower than what it should have been. The new estimates rely on machine learning techniques.
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MRI Machine at the Nanoscale Breaks World Records
A new NMR microscope gives researchers an improved instrument to study fundamental physical processes. It also offers new possibilities for medical science, for example to better study proteins in Alzheimer patients’ brains. Publication in Physical Review Applied.
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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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Robust rules for prediction and description.
In this work, we attempt to answer the question:
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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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A document classifier for medicinal chemistry publications trained on the ChEMBL corpus
Source: J Cheminform, Volume 6, Issue 1 (2014)
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special about human intelligence, that it cannot be replicated in a machine’
Is the possibility of computers making decisions for us in the future realistic? Holger Hoos, professor of Machine Learning, gives his opinion about the future of artificial intelligence in the television show ‘The future is fantastic’ on NPO3.
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Chen Li
Science
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Surendra Balraadjsing
Science
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Julia Wasala
Science
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Tom Kouwenhoven
Science
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Nurbolat Kenbayev
Science
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Gerard van Westen
Science
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Alex Brandsen
Faculteit Archeologie
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Alumnus Robert Ietswaart: ‘Machine learning is revolutionising drug discovery’
Robert Ietswaart does research into gene regulation at the famous Harvard Medical School in Boston. He developed an algorithm to better predict whether a candidate medicine is going to produce side effects. He studied mathematics and physics in Leiden, and gained his PhD in computational biology in…
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'The use of online translation machines in healthcare settings may involve certain risks'
Researcher and lecturer Susana Valdez investigates how migrants make use of online translation technology in medical situations. Her research suggests that they often encounter obstacles when using machine translation in these settings. Potential problems include a lack of understanding or trust.
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Leiden Classics: Bibliotheca Thysiana, a 17th century time machine
From once controversial scientific works and historical bibles, to personal shopping lists and clothing bills. The 17th-century Bibliotheca Thysiana and the archive of the collector Johannes Thysius exhibit both the intellectual and everyday life as it was three hundred years ago. Now a brand-new digital…
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Correspondence article by Eduard Fosch-Villaronga in Nature Machine Intelligence
Robot technology is flourishing in multiple sectors of society, from retail, health care, industry and education. However, are robots representative towards minority groups of society, like LGBTQ+ people?
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The Use of Machine Learning in Public Organizations - an Interview with PhD Student Friso Selten
Friso Selten recently started a PhD position that is part of the SAILS program. This PhD project is a collaboration between FGGA, LIACS, and eLaw, and is supervised by Bram Klievink (FGGA), Joost Broekens (LIACS), and Francien Deschene (eLaw). In the project Friso will investigate the influence of artificial…
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Ghost in the machine: the deep features of Yanming Guo
In the 1960s at MIT, cognitive scientist Marvin Minsky told a couple of graduate students to program a computer to perform the simple task of recognising objects in pictures, thinking it would be a nice summer project. Scientists from Leiden and the rest of the world are still working on it today.
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Unwinding a hank of yarn: how do cellular machines unfold misfolded proteins?
Protein chains typically fold to function. Folding is a complex process and if done correctly leads to a unique functional fold topology for a given protein chain. Other topologies are also possible but are often non-functional or toxic. These misfolded proteins are then unfolded and subsequently refolded…
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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.