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Scholman, Renzo (author)
Even if a Multi-modal Multi-Objective Evolutionary Algorithm (MMOEA) is designed to find all locally optimal approximation sets of a Multi-modal Multi-objective Optimization Problem (MMOP), there is a risk that the found approximation sets are not smoothly navigable because the solutions belong to various niches, which reduces the insight for...
master thesis 2022
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Khan, Abdul Rehman (author)
Transformers have dominated the field of natural language processing due to their competency in learning complex relationships within a sequence. Reusing a pre-trained transformer for a downstream task is known as Trans-fer learning. Transfer learning restricts the transformer to a fixed vocabulary; modification in transformer implementation...
master thesis 2022
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Hoogland, Pavel (author)
In the use of Machine Learning systems, attaining the trust of those that are the end-users can often be difficult. Many of the current state-of-the-art systems operate as Black-Boxes. Errors produced by these Black-Box systems, without further explanation as to why these decisions were made, will deteriorate trust. This effect is especially...
master thesis 2022
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Foffano, Daniele (author)
Model-Based Reinforcement Learning (MBRL) algorithms solve sequential decision-making problems, usually formalised as Markov Decision Processes, using a model of the environment dynamics to compute the optimal policy. When dealing with complex environments, the environment dynamics are frequently approximated with function approximators (such as...
master thesis 2022
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Kanya Paramita Koesoemo, Kanya (author)
The development of data stream processing has become one of the key themes in the database and distributed system community throughout the world as data has grown on a large scale and in a range of industries over the last several years. Because data stream processing is a relatively new breakthrough in data-driven approaches, several teams at...
master thesis 2021
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Smit, Jordi (author)
Offline reinforcement learning, or learning from a fixed data set, is an attractive alternative to online reinforcement learning. Offline reinforcement learning promises to address the cost and safety implications of taking numerous random or bad actions online, which is a crucial aspect of traditional reinforcement learning that makes it...
master thesis 2021
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Marchal, Maxim (author)
Clustering is an important unsupervised learning task, with many applications in machine learning, computer vision, formal program verification and finance. Heuristic approaches such as local search are an excellent strategy for estimating the optimal solution, but they run the danger of getting stuck in local optima. Furthermore, it is non...
master thesis 2021
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Singh, Akash (author)
Single-cell multi-modal omics promises to open new doors in bioinformatics by measuring different aspects of cells, thus offering multiple perspectives on the underlying biological phenomenon. Although simultaneous multi-modal measurement protocols do exist, their inherent technical limitations necessitate focus on single modality measurements....
master thesis 2021
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Saravanan, Nash (author)
In this work, a conversational interaction was designed and implemented to test the effect of references to past events or shared experiences rephrased into motivational phrases within the context of working towards a diet related goal that can assist with type II diabetes over multiple sessions.<br/>Prior works that utilized a memory typically...
master thesis 2021
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Saldanha, Nikhil (author)
A structured CNN filter basis allows incorporating priors about natural image statistics and thus require less training examples to learn, saving valuable annotation time. Here, we build on the Gaussian derivative CNN filter basis that learn both the orientation and scale of the filters. However, this Gaussian filter basis definition depends on...
master thesis 2021
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van den Berg, Michiel (author)
This work analyses context effect in the evaluation of music similarity performed by human annotators to better understand the impact of context effects in the current annotation protocol of Music Information Retrieval Evaluation eXchange (MIREX). Human annotators are known to be subjective when giving similarity judgements. The Audio Music...
master thesis 2021
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Veldhuis, Marthe (author)
Using machine learning to determine the number of contributors (NOC) in short tandem repeat (STR) mixture DNA profiles has been shown to obtain good accuracy. However, the models used so far are not transparent to users as they only output a prediction without any reasoning for that conclusion. Therefore, we leverage techniques from the field of...
master thesis 2021
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Breurkes, Arthur (author)
Database entity type recognition is the practice of recognizing conceptual entity types for which given data sets contain data. In big data or data lake settings, it is not always known which conceptual entity types are represented in each data set, making it difficult to extract value from the data. Depending on the logical schemas, each...
master thesis 2021
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Zirkzee, Wouter (author)
Due to the rapid growth of diseases like diabetes, machine-learning (ML) advanced clinical decision support systems (CDSS) that support doctors or patients in their care might prove to be a valuable asset. In the battle to make healthcare more accessible, adopting ML techniques could be supportive in increasing the overall<br/>effectiveness and...
master thesis 2021
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Bes, Ruben (author)
Many types of database management systems exist, but finding the one that is right for a specific use case is becoming increasingly more difficult. Benchmarks allow one to compare various systems, but in a world where distributed DBMSs are increasingly used for mission critical purposes, we find most existing benchmarks neglect fault tolerance...
master thesis 2021
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Bakker, Paul (author)
For real-world problems even the most complex machine learning models can only achieve a certain accuracy. This makes it important to understand why a specific prediction is made. Explanations can provide human decision support by allowing human experts to assess the reasoning of the model as well as the correctness. Specifically, in this thesis...
master thesis 2020
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Ómarsdóttir, Þórunn (author)
In this thesis, we study automatically generating explanatory reports for anomalous incidents in a train control system (TCS) using Natural Language Generation (NLG). A TCS is a type of safety-critical software that allows train controllers to correctly set the tracks for a train to pass. The goal of this research is to process the majority of...
master thesis 2020
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Seale, Colm (author)
Synthetic lethality (SL) arises between two genes when loss of function of both genes would lead cells to become inviable. This can be exploited for therapy, where a drug is used to selectively kill diseased cells by perturbing one gene of an SL pair where the other gene is inactive (e.g. through naturally occurring mutation). Computational...
master thesis 2020
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Pocchiari, M. (author)
Recommender Systems assist the user by suggesting items to be consumed based on the user's history. The topic of diversity in recommendation gained momentum in recent years as additional criterion besides recommendation accuracy, to improve user satisfaction. Accuracy and diversity in recommender systems coexist in a delicate trade-off due to...
master thesis 2020
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Gong, B. (author)
With advancements in Internet and technology, it has become increasingly easy for people to enjoy music. Users are able to access millions of songs through music streaming services like Spotify, Pandora, and Deezer. Access to such large catalogs created a need for relevant song recommendations. Music recommender systems assist users in finding...
master thesis 2020
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