Searched for: contributor%3A%22Loog%2C+M.+%28mentor%29%22
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Huang, Wenxuan (author)
Supervised machine learning is a growing assistive framework for professional decision-making. Yet bias that causes unfair discrimination has already been presented in the datasets. This research proposes a method to reduce model unfairness during the machine learning training process without altering the sample value or the prediction value....
master thesis 2022
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Biesheuvel, Julian (author)
Yes, convolutional neural networks are domain-invariant, albeit to some limited extent. We explored the performance impact of domain shift for convolutional neural networks. We did this by designing new synthetic tasks, for which the network’s task was to map images to their mean, median, standard deviation, and variance pixel intensities. We...
bachelor thesis 2021
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Lamon, Julien (author)
With an expectation of 8.3 trillion photos stored in 2021 [1], convolutional neural networks (CNN) are beginning to be preeminent in the field of image recognition. However, with this deep neural network (DNN) still being seen as a black box, it is hard to fully employ its capabilities. A need to tune hyperparameters is required to have a robust...
bachelor thesis 2021
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Thakoersingh, Ratish (author)
This research provides an overview on how training Convolutional Neural Networks (CNNs) on imbalanced datasets affect the performance of the CNNs. Datasets could be imbalanced as a result of several reasons. There are for example naturally less samples of rare diseases. Since the network is trained less on those instances, this might lead to...
bachelor thesis 2021
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den Heijer, Remco (author)
Does a convolutional neural network (CNN) always have to be deep to learn a task? This is an important question as deeper networks are generally harder to train. We trained shallow and deep CNNs and evaluated their performance on simple regression tasks, such as computing the mean pixel value of an image. For these simple tasks we show that...
bachelor thesis 2021
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Bons, Wouther (author)
Currently, trained machine learning models are readily available, but their training data might not be (for example due to privacy reasons). This thesis investigates how pre-trained models can be combined for performance on all their source domains, without access to data. This problem is formulated as a Multiple-Source Domain Adaptation (MSA)...
master thesis 2021
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Li, Mengze (author)
Active learning has the potential to reduce labeling costs in terms of time and money. In practical use, active learning works as an efficient data labeling strategy. Another point of view to look at active learning is to consider active learning as a learning problem, where the training data is queried by the active learner. Under this...
master thesis 2020
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Haas, Joey (author), Klazinga, Rembrandt (author), van Stijn, Nick (author), Teunissen, Jasper (author), Zhang, Peter (author)
The core challenge of the BedBasedEcho BEP project is to create an algorithm to find the heart, and apply it on a robotic echocardiography solution. The team has found multiple complex solutions that are related to this problem, and has extracted useful information from these solutions to apply to this problem. However, some of these complex...
bachelor thesis 2020
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Esseveld, J.R. (author)
Records from ledgers of Dutch companies all across the Netherlands are used in this study. Records can be submitted in the ledgers with various lags, because the data of many different bookkeepers is involved with different workflows. Bookkeepers can be punctual or late, therefore records can be submitted with various lags in the ledgers. This...
master thesis 2020
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van Tussenbroek, Thomas (author)
Authorship identification is often applied to large documents, but less so to short, everyday sentences. The ability of identifying who said a short line could provide help to chatbots or personal assistants. This research compares performance of TF-IDF and fastText when identifying authorship of short sentences, by applying these feature...
bachelor thesis 2020
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Keukeleire, Pia (author)
In recent years many new text generation models have been developed while evaluation of text generation remains a considerable challenge.  Currently, the only metric that is able to fully capture the quality of a generated text is human evaluation, which is expensive and time consuming. One of the most used intrinsic...
bachelor thesis 2020
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Happel, David (author)
Using transcripts of the TV-series FRIENDS, this paper explores the problem of predicting the location in which a sentence was said. The research focuses on using feature extraction on the sentences, and training a logistic regression model on those features. Specifically looking at the differences in performance between using ELMo and TF-IDF...
bachelor thesis 2020
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Schmahl, Katja (author)
Large text corpora used for creating word embeddings (vectors which represent word meanings) often contain a stereotypical gender bias. This unwanted bias is then also present in the word embeddings and in downstream applications in the field of natural language processing. To prevent and reduce this, more knowledge about the gender bias is...
bachelor thesis 2020
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Raijmakers, Thijs (author)
Word embeddings are useful for various applications, such as sentiment classification (Tang et al., 2014), word translation (Xing, Wang, Liu, & Lin, 2015) and résumé parsing (Nasser, Sreejith, & Irshad, 2018). Previous research has determined that word embeddings contain gender bias, which can be problematic in certain applications such...
bachelor thesis 2020
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Gabriel, Luka (author)
Both MRI and CT imaging are commonly used and combined in medical imaging because of their complementary information about soft tissue and bone respectively. However, CT imaging relies on harmful ionizing radiation. Thus medical imaging scientists are working on transferring harmless MRI scans to CT-like images where using deep learning methods...
master thesis 2018
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Razoux Schultz, Lex (author)
Automated Sentiment Classification (SC) on short text fragments has been an upcoming field of research. Different machine learning techniques and word representation models have proven to be successful in classifying sentiment of opinion expressions in various domains, i.e. different topics or source media. However, when training on a source...
master thesis 2018
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Ju, Jihong (author)
Training data for segmentation tasks are often available only on a small scale. Transferring learned representations from pre-trained classification models is therefore widely adopted by convolutional neural networks for semantic segmentation. In domains where the representations from the classification models are not directly applicable, we...
master thesis 2017
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Zhou, Yuan (author)
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master thesis 2017
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van Dorth, Matthijs (author)
master thesis 2017
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Boon, K. (author)
In this thesis I develop a Machine Learning method to combine galactic redshift estimates of previous authors into an aggregate estimate. I investigate weather combining earlier results into a single estimate is a worthwhile effort. Disagreement between the earlier results is used as a metric the quality of estimation. This disagreement is...
master thesis 2017
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